PsiQuantum’s $1B Series E, Nvidia, & the Race to 1 Million Qubits

Pete Shadbolt, Chief Scientific Officer & Co-Founder of PsiQuantum, unpacks the company’s landmark $1 billion Series E, bringing total funding to nearly $2 billion & valuing the company at $7 billion. Backed by BlackRock, Temasek, Baillie Gifford, and Nvidia’s venture arm, PsiQuantum is doubling down on its radical bet: you need 1 million qubits to unlock useful quantum computing. Shadbolt explains why PsiQuantum rejected incrementalism, how the partnership with Nvidia and Jensen Huang strengthens the roadmap, and why quantum is positioned as the next wave of compute alongside AI and semiconductors. From photonic chip breakthroughs to government partnerships in Australia and Illinois, this is a rare inside look at the company leading the race to build the world’s first fault-tolerant quantum computers. Previous lead investors include: Playground Global and Redpoint led the Series A in 2016, Founders Fund led the Series B in 2017, Atomico and M12 led the Series C in 2019, and BlackRock led the Series D in 2021. 5 Key Takeaways • PsiQuantum closed a $1B Series E, bringing total funding to nearly $2B • Investors include BlackRock, Temasek, Baillie Gifford, Macquarie, Nvidia (NVentures), Ribbit Capital, QIA,1789 Capital & more • PsiQuantum is committed to an “N of 1 Million Qubits” strategy—skipping demos to build at scale • Nvidia and Jensen Huang are strategic partners, collaborating on GPU-QPU integration, algorithms, and photonics • PsiQuantum’s valuation hit $7B, making it the most well-funded quantum company in the world 1. Pete Shadbolt: https://www.linkedin.com/in/pete-shadbolt-4b7541126/ 2. Molly O’Shea: ⁠https://x.com/MollySOShea⁠ 3. Sourcery: ⁠https://x.com/sourceryvc⁠ Brought to you by: • Brex—The modern finance platform, combining the world’s smartest corporate card with integrated expense management, banking, bill pay, & travel. As a Sourcery Listener you get: 75,000 points after spending $3,000 on Brex card(s), white-glove onboarding, $5,000 in AWS credits, $2,500 in OpenAI credits, & access to $180k+ in SaaS discounts. On top of $500 toward Brex travel, $300 in cashback, plus exclusive perks (like billboards..) visit → https://brex.com/sourcery • Turing—Turing delivers top-tier talent, data, and tools to help AI labs improve model performance—and enables enterprises to turn those models into powerful, production-ready systems. Visit: https://turing.com/sourcery • Kalshi—The largest prediction market and the only legal platform in the US where people can trade directly on the outcomes of future events: https://kalshi.com/sourcery Follow Sourcery for the latest updates! https://www.sourcery.vc/ (00:00:00) Intro and Recent Funding (00:02:29) Building PsiQuantum: From UK to Silicon Valley (00:03:55) Investment Strategy and Deep Tech Focus (00:05:35) PsiQuantum's Differentiation: Going Big from Day One (00:07:03) Rejecting Incrementalism for Large-Scale Vision (00:09:13) The Rocket Engine Analogy (00:10:59) Timeline Debates and NVIDIA Partnership (00:15:36) Global Manufacturing and Facilities (00:20:21) Path to Becoming a Trillion Dollar Company (00:23:32) Revenue Model and Commercialization Strategy (00:27:53) Working with Global Foundries (00:31:27) Quantum Computing's Role in Semiconductor Industry (00:38:28) Public Trust and Government Validation (00:45:49) The Reality of Quantum Computing Valuations (00:53:12) Zero to One Technology Approach (01:00:19) Global Facilities and Infrastructure (01:09:03) Reindustrialization and Strategic Importance (01:34:02) Personal Journey and Vision (01:44:23) Timeline for Useful Quantum Computing (01:53:54) Looking Ahead: Next Steps and Future Vision

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

• 0:00Molly O'Shea?

  This brings your total funding closer to $2 billion. How are you going to become a trillion dollar company?

• 0:06Speaker 01

  Jensen says you need a million qubits. Satya knows you need big systems, et cetera, et cetera. I really worry that leadership at Google or Nvidia or whatever is completely misled about the impact of the technology and when it's going to come. Google has recently demonstrated really amazing systems of about 100 qubits. The general consensus is that you need more like a million qubits to do anything commercially impactful with a quantum computer.

• 0:30Molly O'Shea?

  So you've raised billions of dollars, but you don't yet have a product in the market?

• 0:34Speaker 01

  Just look at the most consequential, durably valuable, strategically important, geopolitically significant companies of our age. TSMC, ASML, SpaceX, Nvidia, OpenAI. These companies are living or dying on the quality of their science. And they're all way out on the frontier. Boiling sulfuric acid, bleeding-edge rocket engines, nanometer-scale fabrication, 100 gigahertz networking, and very, very demanding, bleeding-edge science and engineering that is defining the frontier. Power is captured on the frontier.

• 1:09Molly O'Shea?

  There's one question you absolutely hate, but I'll save it for the end.

• 1:22Molly O'Shea?

  Pete, welcome to Sorcery.

• 1:24Speaker 01

  Thanks for having us. Appreciate it.

• 1:26Molly O'Shea?

  Who's us?

• 1:27Speaker 01

  That is a British term of phrase people refer to themselves in the plural from time to time. Thanks for having me. It's really nice to be here.

• 1:37Molly O'Shea?

  Always a pleasure. Happy to. So we have a lot of ground to cover today. We're going to go deep into quantum.

• 1:43Speaker 01

  Yep.

• 1:44Molly O'Shea?

  You have a big announcement we'll get into. There's one question you absolutely hate, and I'll be sure to ask it.

• 1:50Speaker 01

  Brilliant.

• 1:51Molly O'Shea?

  I'm going to ask it at the end, though.

• 1:52Speaker 01

  I wouldn't expect anything less.

• 1:54Molly O'Shea?

  I know. It's like you tell me not to do something, we're going to do it. But I'll save it for the end, so everybody can stick around.

• 2:00Speaker 01

  Brilliant.

• 2:01Molly O'Shea?

  And then before we start, I have to say a thank you to Dr. Priy Narang from UCLA. She is one of the researchers there for physical sciences, and she's also an operating partner at DCBC. So thank you for the research help. Really appreciate it and very much needed it. So Pete. First.

• 2:23Speaker 01

  Mm hmm.

• 2:23Molly O'Shea?

  Huge congratulations.

• 2:25Speaker 01

  Thank you.

• 2:26Molly O'Shea?

  How did you raise a billion dollars?

• 2:29Speaker 01

  It's a great question. So I mean, I came to Silicon Valley about ten years ago now, moved here from the UK.

• 2:38Speaker 01

  With the idea that we could build what is widely viewed as one of the most consequential technologies humans have ever come up with. And I half expected to be back on the plane, heading back to the UK six months later. And it's astonishing to me that we're still here and still kicking and that we're so incredibly well resourced and I'm very grateful for that. We raised our series D about four years ago now. We've spent a lot of that money to establish the manufacturing process for the systems that we're building. And then last year, we announced these major deals with the Australian government. So that's about a billion dollar Australian deal for us to build

• 3:20Speaker 01

  a very large scale quantum computer in Brisbane. And then also that we would be the anchor tenant of a new five hundred million dollar quantum computing campus that's being built in Chicago. And so, you know, on the back of that progress and the the maturity that we've been able to achieve with the technology, which is really excited now to be closing this round and to be heading towards actually building these really large systems that we need to unlock commercial applications of quantum computing.

• 3:51Molly O'Shea?

  With this round, what was the process like and who are the investors involved?

• 3:55Speaker 01

  We're obviously firmly in the deep tech regime as a company. We've been around for a decade, so I've

• 4:06Speaker 01

  learnt a lot about fundraising and so on in the valley over the last ten years or so. I don't think it was a pretty particularly unusual fundraise, really. We've had so BlackRock, Beatty Gifford, Tamasec are all involved in the rounds. And then we have a bunch of new investors also coming into the rounds. But a lot of familiar names from Silicon Valley and then also some maybe slightly surprising late stage crossover type money that's coming into this this funding round. I think it's nice to be doing it in a context where, you know, a decade ago. When I said the words high performance computing or supercomputer or talked about a building sized machine

• 4:49Speaker 01

  ten years ago, when people were doing apps, like that was a deeply unpopular thing to talk about. HPC is like from the eighties, like, don't talk to me about supercomputers. But of course, now we're doing that in a context where building a megawatt supercomputer is like child's play and people are much more excited about hundreds of megawatts and gigawatts and whatever. So to be doing that, you know, surrounded by the current excitement and also the kind of understanding and comprehension of these big AI systems has been pretty helpful to our to our process. Yeah.

• 5:21Molly O'Shea?

  This brings your total funding closer to two billion dollars.

• 5:25Speaker 01

  In private capital, yeah.

• 5:26Molly O'Shea?

  In private capital, and I believe this is more than any other quantum computing company. So.

• 5:31Speaker 01

  Right to my knowledge, yeah.

• 5:32Molly O'Shea?

  What makes SciQuantum different?

• 5:35Speaker 01

  Yeah, it's a great question and sort of my job to explain this and I endlessly iterate on on how I explain the company. I think you know, it is striking. It's astonishing to me that, you know, we have this support and and that we've raised this money.

• 5:51Speaker 01

  I think as far as like how SciQuantum is differentiated, it's helpful to understand that we sit on the far end of the spectrum in terms of targeting a really big machine.

• 6:03Speaker 01

  So quantum computing always is coming out of university research groups. In order to build a quantum computer, we need to use new physics. And that always starts with a research lab and demo systems and toy machines. And there have been quantum processors on the internet for more than a decade. I put one of the first systems online in the UK more than a decade ago, and that's two qubits. And, you know, Google has recently demonstrated really amazing, beautiful systems of about a hundred qubits. The general consensus is that you need more like a million qubits to do anything commercially impactful with a quantum computer. And SciQuantum is differentiated primarily

• 6:48Speaker 01

  in that we really took that to heart when we started the company. When you're a physicist in a lab and you think that you've come up with the way to build a quantum computer. It's very tempting to take your lab experiment and scale it up a bit. And try and do some demos and make some press releases and publish some more papers. Maybe raise a bit of venture capital. And and sort of build an incremental path to what you know has to be a really big system. We just completely rejected that. So we just went fully a hundred percent committed to realizing a very large machine. And so every investor that we've spoken to, every government that we've spoken to. The deal has basically been that if you give us money.

• 7:33Speaker 01

  We will spend that money exclusively on the technologies that are needed. To get to a very, very large system that is actually commercially useful. And that's a provocative positioning. It's like an unpopular positioning with a whole.

• 7:50Speaker 01

  Group of very smart people, but there's obviously been enough people who understand. Why that makes sense and have been compelled by the progress that we've made and the technology that we've been able to show. That they want to support that approach and that sort of philosophy. And yeah, I'm very, very glad that we've taken that approach. Ten years ago, there was sort of defensible optimism. That small systems might be useful and we took a risk in not pursuing. Like a hundred qubits or five hundred qubits or something. We just went all in on a giant system. You know, five, ten years later, that feels like a really good bet. And I think a helpful analogy is actually with.

• 8:33Speaker 01

  You know, with with flight or getting off of the ground. So you want to get off the ground. Building ladders is a natural way to do it and you can build a ten foot ladder and your twenty foot ladder and so on. And then somebody comes and taps you on the shoulder and says, you've got to get to the moon. And now ladders don't really look like the right way to do it. And and so what we've been doing is investing in a rocket engine. And to build a new rocket engine, you've got to spend hundreds of millions of dollars. And by the way, your rocket engine doesn't even fly, it doesn't get off of the ground. You do tests that are, you know, stuck to the earth. But that's a good use of capital if the goal is ultimately to get to the moon. And and so that's that's kind of how we've operated.

• 9:16Speaker 01

  We've invested in the semiconductor manufacturing, in the networking, in the cooling, in all of the technology that we need to deliver a million qubit machine. And we haven't spent any of our resources on like taking a twenty qubit processor, sticking it online, letting people play around with that. We decided not to do that.

• 9:37Molly O'Shea?

  So instead of incrementalism.

• 9:39Speaker 01

  Yeah.

• 9:39Molly O'Shea?

  We're going straight to being an N of one million qubits.

• 9:43Speaker 01

  Yes, yes. And to be clear, we have intermediate milestones scattered along the the roadmap to that big system. And to me, it looks a lot more like how you build a conventional, like, leadership-class supercomputer. So, you know, we count qubits, right? And it's sort of natural to think about building a five qubit system and then a ten qubit system and then a fifty qubit system. But if you follow that path to a million qubits, that's like a multi-decade trajectory that looks very unattractive. And that's not how people build leadership-class supercomputers. They don't build a five transistor and then a twenty transistor and so on, right? What they do is they establish the manufacturing process for the chip.

• 10:28Speaker 01

  They go and figure out the networking, they go and develop the cooling. Eventually, they put one rack in a building somewhere, bring it up, debug it. Then they go and put ten racks together, gives them all sorts of headaches, they debug that system. And eventually, they go and put a hundred-plus racks into a. Building and that's as much as possible. That's really how we're approaching the roadmap and the staging. And we're clear that none of that is useful until you get to that final very large scale machine.

• 10:59Molly O'Shea?

  I think like many people over and over again will be saying, oh, it's five years away. It's ten years away, et cetera, et cetera. Jensen Huang even was quoted saying he thinks quantum will be fifteen to thirty years out.

• 11:12Speaker 01

  Mm hmm.

• 11:12Molly O'Shea?

  Not long after he actually backtracked on that when he realized that there are quantum public companies.

• 11:18Speaker 01

  Yep.

• 11:19Molly O'Shea?

  And now he's getting involved. In particular, he's mentioned in this round. So how did he become strategic to SciQuantum? And how are you working together?

• 11:29Speaker 01

  Yeah, so so quantum computing is definitely in that list of technologies where it's like very understandable that people say, oh, it's always five years away. It's science fiction, whatever. And if you look at what Jensen said, he said it, he was asked about this kind of off the cuff, unexpected. And I think he, you know, threw back an answer that to me is a pretty reasonable answer. And I think he's probably right on average on that timeline. If you look at exactly what he said in that call, it's pretty helpful. Firstly, he starts by saying you need a million qubits. So he starts by saying that, this is of course left out of the quotes in in media. But first of all, I'm grateful for that. Like, quantum

• 12:12Speaker 01

  computing is a space with a lot of hype, a lot of confusion, a ton of noise. I worry that because of all of that hype and because it's a pretty involved, challenging field to understand, I really worry that like leadership at Google or Nvidia or whatever is completely misled about the impact of the technology and when it's going to come and all this kind of stuff. Pretty encouraging to see like Jensen says, you need a million qubits, you know, Satya knows you need big systems, et cetera, et cetera. And then he says, so Google's got a hundred qubits, you need about a million qubits. That's ten thousand x scale up.

• 12:53Speaker 01

  And then from that, he sort of leaps to that's a multi decade, it sounds like a multi decade exercise for humans to scale a technology by ten thousand X, uh, which is a sort of rational way of thinking. And I was very grateful to have the opportunity to put the counterpoint to him at G T C uh, earlier this year, um, which is that he himself was proven how you scale by ten thousand X in a hundred and twelve days. So the X A I Colossus supercomputer Elon Musk and team famously built a one hundred thousand GPU cluster in a hundred and twelve days. Which is a miracle, right? Like a hundred thousand GPUs

• 13:35Speaker 01

  in a hundred days. It's astonishing that we can do this. How can we do that? It's thanks to the trillion dollars and fifty years that our species is put into the semiconductor industry. And the fact that we can make tens of thousands of wafers, billions of devices, that there's this enormous um, uh, supply chain to build these parts in, you know, and it and it's here where we're sitting in playgrounds, um, early on, I was really lucky to see people do this with cell phones. You know, people look at us and they say, you try trying to build a million qubits. This is like fantasy land. You can't scale like that. These guys here,

• 14:17Speaker 01

  when I first showed up, they had a cellphone this big. They had like a big uh PCB covered in parts with like a screen hanging off the front. The next year, they go to T S M C, Foxconn, you know, Sanmina, Kyocera, et cetera, all of the usual suspects of the semiconductor industry supply chain. And they place an order for a hundred thousand cell phones. And you'd be shocked if those cell phones don't show up working the next year, right? Like you can do that kind of scaling so long as you can fit yourself into the constraints uh, of the semiconductor industry. And that was really our founding thesis. The only reason that we could afford to make that really extreme bet that we're going to go straight to a million qubits. The only reason that we felt that was plausible

• 15:01Speaker 01

  is that our technology fits into the fabs and the OSATs and the contract manufacturers.

• 15:08Speaker 01

  Who are building millions of devices for the semiconductor industry every year. And so that gave us kind of the confidence and the leverage to believe in that trajectory. And yeah, hopefully that registered with Jensen, given, you know, his experience of that same same industry.

• 15:27Molly O'Shea?

  How is Nvidia getting involved?

• 15:29Speaker 01

  Yeah, so we are actually working with Nvidia on a whole bunch of different tracks. They have a quantum computing effort, they have CUDA Q and a whole bunch of really nice software. They're also working on some of the hardware pieces, not the qubits themselves. And Nvidia have been pretty explicit that they're not building a quantum computer. But they do see a whole bunch of opportunities to contribute to the ecosystem. And then they're also having a look at some of the byproducts, basically, of our development. We make some of the best photonics in the world, and that's pretty relevant for networking and AI supercomputers.

• 16:09Speaker 01

  So up until recently, people have leaned heavily on copper wires to network these AI supercomputers. That's becoming untenable as we get to these really huge systems. And so optics is increasingly playing a role. And there's a few people actually who are taking a look at the photonics that we've developed that is beyond state of the art in in performance. But yeah, we're just really, really excited about the engagement with Nvidia. They've obviously decided to take a leadership position in this space. They're deadly serious about it, and I think they're taking a sort of pretty measured approach to the whole thing. So obviously legendary company.

• 16:51Speaker 01

  I think it's kind of interesting that like Jensen's trajectory is the opposite of ours in that they really grew that over decades from something very small incrementally, revenue generating, et cetera, et cetera. It's obviously the scale that they are now. It's kind of fun to reflect on the fact that they used to sell. And this isn't meant to be derogatory, it's just meant to make a point like they used to be a toy company, right? Like they used to sell game, like gaming hardware. And then they transitioned to selling H one hundreds by the bucket loads. And now they are a supercomputer company. Like if you had told people this, I think 10 years ago, a lot of VCs,

• 17:32Speaker 01

  they wouldn't have believed you that the world's biggest company would be a uh scientific computing company that sells supercomputers as their main product. I'm only stretching the definition a little bit when I say that, right? Like the majority of Nvidia's revenue is now for giant supercomputers that are mostly operated by scientists. And that's, you know, multi-trillion dollar company.

• 18:01Speaker 01

  And then it's also like inspirational to me. When I hear inevitably these kind of comments, quantum computing is 10 years away. It's some gobbledygook science fiction stuff. Just to think about the trajectory that AI has been on, right? Like AI was essentially a failed field multiple times in its trajectory. It went through multiple winters. DARPA defunded it, UK government defunded it, and it had the same reputation. A bunch of scientists, I can't understand what they're saying. And they keep failing to deliver on their promises. And it was really the convergence of two things to my mind. The availability of hardware that was built for another purpose, in this case gaming GPUs.

• 18:45Speaker 01

  And then also progress on the architecture where I see very strong parallels between what we've done and what has happened with, you know, transformers and attention and so on. Those two things coming together can really sneak up on you. And I think that's what happened with AI. And I obviously am biased, but I see very strong parallels with with quantum computing.

• 19:06Molly O'Shea?

  Sorcery is brought to you by Brex, the financial stack trusted by more than 30,000 companies, including one in three venture-backed startups in the US. Nearly 40% of startups fail because they run out of cash. Rex is literally built to help founders avoid that. Like traditional banks that let your money sit idle, chipping away at it with fees. Brex's designs help you spend smarter and move faster. Their all in one solution combines checking, treasury and FDIC protection into one powerful account. You can send and receive money globally at lightning speeds. Get 20 times the standard FDIC coverage through their partner banks and even high yield from day one. With same day and even same hour liquidity, access your funds anytime.

• 19:51Molly O'Shea?

  Companies like Skela AI, DoorDash, Service Titan, Pims, Anthropic, Flexport, Robinhood and Plaid. Trust and use Brex. Start today at brex.com/sorcery. That's B R E X dot com slash sorcery. Okay, so I have a really hard question for you.

• 20:09Speaker 01

  Please.

• 20:10Molly O'Shea?

  Are you ready for this?

• 20:11Speaker 01

  Yeah.

• 20:12Molly O'Shea?

  I figured, why save all the hard questions for the end? I'll just start with one of them.

• 20:17Speaker 01

  Yeah.

• 20:18Molly O'Shea?

  How are you going to become a trillion dollar company?

• 20:21Speaker 01

  Are you just trying to provoke my?

• 20:23Molly O'Shea?

  Yes.

• 20:24Speaker 01

  My emotions halfway through, get it warmed up a little bit.

• 20:27Molly O'Shea?

  Oh, we are going to go on a roller coaster of emotions here.

• 20:29Speaker 01

  I love it. Yeah, so how are we going to become a trillion dollar company? So I think like, again, it's surprising to me that I still have to.

• 20:44Speaker 01

  That I still sometimes struggle to get this across. Like you look at quantum computing. I work on quantum computing. People say it's science fiction. It's like fantasy land stuff. It belongs in the research lab. Why would the Fortune 100 or the NASDAQ or a late stage investor or BlackRock, you know, care about quantum computing? And it's baffling to me that people ask this question because just look at the most consequential, durably valuable, strategically important geopolitically significant companies of our age. TSMC, ASML, SpaceX, Nvidia, right?

• 21:29Speaker 01

  These companies, OpenAI, these companies are living or dying on the quality of their science.

• 21:37Speaker 01

  And they're all on the frontier, like way out on the frontier, boiling sulfuric acid. And, you know, bleeding edge rocket engines and nanometer scale fabrication and, you know, ten gigahertz, hundred gigahertz networking. And, you know, this is like very, very demanding bleeding edge science and engineering that is defining the frontier that's having huge geopolitical impact. And that is, uh, yeah, illustrating to me a sort of trend that humanity seems to be on, where power is captured on the frontier. And those companies are valuable, almost by definition,

• 22:20Speaker 01

  uh, or, or, or like almost as a consequence of the fact that they're doing something so technically difficult that nobody else can do it. Uh, there's basically only one TSMC, there's one ASML, there's one SpaceX, there's one Nvidia. And so we have a clear ambition to earn our place in that list of companies. And then to make it concrete, like, how does that actually become money? Look at the Fortune one hundred. I don't know what you visualize when I say Fortune one hundred. And so I typed Fortune one hundred into a, an AI image generator. Um, just type Fortune one hundred and you get an image of men in suits shaking hands with each other. That's what the AI thinks the Fortune one hundred is. Probably white, too.

• 23:03Speaker 01

  Yeah. Yeah. Men in suits shaking hands. That's the, and I think probably a bunch of people, when you say Fortune one hundred, like, that's what they picture. Either maybe a list, or yeah, a bunch of blokes shaking hands. That's the Fortune one hundred, right? And, uh, no, car companies, materials companies, energy companies, pharmaceutical companies, semiconductor companies.

• 23:27Speaker 01

  These are giant industries with huge sales teams and enormous revenue, but they all sit on a microscopic foundation of chemistry, physics and math. Whether that is the drug molecule, the fuel, the catalyst, the fertilizer, the semiconductor manufacturing process. And I like to visualize this sort of inverted pyramid where the whole thing is sitting on atoms and molecules and reactions, and it's really true. And what's exciting is that those microscopic foundations are massively overdue for reinvention. Like they're clearly not fit for purpose. Decarbonization and competitiveness and supply chains and all sorts of headaches that mean that we want

• 24:11Speaker 01

  new fertilizers, new fuels, new drugs, whatever. Currently, our ability to innovate at that microscopic foundation is profoundly limited because we cannot simulate those things. Our conventional supercomputers suck at predicting the properties of these microscopic things. And that happens to be exactly the set of problems where a quantum computer is perfectly suited and where we will solve problems that can never be solved by any conventional supercomputer that you could ever build. And, you know, we're living in this world where Mark Zuckerberg is posting pictures of a Manhattan-sized supercomputer, which is simultaneously thrilling to me, right?

• 24:55Speaker 01

  Like, it's cool, given our ambitions to be surrounded by people who are, you know, thinking the same way. But it's also kind of worrying for humanity that that cannot continue. Like, unless you're a full-on Dyson Sphere person, which you might be, Molly, I don't... Are you a Dyson Sphere person?

• 25:12Molly O'Shea?

  I'm not going to reveal that.

• 25:13Speaker 01

  Not going to reveal the position on Dyson Sphere?

• 25:15Molly O'Shea?

  Uh-uh.

• 25:16Speaker 01

  Okay, well, I'm, you know, I need a bit of persuading on the Dyson Sphere myself. I think a Manhattan-sized supercomputer is, uh, like, let's put it this way. It is likely the end of the road. Like, it seems difficult to me that you can indefinitely progress beyond Manhattan-sized supercomputers. There are people very seriously trying to pave that path, but I think there is profound risk ultimately in that direction. And so, yeah, we're offering an opportunity to go and capture

• 25:53Speaker 01

  virgin territory that will otherwise remain forever unexplored. And that happens to be problems that underpin, you know, our entire economy.

• 26:04Speaker 01

  So, you know, how can we become a trillion dollar company? It's by being the only company on the planet with a categorically new level of mastery over chemistry, physics and math. If we can dramatically accelerate our ability to innovate on drugs, molecules, fuels, catalysts, fertilizers, semiconductors, et cetera. And by the way, a string of things that we don't even know we want yet, but that we're going to find with this machine. If we can then go and actually vertically integrate on top of that fountain of knowledge that we're going to build in Australia and in Chicago. I think there's a pretty serious prospect that we do earn our place in that list of

• 26:49Speaker 01

  legendary companies. And it's bloody difficult, but it's definitely possible. And so that's why I've spent the last 10 years in pursuit of it and will continue to do so.

• 27:00Molly O'Shea?

  One of the undercurrents of the stories that you've mentioned in the examples is obviously a lot of the momentum that we're seeing with AI, it's unlocking new territories, it's creating new inflection points, it's even bringing back nuclear. Who would have thought we would have really made so much of an effort there? I interviewed Scott Nolan of General Matter, because they're creating a uranium enrichment company. Guess what? Uranium enrichment in the US, it's like less than 0.1%. It's crazy. It's like, it's really not there. So there's like these new inflection points popping up. And the way that quantum is being positioned, especially like in this era, is against this new wave of compute. It's like a third wave of compute.

• 27:45Molly O'Shea?

  So how do you see quantum positioned within this new wave of compute and like very competitive AI?

• 27:53Speaker 01

  To me, to my eyes, and I'm biased as a physicist and given what I do with my life and so on, but to my eyes, it really does look like humans are on this monotonic trajectory where they're reaching for more and more extreme technologies.

• 28:12Speaker 01

  Right, like smaller and smaller transistors beyond the diffraction limit down to three nanometers and unthinkable scale, you know, going after nuclear power, going after fusion, building Manhattan-sized supercomputers. Like, to me, humans are just reaching and reaching for these extreme technologies. Quantum computing is obviously an example of that. As far as how we fit into the AI and and supercomputing piece, let me talk about kind of the bridge into the future. And then maybe I'll talk about how those two technologies kind of interact with each other.

• 28:55Speaker 01

  I think we need a bridge to the future, unless we do Dyson spheres, which I don't rule out, and just, you know, manage and, you know, enable fusion in the next few years and have infinite resources and so on. Like barring that situation, I think we need a bridge that gets us out of otherwise quite a dire situation where we run out of power, run out of space and run out of data. We've already consumed the vast majority of language data that is on the internet. People are going to go and mine videos for a few years, but, you know, I think there is a risk, put it that way, that quantum computing helps to mitigate. A single million qubit quantum computer would outperform the net total of conventional

• 29:39Speaker 01

  computing on the entire planet. So you take all of the supercomputers on the planet, you take every AWS system, every Azure system, you add them all together, and we trash that capability for that specific set of problems in chemistry, materials science, et cetera. And so that's helpful. Like, that's a hundred megawatt kind of system. It's a single building. It's pretty ugly, but it is clearly giving us a bridge beyond otherwise a kind of cliff that I think we risk hitting with this linear scale up of conventional supercomputers. And then as far as the way these things interact.

• 30:24Speaker 01

  One of the things that we're most excited about is to use the quantum computer to produce training data that we can then feed to conventional machine learning systems. So with machine learning AI, it's been incredibly successful at making approximations that are learned from data. That's basically what AI does. A quantum computer is sort of complementary and diametrically opposed in a sense in that it is making exact calculations from first principles. So like when we add two and two and get four, we didn't learn that from data. That's just like first principles thinking. That's really what a quantum computer does.

• 31:04Speaker 01

  And with that, we should be able to generate extremely precise data in otherwise unexplored territory of chemical space, of material space, and use that data to train a conventional machine learning system. I think through that kind of mechanism, we can produce a wholesale upgrade of our mastery of those things and then go and use that without having to touch the quantum computer. Then you can go and actually exploit those models on a regular GPU. We're really excited about that prospect.

• 31:37Molly O'Shea?

  In today's high-speed business world, staying ahead means using the smartest tools possible, including the powerful capabilities of artificial intelligence. Meet Turing Intelligence. Turing builds customizable AI systems designed to solve your mission-critical challenges, no matter your industry. From expert guidance to tailored projects, Turing helps top companies realize AI that's more capable, more adaptable, and more effective. With Turing, discover how AI can accelerate your business growth. To learn more, visit turing.com/sourcery, spelt S-O-U-R-C-E-R-Y. That's turing.com/sourcery. Do you think that AI will help accelerate these quantum timelines? Chad Rigetti, actually, the famous Rigetti.

• 32:22Speaker 01

  Yes.

• 32:23Molly O'Shea?

  Company that is publicly traded, he just recently started a new company to take advantage of the new architectures. So how do you feel about that?

• 32:30Speaker 01

  Yeah, so I have a, Psychonum is pretty contrarian on a few things, like my life over the last decade probably would have been easier if I had just taken that chip I had in Bristol, put it online and made it available to people and then tried to raise money off of that. Uh, instead, we said, no, you need to go straight to a million qubits and I need a billion dollars to do it and so on. I'm also pretty contrarian on this AI stuff. So I think probably there's a good fraction of this audience who would love to hear me say, thanks to AI, now we can have the quantum computer much sooner. You're gonna clip that, I know you are.

• 33:04Molly O'Shea?

  I will, I definitely will.

• 33:06Speaker 01

  But uh.

• 33:07Molly O'Shea?

  I'll take it out of context too.

• 33:09Speaker 01

  So like, what is Psychonum, right? Psychonum is uh, uh, is a Silicon Valley startup company, but secretly we're a nineteen eighties Silicon Valley chip company and a big chunk of our DNA and our center of mass is a semiconductor chip company. Like where we spend most of the money is making silicon wafers in a fab in Global Foundries in upstate New York. And the, I have learned over the last decade, like the semiconductor chip industry is incredibly conservative. It's because if you tape out, if you send designs to the fab that are wrong, you don't find out for six months and it completely ruins your life.

• 33:55Speaker 01

  And so the extent to which we actually use new stuff is incredibly uh, low. Uh, we basically don't use AI at all inside the company to design chips. And I think if you go and look at um, uh, people who are building millions of chips in industry, there's a very small amount of AI, if any, that's being involved in the actual design of those chips today. It's a pretty boring, like old school, gray and, and, and kind of ancient uh, uh, practice to design and, and make these chips really work uh, as intended. Uh, so I like that. I think it's cool. Like we're kind of uh, building these chips in a very, very traditional, old school way and that is honest.

• 34:40Speaker 01

  See how the semiconductor industry operates apart from a few little frothy news stories that you see from time to time.

• 34:49Speaker 01

  So, no, I don't think that AI is going to dramatically reduce our timelines. Currently, our timelines are mostly determined, as far as I can tell, by infrastructure, right? We're breaking ground on these sites in Australia and Chicago. These are roughly hundred megawatt, multi-hundred thousand square foot sites. We've got to put about a hundred tonnes of hardware onto the ground to make helium on those sites. That's a multi-year exercise to stand up that helium liquefier for those machines. And so, like, none of that is accelerated by AI. AI, that's accelerated by digging faster, laying concrete faster, cutting metal faster. And there's a limit to how far you can go with that.

• 35:34Speaker 01

  So I think, I think actually it's probably the other way around. It's that, you know, we have technologies as a by-product of what we've done at SciQuantum that I'm pretty optimistic can be helpful in building next generation AI supercomputers in mitigating some of the frightening prospects that are otherwise faced with power and so on. And then ultimately, I want the quantum computer to produce data that is fed into an AI supercomputer. But yeah, that's an unpopular answer. We barely use AI inside the company to, to do this chip, chip design at least, yeah.

• 36:10Molly O'Shea?

  Contrarian take.

• 36:11Speaker 01

  Yeah.

• 36:12Molly O'Shea?

  We love that.

• 36:13Speaker 01

  We do use it for the decoder. So there's, you know, there are limited pieces where it's really, really impactful. So for instance, the system that runs the quantum error correcting code. It's a good example, actually, of the sort of, the surreptitious nature of these technologies where they're fantasy until they're not. I like to point at self-driving cars as well in this respect. Even like five years ago, VCs would laugh when I said self-driving. Like, they would say, self-driving fantasy, never gonna happen.

• 36:51Speaker 01

  You know, they'll get some demo working, but the idea of a fleet of Waymos driving across San Francisco with literally nobody behind the wheel is just, you know, you're very naive. You have no experience. I've seen this before. It's never gonna happen. And suddenly it's real.

• 37:04Molly O'Shea?

  Now they only wanna take a Waymo.

• 37:06Speaker 01

  Now those same people saying, I never, never wanna see somebody in the driver's seat ever again. Like with quantum computing, seven years ago, that decoder piece, which runs the error correcting code. People would tell ghost stories where they would say the decoder is gonna kill you. The decoder needs this like, you know, multi-gigawatt supercomputer to actually run that conventional logic that's just fixing errors in the machine. And over the last five or six years, those estimates have come down precipitously. And that's partly because of new approaches based on on neural decoders that have been really, really successful.

• 37:51Speaker 01

  So yeah, there are pieces of the machine where I AI is definitely gonna be like critically important for the success of the thing. But I I I like the the component where most of our chip development is would be completely recognizable to somebody from the nineteen eighties.

• 38:09Molly O'Shea?

  We're gonna get into chip development a little bit further, but while I still have you at the start, where you're you're gonna get another big question. Okay, so you've raised billions of dollars, pretty much billions at this point. But you don't yet have a product in the market.

• 38:24Speaker 01

  Yeah.

• 38:24Molly O'Shea?

  Why should the public believe in CyQuantum's vision?

• 38:28Speaker 01

  Let me let me not answer the question and then I'll answer the question if that's okay. So the non-answer is that, again, many of the most consequential companies of our time are not used by the public.

• 38:45Speaker 01

  So what happens inside TSMC? What happens in an ASML EUV machine? You know, programming a 100,000 GPU Nvidia cluster, operating a Raptor engine from SpaceX, or designing a payload that's going to go on a launch? These are incredibly esoteric activities. It's really striking to me that like these enormous companies that everyone nods their heads and agrees are legendary. The people who use those companies, like the the end user for those technologies, or like the direct interaction at least with that technology is an incredibly small number of scientists, basically.

• 39:27Speaker 01

  Like, how many people on the planet can operate an ASML EUV machine or design a payload for SpaceX or whatever? It's really an esoteric activity that is that is running these, that's fueling these companies. Quantum computing is the same, right? Like, most people will never program a quantum computer. Most people will never directly interact with a quantum computer, and that is fine. And that is how our economy seems to be operating at the moment. That's the first thing to say, which is this is not a mass mass adoption technology. But then, yeah, the the spirit of the question is very is very understandable, which is you've raised all this money. How do we know that you're not full of crap, basically?

• 40:11Speaker 01

  And so there's a whole variety of pieces of machinery that get deployed there. Firstly, of course, people do diligence on us. We know a huge fraction of the academic community who have come and examined the status of our technology, asked us a lot of questions, come to our labs, seen our data. In the case of the Australian government,

• 40:36Speaker 01

  they looked at two dozen companies in that process. The chief scientist of Australia at the end of that. Well, it's funny, there was actually a media, there was media coverage of that in which there was a, I think, freedom of information or similar kind of request. They dug up a bunch of emails in that Australian process. And one of the first emails from the chief scientist says that she's very skeptical of our approach. So this was a, you know, news story in Australia. Chief scientist highly skeptical of Sci-Quantum approach at the outset. To which I'm like, what do you want? You want her to be like highly credulous on day one and say, I love this? No, you want her to be highly skeptical at the outset.

• 41:18Speaker 01

  And then at the end of that, I don't know, eighteen month process, uh, she concludes that we're a country mile ahead uh of anyone else. Um, and obviously there's risk and, you know, you can never make these things black and white. But that was a very, very in-depth process. They came to the lab, they came and measured our devices, they came and looked over our shoulder and watched us reproduce these uh, these measurements. That's a level of scrutiny that no academic journal has ever done in my experience. Like, they never physically come to the lab and look over your shoulder. And then, you know, probably the, the, the biggest exercise that has been done to look at our technology from a third-party perspective has been DARPA.

• 42:04Speaker 01

  If I were, if I were like a member of the public paying taxes, which I am, uh, I would be uh, pretty frustrated by some of the sort of approach that's often taken to quantum computing, where people say, uh, it's a strategic priority, it's the future. And then governments just kind of hand money to companies. They say, we love quantum computing, quantum computing's awesome, here's some taxpayer money. And I'd be sitting there thinking, well, they haven't proven anything, they don't have a product. They've been talking about this quantum computing crap for years. I don't like the sound of this very much. And DARPA did something really nice, which is they did the opposite.

• 42:44Speaker 01

  They said DARPA is gonna start from the assumption that quantum computing doesn't work. Nobody's got any idea how to build one. Uh, they're full of crap on their timelines. And even if they could build it, it wouldn't actually be useful, it wouldn't do anything worthwhile. That was DARPA's starting assumption. And then they took fifty scientists and engineers and told them, essentially, I'm paraphrasing, uh, but I don't think it's, I don't think it's a, uh, terribly far from reality that they told those people, your job is to kill quantum computing companies. Like, your job is to go and talk to all of these guys and your, your success will be measured on how many of those you can shut down.

• 43:27Speaker 01

  I mean, I'm paraphrasing, but it's not that far from what they did. Which is awesome, right? Because like so now DARPA have, as I said, come and measured our devices. They've spent enormous amount of time with our team over the course of many years. And if you go and talk to DARPA people today, they will not say Sci-Quantum has a one hundred percent chance of success. There is no doubt in my mind, we can prove that they're going to be successful, etc. But what they can say is, we've had fifty people trying to kill this thing for years. We've been in their buildings, we've talked to their people, we've taken a sort of red team position on their approach. And we still have not killed them. And they have just graduated to the final phase of our program.

• 44:12Speaker 01

  They're one of two companies to have reached that point. And that to me is like a really serious way of dealing with a frontier technology like this, right? Like, I'm obviously biased as to the prospects of success and so on and so on. And it's really welcome to have a serious qualified bunch of people come and do that kind of red team exercise on what we're doing. And I'm very proud of our team who've made it through to the end of that. But yeah, like, we're obviously a deep tech company, we're obviously a zero to one kind of technology. There are a lot of examples of companies who've raised a hell of a lot more money than us

• 44:54Speaker 01

  in a similar kind of situation, not in quantum computing, but in kind of similar technologies. And I just think it's exciting that Silicon Valley and the world is still doing this kind of thing, like, and actually seems to be increasingly understanding that

• 45:13Speaker 01

  that you've got to do this kind of stuff, that you've got to push the frontier. I think it's good.

• 45:19Molly O'Shea?

  One thing that's just so fascinating with all of this is that there are so many quantum companies.

• 45:25Speaker 01

  Yeah.

• 45:25Molly O'Shea?

  And there's so many public quantum companies too, like, who would have thought you could just like take your pick out of like five, six stocks, but they all kind of like took the window, did a SPAC thing, and they're they're still around. In terms of that, how do you justify these public valuations? How do you justify private or public valuations for frontier tech?

• 45:49Speaker 01

  Yeah, so I mean, I I I uh I'm not gonna justify the valuations of competitors. I'm a scientist, but um uh yeah, there's tons of companies. If I were an observer of this field, I'd be really frustrated just by the endless competitive positioning that goes on. Uh every founder like me will come and talk to you and tell you why their technology is the best. And and everybody else's technology sucks and it's awful and like journalists and investors and government people endlessly frustrated with this kind of stuff and I would be frustrated too. I think it's probably like I wasn't there for it, but I imagine this is how

• 46:30Speaker 01

  memory technologies went in the sort of nineties in Silicon Valley. So there were people running around with different different physical implementations of memory for conventional computing. And uh um and all arguing in in in favor of one particular approach. And then, you know, from my from my point of view at least, it tends to be that that is much more of a filter than a race. Uh if you look at what we do with transistors, if we look at what we do with SRAM, if you look at all sorts of these kind of hard, physical uh technology um like landscapes, they tend to concentrate into one or two uh leading approaches.

• 47:16Speaker 01

  But yeah, on valuations, I mean, again, I I think to be sympathetic to these high valuations.

• 47:25Speaker 01

  If you can do it, you you earn your place in that list, right? Like TSMC, ASML, SpaceX, Nvidia, like you earn your place in that list. And they're not there yet. Nobody is. Nobody has a useful quantum computer. Um but all of these teams are seriously believing that they can get to that kind of uh stage. I think probably the the way I would put it is just that you've got to judge the risk on that, right? Like, you know, ultimately, are they on a trajectory that can end with world domination and, you know, multi hundred billion dollar valuations and so on? Yeah, I think that there is a serious prospect that.

• 48:08Speaker 01

  These companies do that, but there is a much greater chance that they go to zero is basically my simple way of putting it. If people want to bet on that, that's, that's cool. I would say, don't listen to anybody who produces a little table of the pros and cons of their own technology, where the checkmarks are all in their own column, and then you put crosses in everybody else's column. These tables somehow are very persuasive to people, and I think they're really stupid. I think, you know, better questions are again, like, with the understanding that you need a giant system to solve commercially useful problems. If I were looking at quantum computing

• 48:48Speaker 01

  companies, I would be asking, in addition to, you know, peer-reviewed academic literature on the qubit, the gate, the fidelity, all of that kind of stuff, like the basic properties of the technology, there's a bunch of questions that kind of go missing, which is like, who is your fab? Who is your contract manufacturer? Where are you getting power? Where are you getting water? Where is the plot of land where you're going to build this system? Has a government spent a few years diligencing you? Did they give you serious capital at the end of that process? You know, who's your building contractor? These are necessary but not sufficient questions,

• 49:30Speaker 01

  but they tend to get completely lost in this honestly kind of silly arguing over, you know, ions versus superconducting qubits versus photons versus whatever. That stuff is really important, but it's a small piece of the puzzle of building a data center sized machine.

• 49:50Molly O'Shea?

  One of the things I've been listening to public market analysts on this is the business fundamentals.

• 49:57Speaker 01

  Yeah.

• 49:57Molly O'Shea?

  And, you know, you can look at P E ratios, you can look at all these different things, EBITDA, blah, blah, blah. Some of them are looking at their cash positions. I'm like, hmm, like, really? I don't know if like cash positions is really like the great measure here. But a critical question below all of that is, is the revenue drivers.

• 50:16Speaker 01

  Yeah.

• 50:16Molly O'Shea?

  And so how are you going to be making revenue? How are all these other companies making revenue along the way?

• 50:22Speaker 01

  Yeah, I mean, I think it's unfortunate that people like me, who are scientists.

• 50:30Speaker 01

  Who end up founding these companies and having leadership positions in these companies.

• 50:36Speaker 01

  We get coached that there is a certain way to do a venture backed company. And the coaching is very often, you've got to get some kind of revenue by any means necessary really early. And that's certainly true if you're a SaaS company, right? Like if I'm building a SaaS company, I better go and produce some early revenues. But uh what happens with quantum computing is that it motivates people to go and twist themselves into knots. Trying to produce revenues with a machine that is categorically not useful. And that is kind of what has been going on in quantum computing. Right? Like that two Q bit chip that I had in Bristol ten years ago.

• 51:20Speaker 01

  It had a web API, people could sign in, they could run code. I could have started charging people for that, right? Like a few thousand bucks a shot or something and people would have paid for it. And it would create the impression that I'm on some incremental trajectory of revenue. Where that machine can be simulated by a pocket calculator and a laptop. It's a completely useless machine. And so the appropriate thing with quantum computing is to face up to the unpleasant reality. Which is that this is a zero to one technology, like self-driving, right? Like Waymo is, I think, a pretty good example here.

• 51:59Speaker 01

  Waymo is a useless company until they cross that threshold, until they got that fleet. And it's really working. And then you look at them and say, my God, these guys are going to dominate every major city in the world, right? And unless Tesla comes for them, but you know, they're now like they transition from enormous upfront capital. Like way more than anything we're planning for. They transition from that to uh something really compelling and exciting pretty, pretty fast. So it's important with quantum computing to understand, like you are on the far end of the spectrum in that respect.

• 52:39Speaker 01

  Get comfortable with it. Don't pretend that you're a SaaS company or some incremental type of technology. You have to own the reality of what you're doing. And then how are we going to make money? So we are basically hell bent on getting to that very large system as fast as humanly possible. And that is the promise that we've made to investors, to governments, et cetera. That capital, that time is only going to be spent to get to a really useful machine as fast as humanly possible. Once that machine comes online, we're not going to sell it. Selling a, you know, a hundred thousand square

• 53:19Speaker 01

  foot system is, is dangerous and stupid is probably how I'd put it. Selling time on it is like really natural, right? Like you want to sign in, run some code on the machine. We can charge people to do that. And again, you should sort of visualize this thing as a fountain of knowledge, as a machine, an oracle that can answer questions about chemistry, material science, et cetera, that no other system on the planet can answer. We expect that people will pay serious money. When, you know, currently they might spend a billion dollars over a decade to bring a new drug to market. They might be doing incredibly intensive trial and error development of new electrodes for lithium ion batteries. You

• 54:02Speaker 01

  know, there's enormous waste and time and overheads currently involved in these kind of processes. And so I think that's driving the appetite with our commercial customers that we have today to prepare for the existence of that machine. And so you can build a nice Excel spreadsheet, right? Where you model selling time on that system in a kind of cloud computing framing like Azure or AWS or whatever. And that's, you can pretty quickly convince yourself that that will be a very lucrative machine when we turn that system on. However, I look at that and I say, I've just spent my entire adult life trying to

• 54:45Speaker 01

  bring about an oracle that, you know, gives us a glimpse into virgin territory that we will otherwise never access. And I think, why are we, you know, why are we selling that in its raw form, right? Like, why are we just not capitalizing on that ourselves? And so I get a little bit into deranged founder territory here, but I think there is, you know, you look at that thing and you say, why would we not hire drug discovery people, hire material scientists, hire chemists and start to actually exploit the knowledge and information that's coming out of that machine, exploit that directly, build something that is more vertically

• 55:28Speaker 01

  integrated. We're not about to build Pfizer on top of a quantum computing company overnight, but we can definitely take steps in that direction. And that, I think, is how you build a company, the likes of which the world has never seen before, a legendary, generationally significant company is through that kind of little bit more vertical integration.

• 55:48Molly O'Shea?

  What would be the main value drivers of potential customers and who are your customers now?

• 55:54Speaker 01

  I think it's helpful just to think about what is currently done with conventional computing, and especially what's currently done with AI. When I look at language models as an outsider, I see those language models sort of consuming territory really quick, right? Like they're getting stronger and stronger, and they're starting to take over things that people have been doing, either with human beings or with regular computing at a very fast rate. But we also know there's a limit to that. Like, that kind of expansion of territory will stop at a boundary where the stuff on the other side of that boundary is for us, from my point

• 56:37Speaker 01

  of view, right? Like, we're gonna go after the stuff that people don't do with language models, et cetera. And as far as we're concerned, the low-hanging fruit is really materials and chemistry. That's where our, so we have about fifty-person applications team who engage with car companies, materials companies, energy companies, et cetera, to prepare them for the existence of that big machine. And a lot of that work is on small molecule drug discovery, catalysts, basically, small, hard molecular or like reaction chemistry, material science problems. When you talk about quantum computing, people sort of tend to list the same categories of

• 57:22Speaker 01

  application, and that's one of them. They often talk about optimization problems. They often talk about quantum machine learning. And then the one that always comes up like the elephant in the room is code breaking. And I think we'll probably touch on code breaking. But our view on optimization is that it's really difficult to do, to do like tremendously impactful optimization with a quantum computer. Conventional computers are already surprisingly good at optimization. And the quantum algorithms that we know of for optimization problems have some bugs, basically, like they're pretty painful to make them useful. So the jury's out is sort of how I put it on optimization. We're a little pessimistic on that piece. Quantum machine learning is really

• 58:05Speaker 01

  nice to think about. It sounds awesome in like a press release. It's really early is how I put it. Like the development of the theory is not yet at a stage where you can confidently say quantum computing is going to be awesome for accelerating machine learning models or something like that. You cannot say that at this point. There are sort of reasonable reasons to be excited, and there's some glimmers of excitement. That needs a lot more work. We do a lot of work on partial differential equations, which is jargon, obviously, but underlies a huge fraction of things that people do in financial industry, fluid dynamics, in aerospace, all sorts of stuff

• 58:46Speaker 01

  is sitting on PDEs. And we have some pretty exciting work on that piece. And then I think we'll come to code breaking. But yeah, the sort of macro picture that I think is helpful is that language models are going to eat language encoding and stuff like that. And then there's this whole territory of like the physical world, basically, that we're going to go after and that we think will remain mostly off limits.

• 59:12Molly O'Shea?

  One thing that you've mentioned a couple of times is the construction and the value where you think there's actual like there's actual weight behind these companies.

• 59:22Speaker 01

  Yeah.

• 59:23Molly O'Shea?

  For SciQuantum in particular, you have the Chicago site, you have Australia.

• 59:27Speaker 01

  Yeah.

• 59:28Molly O'Shea?

  You are starting to break ground and we're gonna do a tour on this on your, I don't know how to say it.

• 59:35Speaker 01

  South Bay.

• 59:36Molly O'Shea?

  We'll just go with, okay, we'll just go with South Bay, cuz I keep on messing up the name of that. Molybdis.

• 59:41Speaker 01

  Molybdis, yeah.

• 59:43Molly O'Shea?

  Yeah. You've you've set up shop in America. Actually, before that.

• 59:48Speaker 01

  Yeah.

• 59:49Molly O'Shea?

  Talk about those sites because I know each one of them is like completely different in like, I don't wanna give it away.

• 59:57Speaker 01

  Okay, you want just a map of the world?

• 59:59Molly O'Shea?

  No, no, no, cuz like, what I'm trying to get is like, you um, you built the Australian one.

• 1:00:06Speaker 01

  Yeah.

• 1:00:06Molly O'Shea?

  On an airport. The Chicago one is an old steel mill, like. Could you just break down how each of these sites you strategically found like the best possible place to build?

• 1:00:19Speaker 01

  Yeah, um, so, uh, I wanna make a drawing, but I.

• 1:00:25Molly O'Shea?

  Here's some paper.

• 1:00:26Speaker 01

  Yeah, I'll save you that. Um, yeah, so we, I'm British, everyone thinks I'm Australian. Uh, that's because.

• 1:00:33Molly O'Shea?

  Wait, you're not?

• 1:00:34Speaker 01

  No, I'm British.

• 1:00:35Molly O'Shea?

  You're British?

• 1:00:36Speaker 01

  Yeah.

• 1:00:37Molly O'Shea?

  Okay.

• 1:00:38Speaker 01

  That's what my girlfriend does when I, when we talk about England, she goes.

• 1:00:43Molly O'Shea?

  Cold place, warm place.

• 1:00:45Speaker 01

  Yeah, um, we started out in the UK, but my two co-founders are Australian and they were working in Australia in Brisbane prior to that. A lot of the foundational work on optical quantum computing specifically was done in Australia like twenty, twenty-five years ago.

• 1:01:02Speaker 01

  And they're just a few nations and like specific parts of the world that were early in investing in quantum computing. So, UK, Australia, uh, US, obviously, uh, Chicago specifically has a really, really strong concentration of uh, quantum computing stuff specifically, and that's partly because of their history, you know, national labs. First sustained nuclear reaction, uh, et cetera, et cetera. Great universities. But yeah, we we did the basic research in the UK for a decade at the University of Bristol and Imperial College. We now have a lab in the north of England, which is a decommissioned synchrotron accelerator. It's a UK government lab.

• 1:01:44Speaker 01

  And that happens to have a big helium liquefier that we can use to run test systems. We're not building a quantum computer in the UK at the moment, but we do have that R and D site that's been incredibly productive for us. We're very grateful to UK government for some funding there. Headquarters is currently in in California, so we're based in Palo Alto. Up until about nine months ago, our main lab for the whole company was 10,000 square feet of former conference rooms, basically. So nine months ago, we had raised 750 million dollars. We were

• 1:02:25Speaker 01

  a few hundred people and we were like crammed into this 10,000 square foot space where we've like ripped out the whiteboards and the coffee machines and put in like 300 millimeter wafer tester, like classic Silicon Valley. You walk through the front door and expect that it's like a crappy lawyer's office. And then it's like full of silicon wafers and liquid helium and cryostats and whatever. We still have that space, but it's it's way too small for us. And so now we've moved to the South Bay. We've got 140,000 square foot of space in this new building. We've got 40,000 square feet of former class 10 clean room space. That building used to have 10 megawatts of power to it.

• 1:03:07Speaker 01

  Currently, we're down to a megawatt, but we'll go back up to 10 megawatts. And my shorthand on what that building is doing is that it's the Foxconn of quantum computing. So our whole approach is to leverage existing fabs and contract manufacturers and so on. But that building is doing the exceptions to that rule. So where we have to do our own materials production or our own assembly, that kind of assembly line and clean room space is is here in the South Bay.

• 1:03:39Speaker 01

  We use global foundries in upstate New York, so we have ten people and ten shipping container sized semiconductor manufacturing tools. We've spent over a hundred million dollars over the last eight years or so to establish that manufacturing process at GF. That's an enormous tier one semiconductor foundry. I think four hundred and fifty thousand square foot, single span room, uh, full of tools, like many, many, many billions of dollars of of semiconductor manufacturing. That's where our silicon comes from. And then we use the Stanford linear accelerator on Sand Hill Road, so a lot of VCs endlessly drive up and down Sand Hill Roads without realizing.

• 1:04:19Molly O'Shea?

  This was really cool.

• 1:04:21Speaker 01

  Yeah, so right opposite light speed uh is the Stanford linear accelerator. I think you had not seen it.

• 1:04:26Molly O'Shea?

  I had no idea. And then you showed me on on Google Maps.

• 1:04:29Speaker 01

  Yeah, so people live in Silicon Valley their whole life.

• 1:04:33Molly O'Shea?

  Have no idea there's an alien structure there.

• 1:04:35Speaker 01

  By the way, they never see a wafer. Every time I show people a wafer, they're like, it's, why is it round?

• 1:04:42Speaker 01

  And I'm like, you're Silicon Valley. Like, what are you doing? You haven't seen a silicon wafer. You've never touched a silicon wafer. So that's that. And then people drive around in Silicon Valley and they don't know that there's a two mile long linear accelerator carving through some of the most expensive real estate in the United States. That's, you know, visible from space, immediately visible on Google Maps and cuts under two eighty. So when you drive down two eighty and there'll be a lot of people listening who drive down two eighty every day and who have never looked right at just the right moment to see an up very surreal looking object that stretches off into the distance. That's the Stanford linear accelerator. It used to be the longest building in the world.

• 1:05:24Speaker 01

  Uh, just sort of heading down towards Woodside. Um, there's a thirty six thousand watt liquid helium cryo plant on that site. So thirty thousand square foot uh, industrial facility and that's making helium at deep space temperature. So at minus two hundred and seventy degrees just over the street from light speed.

• 1:05:47Speaker 01

  Most people don't even have any uh, so when we were raising money, when we were raising series A. Myself and my co-founder were, uh, would ride our bikes up and down um, Sandhill, trying to find a series A term sheet. And it didn't cross our minds that there's kilowatts of liquid helium over the street. But we now have a, have hardware on, on the concrete there. So we have some of our cabinet, well, one of our uh, cabinets right now installed at Slack and uh, up and running and.

• 1:06:16Molly O'Shea?

  How do you get that? Like, how do you secure that?

• 1:06:19Speaker 01

  We are very grateful for our partnership with the Stanford linear accelerator and the Department of Energy, and they've been really gracious. They had some spare helium. Um, so that's a huge, like loud industrial facility that's making very large volumes of helium to run, to run the, the, the Linac. We're tapping off just a little tiny piece of that uh, to evaluate our system. And that, but it's pretty exciting because that is the same scale of cryo-plant that we will now put into Australia and Chicago. And the cabinet that we use is pretty close to the real form factor. So it's like a three ton, two meter high stainless steel fridge, basically, that keeps our chips cold. Um,

• 1:07:03Speaker 01

  it gets away from the chandelier. So I think when you say quantum computing, a lot of people immediately visualize the beautiful golden chandelier. We got rid of that. That goes in the trash and we have these like much more boring uh, cryostats. But yeah, we're very grateful to Slack for letting us uh, squat in their building and steal a bit of helium. Uh, and then, yeah, the main, uh, the main event is gonna be in Australia first. So Australia is gonna have the first very large scale machine uh, just outside Brisbane Airport.

• 1:07:38Speaker 01

  And then the site in, in Chicago used to be the US Steel site. So it was a. Uh, you know, linchpin of US industrial might during the Second World War, used to make a million tons of steel a year. Enormous steel foundry. Uh, that site has been empty for a few decades. And uh, obviously, you know, used to be furnished with enormous amounts of power, water, et cetera. We're just thrilled to be uh, the anchor tenant of that site, to be breaking ground this year on these big systems. And uh, and to be surrounded by people in Australia and Chicago, who are world-class. You know, scientific expertise in quantum computing, error correction, et cetera.

• 1:08:23Speaker 01

  But also like bias to action and bravery. So in Chicago, they reversed the flow of the river. Uh, to make it flow out of the lake. They lifted entire city blocks off of the ground like it's nothing. Like, we needed, we need to be surrounded by people with that kind of mentality when we're, you know, forging ahead. With the kind of aggression that quantum has.

• 1:08:47Molly O'Shea?

  Okay. I'm just really curious, in the theme of reindustrialization. I think Global Foundries is probably one of the most underrated companies in America.

• 1:08:58Speaker 01

  Yeah.

• 1:08:59Molly O'Shea?

  Could you share more about your work with them and how they operate?

• 1:09:03Speaker 01

  Yeah, so again, like, thi- this, this is to the theme, which is that. I'm still, I'm still amazed that people don't, are not more intensely focused on this thing that. Power is being captured on the frontier of advanced technology. Still, when you say, when you talk about. You know, these very advanced technologies, quantum computing, uh, semiconductor manufacturing, et cetera. Still, people's eyes glaze over a little bit and it's like, you need to get with the times. Like, ten years ago, if you put a silicon wafer in front of. The average politician, they didn't know what they were looking at. They'd never even heard of a semiconductor fab.

• 1:09:47Speaker 01

  And certainly, they didn't care at all about semiconductor manufacturing. Now it's on everyone's lips. Everyone understands that the fact that TSMC is located in Taiwan is geopolitically profoundly impactful.

• 1:10:01Speaker 01

  Politicians even now know where ASML is and why ASML matters. Like, that's an upgrade. That's very welcome. And I've forgotten the question, sorry.

• 1:10:14Molly O'Shea?

  Why is, why is global foundries underrated?

• 1:10:17Speaker 01

  Yeah, that's right. So, so now, like, politicians and investors and so on cleanly understand that you have this kind of concentration of power into a few companies, a few bleeding-edge technologies, and surprisingly small pieces of land. Like, it's shocking.

• 1:10:35Molly O'Shea?

  Yeah.

• 1:10:36Speaker 01

  If you just think about how much land does TSMC occupy, how much land does GF occupy? And the same goes for AI supercomputers, the same goes for rocket companies. These are incredibly consequential companies, and it's like a tiny plot of land, like you can barely even see it on the map. And so, yeah, semiconductor manufacturing is firmly in that category. In the United States, we have Intel, of course, and they've been in the news, I heard recently. But GF is really the last remaining pure-play semiconductor, like, tier-one semiconductor foundry in the United States. So we offshored huge fractions of our semiconductor industry.

• 1:11:18Speaker 01

  As far as like a high-volume 300-millimeter foundry that is making chips for industrial applications and for sort of high-volume, GF is it, they're the last remaining capability. They're in upstate New York and.

• 1:11:35Molly O'Shea?

  What's their facility like?

• 1:11:36Speaker 01

  So it's this enormous fab, right? Like, it's, I, we have been working with GF for, so, just zooming out, right? Like, if you want to make chips, you can make chips in a university cleanroom. I just saw this morning somebody who's made a fab in their garden shed, and you can, that's brilliant for my narrative.

• 1:11:55Molly O'Shea?

  Oh, wow.

• 1:11:56Speaker 01

  Because now you can literally make chips in your garden shed. But the quality of chip that you're gonna make is very, very far removed from what you need for a useful application. And so there are tiers of fab.

• 1:12:12Speaker 01

  And you basically wanna be in a big fab. If you wanna have good uniformity, if you wanna have high device performance, and if you want a promise that you can make thousands of chips that are all the same, you need to be in a tier one fab. There are very few tier one fabs on the planet. TSMC, uh, Global Foundries, Samsung, Intel, uh, SMIC in China, there's a few others. Um, but they're very limited and GF is one of those. Uh, their facility in Upstate New York is a huge clean room. So, you know, you gown up and put a mask on and so on. And then they're just, uh, multi hundred million dollar semiconductor manufacturing tools, stretching as far as the eye can see.

• 1:12:53Speaker 01

  It takes twenty five minutes to walk from one corner of the room to the other corner of the room. And so we have been working with these guys for like five or six years before they let me in the building. They finally let me go in there. And I've been talking about it and raging about, you know, the semiconductor industry as leveraged to get us to big machines quickly. This has been the sort of religious belief of SciQuantum, uh, for many years. And actually getting in that building for the final, for the, for the first time, was a religious experience, unbelievable. Just scale and, and, and dynamic range, right? Like they're making things with Angstrom control on the thickness.

• 1:13:37Speaker 01

  So sub nanometer control on thickness. And then this thing, it takes twenty five minutes to walk across. You've got boiling sulfuric acid, you've got wafers at thousands of degrees Celsius, you've got liquid helium at minus two hundred and seventy. And obviously, like, I'm confident in what we're doing. I've been doing this my whole adult life. But occasionally I worry about the difficulty of building a quantum computer. And, uh, going to that fab, uh, was hugely encouraging cause you look at what humans have been able to do in a tier one foundry like that. And it makes almost everything else that we do as human beings look pathetic, uh, by comparison. It's unbelievable, the precision, the control, the extreme nature of

• 1:14:22Speaker 01

  everything that they're doing in that facility. And yeah, GF is a trusted fab. They make, uh, silicon for.

• 1:14:30Speaker 01

  Uh for defense applications, they make a ton of silicon for industry. And it's really like a point of pride that we're in there. Like when I say quantum computing, again, naturally, people, their brain immediately goes to a physicist in a university research lab, making some crappy like demo device, which is what we were all doing. You know, five or 10 years ago. It's quantum computing and tier one foundry don't naturally belong in the same sentence. Like, there's a parallel universe in which we went to GF six years ago, seven years ago. It's pretty daunting, right, to show up on the front door of a facility like this, a legendary company.

• 1:15:12Speaker 01

  And say, hey, um, I'm a, I'm a graduate student.

• 1:15:18Speaker 01

  I want to put superconductors into your factory, please. I'm going to put quantum computing devices into your factory. There's a parallel universe in which they called security and said, there's some mad people at the front door. Please have them removed uh from the property. And uh I'm glad that they didn't. And now that that puts us, you know, shoulder to shoulder with people who are routinely making tens of thousands of wafers, millions of chips. Uh and that's, yeah, that's always been critical to our approach.

• 1:15:46Molly O'Shea?

  What's so astonishing is like you have laid out such a complex supply chain and so many different touchpoints of just one company. How many jobs do you think this will create if, you know, quantum does really work?

• 1:16:02Speaker 01

  Yeah, so we've um shared some job numbers in Australia and Chicago. And that's really, you know, pretty specifically to support the sites that are getting built. So that that's a data center like facility. You can't build and operate something like that without hundreds of people. But that like is a, you know, very um reductive way of looking at the jobs that are created by what what we're doing.

• 1:16:31Speaker 01

  I don't want to be a pessimist, but again, coming back to some of our earlier discussion, discussion, to me, it looks as though. Humanity is rapidly moving forwards on this trajectory of of like pushing the boundary of what we can do with advanced technology. And there are real risks that you stop, right? Like, there are real risks that you run into a barrier of power or uh or cooling or space or computational mode of operation that means that you stop innovating or you stop pushing that boundary. And so I have no idea how you quantify the impact on the economy and jobs in that situation.

• 1:17:16Speaker 01

  But um what we're trying to do is build a machine that allows us to continue on that trajectory as a nation, as a species. And uh yeah, if you want to grow an economy, uh it seems to me that you want advanced technology. And so that's obviously a very broad macroscopic view on things, but that's really how I see it. If we if we continue to use the same old petrochemicals, certainly the same semiconductor manufacturing processes, uh the same fuels, the same drugs, we're just stuck on that for the next decade. Uh, that's not a healthy situation for a an economy.

• 1:17:59Molly O'Shea?

  Yeah, I was I was talking with Pri about this and um not to quote her, but some are expecting over fifty thousand jobs created in areas of quantum manufacturing from chips to cryogenics.

• 1:18:14Speaker 01

  Yeah.

• 1:18:14Molly O'Shea?

  And that's before counting the scientists and the technologists.

• 1:18:18Speaker 01

  So like eighty percent of our supply chain is in the United States. We have hundreds of companies in that supply chain here in the U S. Um, and we have obviously a further like worldwide supply chain. So I don't know the exact number, but it's hundreds and hundreds of companies.

• 1:18:35Molly O'Shea?

  Let's get the exact number.

• 1:18:37Speaker 01

  That already extend to thousands of people who are involved in working on on parts for our for our systems.

• 1:18:45Molly O'Shea?

  Mm hmm.

• 1:18:45Speaker 01

  And that's only going to grow. And of course, but but again, I think that all pales in comparison uh to uh the broader context, which is that like um.

• 1:18:58Speaker 01

  How do you assign the number of jobs that we have now? That we like, how do you try to estimate the number of jobs that? That are in the US, let's say today, that wouldn't exist? Had we not come up with high performance computing? So the ability to simulate new fuels.

• 1:19:22Speaker 01

  Likes especially things like, you know, fluid dynamics, design of aircraft. All of the stuff that we can eventually do with with with high performance computing. Erase that from history, right? Like go back in time and say, we never, we never invented supercomputers. What impact would that have on the number of jobs in the United States? I have no idea, but it's it's catastrophic to our progress as a as a species. So, yeah, I don't know. It's difficult to estimate, but.

• 1:19:50Molly O'Shea?

  So I've seen quantum get framed politically over and over again as kind of like a scare tactic.

• 1:19:56Speaker 01

  Yeah.

• 1:19:56Molly O'Shea?

  Everything from cybersecurity, code breaking, China. And just these like overall doom narratives. So how much of this is hype and PR versus genuine interest in progress?

• 1:20:08Speaker 01

  If you build a big enough quantum computer, you can break. Most of the encryption, public key encryption that we currently use on the internet. Uh, that's a fact. And um, actually, there was a recent paper which reduces the resources required to do this. Uh, quite significantly. Uh, so that is a widely publicized prospect that a big quantum computer is going to let you break encryption and read secrets. And um, that's a, that's completely real.

• 1:20:41Speaker 01

  There is a sort of um, cringe worthy thing that people do where it's just so. Transparent that they just imagine a big lever that says China on it. And people like me who want money, they think you just pull the lever and money comes out. They just say China, China, China, China. And then imagine that money is just flying to the company through this mechanism. Like it's kind of cringe worthy and a bit lame, if you ask me. Like the, the thing that to me is clear is like just a slightly different framing on that. Do your, do you want your nation? To be on the frontier of advanced technology, like, do you want your nation to have the best

• 1:21:23Speaker 01

  technology? That seems like quite a straightforward question to answer. And so, like, the risk is real, like, there is a clear technical basis for these concerns. I think sometimes the way people wield that as a sort of marketing tool is a little bit unfortunate. And then the good news on that is that, as far as we understand, still, despite these new architectural results, algorithmic results, you still need a bigger computer to do code breaking than you do to tackle the commercially useful applications that we talked about earlier. So that's good for us

• 1:22:03Speaker 01

  because I want to be rich. And as far as we know, we're going to get rich before we get dangerous. You can imagine a parallel world where, for whatever reason, nature conspired such that we get dangerous before we get rich, right? Like, if the number of qubits and gates needed to break encryption was really small, it'd be really annoying because I'd have to deal with all of those, like, frightening geopolitical implications before I can even buy my Ferrari. Fortunately, it's the other way around. So you should get, like, an economic early warning sign, essentially, where you should see quantum computing companies doing commercially impactful things, making money, and then eventually scaling towards systems that are big enough

• 1:22:48Speaker 01

  to be cryptographically relevant. So hopefully that helps people to sleep at night a little bit. But yeah, there is absolutely serious technical reality to these things. Sometimes they're wielded in a slightly silly way.

• 1:23:02Molly O'Shea?

  You've definitely got some support from the public side and government, but has The public side been overall very supportive to this industry?

• 1:23:12Speaker 01

  Yeah, so I think sometimes people are surprised to see like BlackRock and Bailey Gifford and people like that on our on our cap table. Obviously, like those guys, that category of late-stage crossover type people, they were in SpaceX, they were in Nvidia, they were in ASML when ASML was a pretty crazy prospect.

• 1:23:36Speaker 01

  And again, like, just look at that list of companies, like, it's not, I don't think it's as surprising as people make out. And with quantum computing, like, there is this sort of additional mystique. Every time you talk to people, they say, they kind of give up before they've even started, right? Like, they say, oh, quantum computing, I can never understand this, it's just crazy, spooky action at a distance and so on and so on. I'll never understand the physics, I can't engage. When I talk to people like that, I sort of like to, my co-founder particularly likes to make the point that if you go to the average technology conference, you'll find thousands of people who are very comfortable talking about GPUs

• 1:24:20Speaker 01

  and, you know, transistors and three nanometer and so on and so on. And then you ask these people, okay, like, you love AI, you love computers, you love GPUs. Please get on the whiteboard and explain to me how a transistor works. Like, talk me through Fermi levels and electron hole pairs and the physics of a transistor. And it's like hopeless, right? Like, ninety-nine percent of people, most physicists can't give you a really good explanation of how a transistor works. It's pretty bloody involved physics. And so the fact that a, you know, late-stage crossover investor, whatever, is looking at quantum computing,

• 1:24:59Speaker 01

  you can't rebut that from the point of view that, you know, the physics of the device is involved. The same is true of transistors, right? Like, there's nothing unusual about that, really. And so, yeah, we're delighted to have that support. BlackRock and various of those people have been supporters of the company for many, many years now. They came in at Series C, and then, you know, it's been nice to be able to. Show them that we've done the things we said we were gonna do with their capital. And to have them continue to support the company over, over years like this has been very gratifying to me. Yeah.

• 1:25:34Molly O'Shea?

  So I've heard I'm going to go put on a bunny suit tomorrow.

• 1:25:37Speaker 01

  Yeah.

• 1:25:38Molly O'Shea?

  What is going on at this facility?

• 1:25:40Speaker 01

  Yeah, so this is the facility that I didn't mention in my long list of locations, but this is also in the South Bay here.

• 1:25:49Speaker 01

  We've been missing a piece, basically. So hopefully I've given you a good sales pitch that we've got the chip manufacturing at a high level of maturity. And we threw away the chandelier and we're building these parts. We needed to switch lights, like route light with incredibly high performance. Uh, this isn't really a quantum problem. It's just an optics problem. But to do that, we had to make a new material. And so this was sort of again, like outlier to the standard approach. Where we're trying to use existing stuff. We were forced to do something pretty unusual. This was a big piece of like the risk that we uh went after with our series D uh fundraise.

• 1:26:35Speaker 01

  And so we told Blackrock and Co., don't worry, we got it covered. We're just gonna uh occupy an empty building in uh in San Jose. We're gonna build five clean rooms in there. We're gonna put the biggest molecular beam apotaxy tool in the world ever in that, in that facility. It's never been built before. The vendor, by the way, thinks it's a terrible idea for us to do this and it's not gonna work. Various of our employees think it's not gonna work. And then what we're gonna do is we're gonna have molten titanium at a few thousand degrees Celsius. We're gonna have liquid helium at minus two hundred and seventy degrees, same temperature as deep space. We're gonna expose them to the same vacuum chamber.

• 1:27:15Speaker 01

  And then we're gonna grow essentially perfect material uh that nobody else can make on a giant wafer. We're gonna do it clean enough that we can then FedEx that up to New York to put into level foundries. And then we're gonna have our device and um. The shipping company actually dropped the main chamber, uh, I think up in Alameda and broke it during Covid.

• 1:27:37Molly O'Shea?

  Oh my god.

• 1:27:37Speaker 01

  Uh, so we had some, uh, some, uh, drama in bringing that thing online. But yeah, huge risk for the company, new material science, never before demonstrated tool. Requirement to get that into a, into the highest level of maturity of chip manufacturing that exists. And, um, a lot of trust put in us by those series D investors. And, uh, it's just been a wonderful thing. So we now make that material with essentially perfect. Properties, we ship it off to GF, we integrate it into the flow and we make these switches. And we need that for quantum computing. They're beyond state of the art in performance. They're published in Nature, uh, a few months ago.

• 1:28:21Speaker 01

  But when we published that paper in Nature, I think it was something like fourteen, uh, regular. Optical networking and semiconductor companies rang up global foundries, asking how they could get access to that material. Because it is basically the strongest optical material known to science. We're the only people in the world who make that on a three hundred millimeter wafer. So you're gonna go and see that tool and, uh, get in the clean room. And if our yield goes down, uh, or if our quality goes down in a few weeks' time, I'll know who to blame.

• 1:28:55Molly O'Shea?

  It'll be me.

• 1:28:57Molly O'Shea?

  So like, logistically, this might be a dumb question, but do you need like armored cars to ship this across America? Like. It gets bumpy. How do you ship these?

• 1:29:07Speaker 01

  We FedEx.

• 1:29:08Molly O'Shea?

  Just like a straight up FedEx truck?

• 1:29:11Speaker 01

  Yeah, uh, the semiconductor industry is pretty amazing. There's a lot that goes under, under the hoods that people, that would blow people's minds. Um, I mean, we're also, so that material is a very special material. There are a few other people who make it around the world, but we're the only people who make it on a big wafer like this. But then that's getting integrated with other components at Global Foundries that we also design and where our team is located in the fab doing that development. I was very gratified when invited to go and testify to Congress to be able to tell them that our company has spent north of a hundred million dollars on sovereign semiconductor

• 1:29:53Speaker 01

  manufacturing in the United States, and a lot of that has been those components at GF. And all of that stuff has to work in concert. So like, you know, Sci- Sci-Quantum is obviously a company that has to take security really, really seriously. Quantum computing is a dual-use, sovereign, strategic capability. We red-team our IT systems, we leave USB sticks in the parking lot, we work with the three-letter agencies, we do, you know, all of the, you know, basic hygiene and, and steps that you would imagine that we take. And that helps me to sleep at night. But the thing that really helps me sleep at night is that we are ahead on a really,

• 1:30:35Speaker 01

  really complex, incredibly technically challenging technology. And you've seen actually some of the, some of these companies, they publish their patents, right? Like you saw that from Elon a few years back. That's, that's a familiar kind of stance to me because we have a ton of IP. The IP is awesome. But really, the thing that makes me feel strong is that we're the only company in the world who is actually capable of executing on this stuff. We're ahead and we've got our foot on the, on the gas pedal, not least thanks to this recent funding rounds. So, you know, in addition to all of that kind of hygiene and security

• 1:31:22Speaker 01

  work. The main responsibility on the company is just to be ahead, move faster, execute better. And so, and I currently were able to do that on that material and on those devices, yeah.

• 1:31:36Molly O'Shea?

  So what is SciQuantum's role in reindustrialization?

• 1:31:40Speaker 01

  Yeah, so I think twofold. Firstly, we're actually doing it today. We're laying concrete, cutting steel, building parts in concert with hundreds of companies here in the United States, eighty percent US-based supply chain. Everything from the Angstrom to the kilometer, right? Like, we're spending hundreds of millions of dollars making chips, making big metal boxes, flowing helium, doing, you know, breakthrough work on quantum algorithms and quantum error correction. Very high dynamic range, like, that's one of the joys of the company, is that we have everything from extremely rare, elite, mathematically oriented people who do the algorithms and architecture work,

• 1:32:22Speaker 01

  through to people who are, you know, laying a hundred thousand square feet of concrete and putting walls up, everything in between. We're doing that today. We're not just talking about it in the future, like, we already do that stuff. We just tore up the floor here in the South Bay. We just had a cryo-plant arrive, which I think maybe you're gonna see later today, or hopefully soon. We've put ten shipping container semiconductor manufacturing tools into GF, like, we're doing it, we've already done it. But then, yeah, in the in the future context, again, we really hope that the quantum computer is this kind of fountain of knowledge, engine for innovation that allows

• 1:33:02Speaker 01

  this country and our species to move forwards beyond what otherwise, I think, look like boundaries to continued progress. If we can't deterministically design the microscopic foundations of our world, I think it's really difficult to just continue. And we see that so clearly with semiconductors, right? Like, we're coming to the end of Moore's Law. We cannot make smaller transistors. That's gonna get really, really painful in the next decade. And so, a future, you know, industrial environment, I think is hugely benefited by the existence of a big quantum computer.

• 1:33:44Molly O'Shea?

  Want to talk a little bit about your sanity through all this? Cause this is.

• 1:33:48Speaker 01

  Based on the previous ninety minutes of discussion.

• 1:33:50Molly O'Shea?

  Yeah, yeah, I'm uh, I'm a little concerned.

• 1:33:54Speaker 01

  Yeah.

• 1:33:56Molly O'Shea?

  No, but um, so.

• 1:33:58Speaker 01

  So am I, actually.

• 1:33:59Molly O'Shea?

  You are?

• 1:33:59Speaker 01

  A little bit, yeah.

• 1:34:00Molly O'Shea?

  Why?

• 1:34:02Speaker 01

  Um, you only get one reference point in life, you know, like this has been my entire adult life. Um, when I start talks these days, like public talks, I like to start by showing an image of a black hole. Uh, it's a beautiful historic image of a simulated black hole that was uh, done by a French gentleman, Jean Pierre Lumiere. Uh, and that was done on IBM punch card computer, so there was no display on these systems. You look at this image and it's clearly hand painted.

• 1:34:39Speaker 01

  So this French guy got hold of an IBM punch card machine and spent presumably a very long time feeding punch cards in, getting numbers out, and then with a paintbrush and India ink and a canvas, painting the data that was coming out of the system. And it's this stunning picture of a black hole, um, very similar to the images that you see in uh, in Interstellar. Um, but done decades prior, and of course decades before human beings ever actually saw a black hole uh, directly. Um, must have been like, must have been astonishing feeling to see that image appear.

• 1:35:19Speaker 01

  Um, but I show that uh, both as a sort of testament to the power of seemingly primitive computers. You know, that's a really primitive looking machine relative to what we have on our desk today. Um, but it's still showing us a glimpse of something, you know, unfathomably far away in our universe.

• 1:35:42Speaker 01

  But also as a sort of allegory for the way that quantum computing has acted on the lives of people like me. It's very rare that you have a technical goal like this. Where quantum computing has already consumed billions and billions and billions of dollars of capital. It has consumed the entire adult life of tens of thousands of our best scientists and researchers. Some total has got to be nearly a hundred thousand years of human life. Billions of capital and, you know, inordinate amount of time has been thrown into the pit of quantum computing. And that's, it's a sinister, I'd show you it deliberately as a sinister image, right? Like so far, we have made this huge investment.

• 1:36:27Speaker 01

  And we don't have useful quantum computers today. We know that it's possible to build these machines, like it is definitely physically possible. And so, um, yeah, I feel a huge responsibility that we make this successful and I'm very lucky that we have a real shot on goal to do it. But yeah, it's a, it's a big responsibility and a pretty weird technology to be involved in. I think, you know, one thing that really helps with the sanity is that, like so many things, you can look at quantum computing through two lenses. One lens is, this is alien technology from outer space, baffling physics, spooky action at a distance,

• 1:37:10Speaker 01

  mysterious, et cetera, et cetera. And then there's another perspective that I think is pretty helpful, which is, what are we really doing? And especially when you talk to people and they say like, why in Brisbane, Australia? Or they say, why in Chicago? And to those people, I always sort of say, like, what are we doing? We're building a big computer, big, ugly, building scale computer. We're building it.

• 1:37:38Speaker 01

  Uh through collaboration of private industry and public funds. And we're doing it uh outside of the capital city of the nation, right? Like you're not building it in uh in downtown Washington DC or Times Square or something. You're building it somewhere else. And it's going to be operated by scientists and it's going to give us a view into the future of what we do with technology. That story is a story that's decades old. People have been building leadership class supercomputers for a very long time. And usually people don't care about them and that's fine. Like, nobody cared prior to G P T three.

• 1:38:19Speaker 01

  Nobody cared that Azure and Microsoft and OpenAI were building big ugly computers. Like, nobody even thought about that. They only care about the output. And so for the average person, I hope that they never care about our quantum computer, that they never even think that there's this pretty nondescript looking warehouse sitting out there next to the airport or on the south side of Chicago. But I really hope that one day they use a drug or a fuel or a fertilizer or a material or some technology that simply would not have existed without the lens and the simulation capability that quantum computing is going to give us.

• 1:38:59Molly O'Shea?

  Rex, since you're such a big fan.

• 1:39:01Speaker 01

  Mm hmm.

• 1:39:02Molly O'Shea?

  And I think you're converting over pretty soon.

• 1:39:04Speaker 01

  Mm hmm.

• 1:39:04Molly O'Shea?

  Um, they're all about spending smarter, moving faster.

• 1:39:07Speaker 01

  Mm hmm.

• 1:39:07Molly O'Shea?

  How have you, as a company, thought about doing all of these things in the most efficient way possible? How do you think about performance as a company?

• 1:39:16Speaker 01

  Back to the eighties, uh Silicon Valley hardware company DNA, uh bad coffee, polystyrene ceiling tiles, dilapidated former conference rooms converted to lab space. Like, to me, as a, as a uh.

• 1:39:36Speaker 01

  Immigrant to Silicon Valley, that stuff is so cool. That stuff is like legendary, right? Like that you walk into some space that isn't polished and beautiful with a, you know, ten thousand dollar coffee machine and bean bags and massages, but you walk into somewhere pretty grim and find the most astonishing technology being built by the most legendary people. Like, that ethos is embedded in the, in, in SiQuantum. Partly deliberately and partly simply by virtue of being a chip company. I think that kind of comes with the territory to some extent, because chip manufacturing is bloody expensive. Um, I think more recently, we, we have a bit more scope than that. Like,

• 1:40:17Speaker 01

  we have a little bit more dynamic range. Obviously, we now have this wonderful new facility in the South Bay that is a massive order of magnitude step up from, from where we have been for the last eight years. Um, and then we, we don't only do chips. We also have algorithms people, we have theory people, we have special people at the company. But yeah, to me, it's pretty cool to have that kind of pretty thrifty, pretty, um, uh, modest approach to our environment in which we're operating. I think that's, um, it's traditional for hardware companies.

• 1:40:57Molly O'Shea?

  And what's your favorite flavor of chip that you make?

• 1:41:01Speaker 01

  Favorite, I, so actually, uh, I don't know whether this is public or not. It's probably not. Have to save that one. Uh, favorite flavor of chip?

• 1:41:11Speaker 01

  Um, oh, we did actually have a visitor, uh, who, uh, will remain nameless but knows who he is, who came to see the BTO wafer. So the BTO wafer, you're gonna see it tomorrow. It's like, it's just a blue disc, but the blue on that disc is a very special blue. It's made with the strongest electroptic material known to science. And, uh, that's a really special object, right? Like, there's, there are very few of those ever been made in the world and took us a ton of blood, sweat and tears to be able to make that. After this visitor came, he left and texted, I think he tweeted that he'd just seen the wafer. And then he was asking the internet uh whether he should have licked it or not.

• 1:41:56Molly O'Shea?

  Oh my god.

• 1:41:57Speaker 01

  And um we're never letting him back in the building unfortunately. But uh that we, yeah, I think, I think it's probably fine. So you could find out.

• 1:42:07Molly O'Shea?

  If he licked it or not?

• 1:42:08Speaker 01

  No, you could find out how BTO tastes.

• 1:42:10Molly O'Shea?

  Oh, I could do a taste test.

• 1:42:13Speaker 01

  You could do a taste test.

• 1:42:14Molly O'Shea?

  We could line them up.

• 1:42:15Speaker 01

  Yeah, the um there is the, the, obviously, the human uh taste uh capability is, is borderline science fiction in terms of like how good it is and.

• 1:42:28Molly O'Shea?

  This is how you do quality testing.

• 1:42:29Speaker 01

  And so on, yeah.

• 1:42:30Molly O'Shea?

  Mm hmm.

• 1:42:31Speaker 01

  Um, no, I mean, I just like.

• 1:42:36Speaker 01

  You asked about my sanity earlier, right? Like I, I came out here a decade ago and I fully expected to be sent home packing with a failed company six months later.

• 1:42:47Molly O'Shea?

  Mm hmm.

• 1:42:49Speaker 01

  And I've never, I've never done this before. I've never founded a company before. The only real job I've ever had is stacking compost in a garden center. And so it is daunting to me to go to like the biggest pure play foundry in the United States, to go to the Blackrocks and, you know, all of this stuff that is just so far from my prior experience and make these claims. Yeah, yeah. We're gonna put superconductors into a commercial semiconductor foundry. It's gonna be fine. We're gonna build this clean room and we're gonna grow epitaxial barium titaniumite on a silicon wafer. Uh, this architecture is like ten thousand times too bad right now, but we're just gonna make it better at a

• 1:43:32Speaker 01

  geometric rate for seven years running. Don't worry, the next seven years, we're just gonna progressively make that architecture better. We're gonna keep innovating, whatever. Like, it's daunting to make those claims. And it is a, you know, personal joy and uh and treat to be able to then come back to those people a few years later and say that we actually did it. So when I look at those wafers that you've seen in our, in our facility and I think about the fact that there's thirty five layers of stuff on that wafer, that there's a superconducting material inside and that it was made in a commercial foundry next to regular chips. Yeah, it's a pretty special feeling. I love that stuff. It's awesome.

• 1:44:14Molly O'Shea?

  I have to ask you the question you don't want to be asked.

• 1:44:17Speaker 01

  Please.

• 1:44:19Molly O'Shea?

  When do you think the first useful quantum computer will be developed?

• 1:44:23Speaker 01

  Yeah, so it's a it's a great question. I would ask people not to try to nail it down to the week, which they sometimes do, like Get Real. This is a big, very difficult in uh infrastructure project.

• 1:44:36Molly O'Shea?

  We need to get down to the day.

• 1:44:37Speaker 01

  The plan of record that the company marches to is that we want to have that site enabled by end of twenty twenty seven in Australia. Uh that's what we march people to. That is an incredibly a difficult thing to do. That's a really aggressive timeline. But we've done well as a company historically by driving people to, you know, the most aggressive timeline that we can reasonably stomach. And that's what Silicon Valley companies do. As part of the value proposition of a startup is that we're willing to try to do things extremely quickly. And we have done that historically. If you look at claims that we've made about standing up a completely new manufacturing flow at GF

• 1:45:18Speaker 01

  or standing up this BTO material or building these high-power cryostats. I keep a pretty good record of claims that we've made historically, and I think we do a uh pretty good job of sticking to those claims, especially given the novelty and the difficulty and so on of what we do. But then also to rationalize that claim.

• 1:45:41Speaker 01

  Uh again, I'd go back to Jensen's comment, right? Like, okay, you got to scale up by ten thousand X. That's twenty or thirty years. XAI Colossus supercomputer built in a hundred and twelve days. You know, much kind of bigger system than than what we're building in many ways.

• 1:46:01Speaker 01

  We're not going to build this thing in a hundred and twelve days, obviously. We know limits on the speed with which we can commission the cryo plant and, you know, various other sort of constraints. But, you know, going five or ten times slower than that, that sounds pretty reasonable to me. And that's essentially what what we're trying to do. If you think about that timeline, I don't know, six times slower or something than Elon. That's that feels like a reasonable ambition for a company like ours.

• 1:46:27Molly O'Shea?

  It was funny. I tweeted out this Kashi market, which currently has. Before twenty twenty seven at eighteen percent, twenty thirty at thirty percent, twenty thirty five at forty five percent and before twenty forty at fifty five percent. And one of the comments on it was by Martin Shkreli.

• 1:46:45Speaker 01

  Yeah.

• 1:46:46Molly O'Shea?

  And he said, as the CEO of Rigetti famously said, define useful.

• 1:46:51Speaker 01

  Yeah. So I mean, like defining useful capitalism has a pretty helpful uh assist on that, which is that when you're making a ton of revenue, uh you're probably doing something uh that, you know, within reason you could call useful. And uh uh like a billion dollars of revenue would be a nice starting point for a useful machine like this. Um uh yeah, definitely what is not useful is pure pedagogical science like learning, uh which is what people are currently doing with quantum computers. It's valuable. It's important to move uh towards useful machines. But it's not commercially useful, and that's what we mean by useful.

• 1:47:36Speaker 01

  Um but also like the polymark, it's on polymarket.

• 1:47:41Molly O'Shea?

  No, it's on Kalshi.

• 1:47:42Speaker 01

  Uh what?

• 1:47:43Molly O'Shea?

  Uh. They have a big rivalry.

• 1:47:44Speaker 01

  Oh, I didn't know that.

• 1:47:45Molly O'Shea?

  Yeah.

• 1:47:45Speaker 01

  Okay, Kalshi.

• 1:47:47Molly O'Shea?

  Mm hmm.

• 1:47:48Speaker 01

  Well, I would have loved to see, I think they didn't have Kalshi or polymarket uh before. Uh like they didn't have those, what did they exist prior, well, they weren't like, they weren't like popular prior to like G P T three or prior to.

• 1:48:06Molly O'Shea?

  No, like I.

• 1:48:07Speaker 01

  Like I think.

• 1:48:08Molly O'Shea?

  Kalshi just first got regulated like in the last year.

• 1:48:10Speaker 01

  Yeah, yeah, yeah. I mean, people were talking about it decades ago, obviously, but like the this wave of people really using those systems. I think it would have been fascinating to see what that looked like if you ask people about like uh about. Like the Turing test, for instance, prior to G P T three, or if you ask them about self-driving five years ago, I guess. Would have been pretty interesting to see what people say.

• 1:48:36Molly O'Shea?

  Dialing into Nvidia and how Jensen apologized.

• 1:48:40Speaker 01

  Not sure he actually apologized.

• 1:48:42Molly O'Shea?

  He walked back on his comments.

• 1:48:44Speaker 01

  Yeah.

• 1:48:44Molly O'Shea?

  Of when he thinks the timeline for.

• 1:48:47Speaker 01

  Yeah.

• 1:48:47Molly O'Shea?

  Quantum will actually come to pass. And I think one of the interesting points that I gleaned from that is. He seemed to be confused and so um. He actually positioned it as more of like a branding and marketing problem for quantum. And I think like this new wave of A I and technology and frontier tech. That is taking on a new language and marketing positioned.

• 1:49:07Speaker 01

  Yeah.

• 1:49:07Molly O'Shea?

  Is probably like. An opportunity for more language in quantum. I don't know if that makes sense, but. To quote him, he said, I do wonder whether quantum computing is simply poorly positioned. Because it was described as a quantum computer instead of a quantum instrument, which is. A little bit contentious for some people. Um. And then he added, there's a common sense about what a computer is. It has to have memory. It has to have networks. It has to have storage. It should be able to read and write, but there's a programming model associated with computers. I wonder if it's just a wrong mental model. What do you think about the positioning of quantum as it's evolved through time? And how should it be labeled today?

• 1:49:49Speaker 01

  I think that Jensen's um. Use of the word instrument is very well um. Is is very sensible and it it's uh it's pretty instructive, actually.

• 1:50:07Speaker 01

  When people think of computer, like the average person, they think of laptop, cellphone, something like that. Um. The the applications of quantum computing. Are similar to the applications of high performance computing. They're about seeing into the future or about seeing into some system that you otherwise can't access. And so thinking about a quantum computer as a telescope or a lens or an instrument or a measuring device or something. That's a very helpful analogy just to differentiate from PowerPoint and Microsoft Excel and TikTok. Right. Like it's really got nothing to do with that stuff.

• 1:50:49Speaker 01

  Uh, although TikTok would be really, quantum TikTok would be really good. Um, but, uh, yeah, so I think, I think that's, that's, I like, and I, I've said, I've said earlier. I really worry that CEOs of this, these, these hyperscalers and um, leadership of the, you know, here in Silicon Valley, I worry that they're going to get completely detached from reality on what quantum computing is for. And I've actually been honestly pretty impressed that they know you need about a million qubits. They know you need error correction. They know that it's not for PowerPoint and Excel. They under, and you know, these guys have got 20 other things of extreme high priority that they need to be thinking about.

• 1:51:34Speaker 01

  So I'm actually pretty relaxed that they basically understand the nature of, of what's going on. The same maybe cannot be said for some of the public market investors. And it's pretty uh, striking, let's say, that there are, that there are now public quantum computing companies where anybody can, can, uh, can get a view. But um, the other thing, the other thing on that is, is that. So I, I, I, I'm fully on board with the instrument language. Okay. I think that's really, really helpful. Um, as far as marketing of quantum computing and positioning and so on.

• 1:52:11Speaker 01

  Again, I'll just emphasize that the number of people who directly interface with the products of TSMC, ASML, SpaceX, Nvidia, like how many people do a tape out at TSMC? How many people run an ASML EUV machine is minuscule number of people? And so there are these giant companies where their contact point with the outside world is incredibly narrow. We are categorically one of those companies. The downstream effects, we think are profound, but the direct interaction with the company is is very narrow. And so I would love to be able to do this without any publicity. It'd be awesome, right?

• 1:52:56Speaker 01

  Like, and and it feels as though you should be able to develop technologies like this and get them funded and get them built without some giant marketing campaign. And we generally have, you know, bias towards not spending a lot of time on TV and in the newspapers.

• 1:53:15Speaker 01

  But quantum computing is also so kind of thought provoking and exciting to people. And we think, you know, ultimately, you know, profoundly impactful and determining factor in all sorts of things that there's no getting away from the fact that people really care about it and they want to understand it and they want to hear about it. And so, um, yeah, messaging it carefully is is a sacred responsibility that falls on leaders of these companies, people like myself, et cetera. Uh, it's a delicate thing to get right, yeah.

• 1:53:50Molly O'Shea?

  As we wrap up, what are you most looking forward to in the next year?

• 1:53:54Speaker 01

  In the next year?

• 1:53:55Molly O'Shea?

  Mm hmm.

• 1:53:56Speaker 01

  Uh, I mean, hopefully we'll break ground on these on these sites. That's going to be a pretty special experience, having been thinking about building these systems for fifteen plus years of my life and twenty plus years of my co-founder's lives, uh, to actually be able to point at the piece of land where we're building these systems is going to be, uh, special. And then also, um, as I said, we've up until very recently been kind of grinding away in a pretty small, not fit for purpose kind of space, suddenly expanded into this much bigger facility. Um, and so that is now just like a, a engine of, of, uh, of, of activity,

• 1:54:39Speaker 01

  hive of activity. Um, that's just thrilling to be close to that. Like it was completely empty six months ago. We've now got a cryo plant there. We've ripped up the floor. We've laid concrete. We're putting fiber attached tools into a clean room space. Suddenly there's a hundred plus people in the building, like just seeing the pace of that. And, you know, I've been doing this for a long, long, long time. It's very difficult. You're always staring, you know, at the top of Everest and um, in moments of, uh, depression or frustration or whatever, always the good thing to do is just go to the lab, go and see the people who are actually doing stuff. It always cheers me up. They always have something that I didn't know they were doing.

• 1:55:22Speaker 01

  And, you know, one of the exciting things about quantum computing for me is that when I look at conventional computing, as we've discussed, uh, it looks as though there is still some life in it. There's still great progress to be made, but it is also clear, clearly coming to the end of life, right? Like Moore's law is over. We're turning on nuclear power stations that'll get us so far, but you know, how much further can we go with conventional computing? It looks a little worrying. And this year, Jensen will build an AI supercomputer and he'll look back on that 10 years from now and say, that's a pretty good system. Like it's a pretty badass system.

• 1:56:06Speaker 01

  With quantum computing, we're really at the very beginning. We're really early. And so when we build our parts for our systems today, when we build those systems in Australia and Chicago, um.

• 1:56:21Speaker 01

  We're going to look back on that two years in and we're going to say, man, those were horrible systems. Why did we design it like that? You know, this was completely, you know, inefficient use of space or power or whatever. And we're already thick into that, which to me looks like the early days of the microelectronics industry, or of mainframes, or of conventional computing, where every month, you're looking at what you did the previous month and saying that was stupid. We should make it smaller. We should make it higher density. We can reduce errors. So to be on a gradient like that is life giving and privilege. Yeah.

• 1:56:58Molly O'Shea?

  That's a great answer. Well, Pete, it was a pleasure to have you on.

• 1:57:01Speaker 01

  Thank you so much, Molly. I really appreciate it.

• 1:57:03Molly O'Shea?

  We didn't have to like, you know, refill the batteries every so often. We could just keep on going. Do you think you have it in you?

• 1:57:11Speaker 01

  What if we, it's lunchtime? Get some lunch and then crack back on?

• 1:57:15Molly O'Shea?

  Might as well.

• 1:57:16Speaker 01

  Great.

• 1:57:17Molly O'Shea?

  Well, thank you so much for coming on and congratulations on the announcement and the big funding.

• 1:57:21Speaker 01

  Thank you.

• 1:57:21Molly O'Shea?

  It's huge.

• 1:57:23Speaker 01

  Privilege.

• 1:57:23Molly O'Shea?

  Hey, it's Molly. If you enjoy our interviews, check out our newsletter, sorcery.vc, where we deliver a once a week top deals and tech headlines email, and also go deeper on our podcast interviews. Subscribe to Sorcery today. And don't forget to subscribe to the podcast on YouTube, Spotify, Apple, or wherever you listen. Link in description to sign up.