Sachin and Adam
00:07:31 · Pete Shadbolt
[00:07:31] S0: Yeah, it's a great question. So I think that the first thing to say is that quantum computing is not a mass adoption technology. You should not expect that millions of people are directly interfacing with the quantum computer. Quantum computing is going to be narrowly available to a very small number of acute, hard problems. And that is true of many of the most important industries that we have on our planet. So So if you think about the biggest, most consequential, most important companies of our time, TSMC, ASML, SpaceX, NVIDIA, what number of people directly interface with an ASML EUV tool? How many people have ever done a tape out of TSMC? How many people design a payload for a SpaceX satellite? How many people have ever actually written a kernel for a GPU cluster? It's a really small number of people. It's like hundreds of people who really do that work. And so the first order utility of the product is opaque to the average person. And that's fine. Like, that's a very natural, healthy property of our world. And it's really the downstream products. It's the second and third order effects of these pretty alien technologies, right? Like a semiconductor fab, 99.9 of people will never step foot in that building. And if they did, they'd seed stuff that's totally baffling, like boiling sulfuric acid and two nanometer transistors and so on. They don't care. What you care about is that you can watch YouTube on your cell phone at the end of the day or that you can buy a drug in the pharmacy or you fly on an aircraft where that wing wouldn't have been designed were it not for a leadership class supercomputer in a DOE national lab. So quantum computing has exactly the same property. The machine and the simulations and the calculations that that machine does, those are always going to be the domain of weird people in a building somewhere who, you know, talk in code, but it's the downstream effect. It's the drugs, the fuels, the catalysts, the fertilizers, the next level of mastery over chemistry, physics and math that will flow down into our economy, into our society and ultimately into our daily lives. And so I really hope that in the near future, people are using catalysts, fertilizers, drugs, fuels, whatever that wouldn't exist were it not for the computational capability of these machines. And honestly, they might not know.
00:10:17 · Pete Shadbolt
They might not know that that thing was designed on a quantum computer. And to me, that will be success. I'll be delighted that they don't know that that thing was brought into life partly by virtue of this technology.
00:10:33 · Adam Miller
[00:10:33] S3: I want to thank Notion for sponsoring this episode. Without companies like Notion, these episodes wouldn't happen. They're ambitious and they cost money, and Notion backed us early. But the other way that Notion helped us is that we've literally been using the product since day one. We've been using it for media, operations, product. Within media we organise all of our videographers, our sponsors, our episodes on Notion. It's an absolute lifesaver. It's basically where our whole company operates. But the thing that was a real game changer for us was Notion AI, because our company is bigger now. We've got 25 plus people, different divisions, so there's a lot of docs, systems and structure. That's helpful, but with Notion AI, I can simply prompt something in a chat bar, so I don't even have to go into docs anymore. It's a lifesaver. Think episodes like the one you're watching today about PsiQuantum and Pete, it's really reiterated this idea to me about focus. If you want to be a standout company and do extremely ambitious things, you've got to focus on the one thing that matters. And for PsiQuantum, it's building a 1,000,000 qubit quantum computer. It's not docs, it's not administration, it's not boring paperwork. And that's what Notion allows you to do. As a founder, focus on the things that only you can do, people, culture, your product, and then let Notion take care of all the boring stuff in the background. We've got a special offer at notion.com/sachanandatom. Give it a try. I promise you, I've never heard anybody that's regretted it.
00:11:59 · Sachin
[00:11:59] S1: I think there's a really interesting analogy there, particularly with TSMC, one of the most important companies in the world, not many people actually understand how it works, and they understand that NVIDIA uses a lot of their chips, etc. I think for the audience watching, can you really clearly articulate what this world may look like in ten years? Obviously, you have dedicated your career to this. What is the use case that gets you out of bed every morning going after this problem? And what do you think the world will actually look like in ten years?
00:12:30 · Pete Shadbolt
[00:12:30] S0: Yeah, so again, to this point of these iceberg like technological capabilities, fabs are now all over the news. But even six, seven, eight years ago, nobody cared about semiconductors. Nobody cared about fabs. Most politicians, if you show them a wafer, they don't know what they're looking at. And and that was that was a trillion dollars. A trillion dollars of investment over fifty years to build that semiconductor industry. That happened in a deeply unsexy way. Nobody cared. And it, you know, gradually became this geopolitically significant world changing capability. Similarly with AI, as you guys know very well, AI was a failed field, multiple winters, defunded by DARPA, defunded by UK government, had a bad reputation as a science project that was always over promising and under delivering. People would go to... And it's it's, like, hard to conceive of today, but only, like, you know, ten years ago, people would go to AI conferences and they'd write statistician on their badge because they were so embarrassed to work in the field of AI. And it took decades of work, and it took sustained improvement of the algorithms and architectures, transformers and attention and all this stuff that we read about on LinkedIn now. And it also took people building huge GPU clusters at enormous expense. And again, prior to GPT-three, nobody cared. Somebody in that Microsoft CFO department was saying, Why are we buying all these bloody GPUs? What's this AI crap? And so quantum computing is at that stage where we haven't delivered anything yet. There are no useful quantum computers on the planet. We're spending a lot of money. We're building huge machines. There is a very strong scientific consensus that that's gonna bear fruit. But you're absolutely right. We're asking people for a challenging amount of patients. And that's kind of the nature of the beast. These technologies, again, if we think about TSMC, ASML, SpaceX, Nvidia, they all took decades to really get there. And now we're really delighted that we've gotten. And yet quantum computing absolutely has the same dynamics.
00:15:01 · Adam Miller, Pete Shadbolt, Sachin
[00:15:01] S3: Why is PsiQuantum's approach the right approach? Because there's a number of different approaches in the field that Google, IBM, different companies at PsiQuantum are doing. You've sort of got this approach of trying to build this enormous 1,000,000 qubit machine, which means that is this really long time an investment to then show this almost final product that causes technical milestones along the [00:15:22] S0: way? [00:15:22] S3: Why go with that approach? [00:15:24] S0: Because it's scientifically correct, is the short answer. So there are no known applications that are actually worth doing for small quantum computers. [00:15:38] S1: And you actually did one of first ones, right, with the API that you released? [00:15:41] S0: Yes. So you probably felt [00:15:42] S1: this visually that probably was there any good use case of it?