Republic opens private markets as Positron takes on GPUs: A TWiST500 doubleheader! | E2176

Today's show: In this founder-focused episode of This Week in Startups, we sit down with Republic's Kendrick Nguyen to learn more about the company's efforts to make the private markets accessible to the common investor. Best known for its work in equity crowdfunding, the Valor-backed startup now offers access to secondary shares, tokenized assets, and much more. Following, TWiST spoke with Positron CEO Mitesh Agrawal to learn more about his company's inference-focused AI compute hardware. Related to fellow TWiST500 company Etched, Positron is building custom chips to take on the computing work required to deliver your AI query results faster and with less power draw than what GPUs can offer. With AI inference compute demand rising, the fellow Valor-backed startup has even more powerful systems coming to market in 2026 that have our hopes up that the AI cost curve can continue to point downward. Timestamps: (0:00) A future with more efficient and accessible intelligence(0:41) the following sponsors are mentioned: AlphaSense, Vanta, and Oracle Cloud Infrastructure(1:26) Introduction to the episode and the two featured companies, Republic and Positron(2:43) The history and purpose of Republic, a financial crowdfunding platform(5:39) Discussion of the current state and growth of equity crowdfunding(9:19) Kendrick Nguyen explains Republic's role as a financial infrastructure company, not a competitor to venture capital firms(11:17) Oracle - Try OCI and save up to 50% on your cloud bill at https://w⁠⁠⁠⁠ww.oracle.com/twist⁠⁠ (12:25) Republic's different products, Republic Capital and Republic Venture, are explained(14:00) The challenges and progress of secondary trading for non-accredited investors(20:14) Vanta - Get $1000 off your SOC 2 at https://www.vanta.com/twist(21:21)The concept of a unified "e-finance" infrastructure is introduced(25:45) Positron CEO Mitesh Agarwal discusses the future of AI chips and the limitations of current GPUs for inference workloads(31:12) Alphasense - Get deeper insights into your business with the power of AI search and market intelligence. Start with a free trial at https://www.alpha-sense.com/twist(32:22) Discussion on the inefficiency of GPUs for inference and how Positron's Atlas system addresses it(38:30) Positron's strategy of using their Atlas system to prove their technology and generate revenue(42:51) The market shift from AI training to inference and the future of Positron's chips(51:14) The confidence in Positron's capital efficiency and their ability to compete with NVIDIA(52:47) Positron's focus on linear algebra acceleration rather than just transformers Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp Follow Lon: X: https://x.com/lons Follow Alex: X: https://x.com/alex LinkedIn: ⁠https://www.linkedin.com/in/alexwilhelm Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis Thank you to our partners: (09:04) Netsuite - Download the ebook CFO's Guide to AI and Machine Learning for free at https://www.netsuite.com/twist (21:20) Coda - Empower your startup with Coda's Team plan for free—get 6 months at https://www. Coda.io/twist (31:43) . TECH: Say it without saying it. Head to get.tech/twist or your favorite registrar to get a clean, sharp .tech domain today. Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland Check out Jason's suite of newsletters: https://substack.com/@calacanis Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com 🎥 Watch the full episode here 👇

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

• 0:00Speaker 00

  It just strikes me that we're going to have a future that's going to be a little bit more efficient than people think. We're not going to boil all the oceans. We're still going to have power for our appliances. And we're going to have essentially infinite intelligence in our pockets and all of our screens. And to me, that's a pretty exciting future.

• 0:14Speaker 01

  And we better, we will need to have infinite intelligence. And that's the whole goal. Like, if you ask Nvidia, if you ask us, if you ask any other silicon company, I think they're all trying to say that, you know, we are, we are approaching it with some different approaches. But basically, it's like, how can we bring like cost per token or cost per generation down? So that we can afford to have infinite intelligence while increasing our energy, energy production as much as we can.

• 0:41Speaker 00

  This week in startups is brought to you by AlphaSense. Get deeper insights into your business with the power of AI search and market intelligence. Start with a free trial at alpha-sense.com/twist. Vanta, compliance and security shouldn't be a deal breaker for startups to win new business. Vanta makes it easy for companies to get a SOC two report fast. Get $1,000 off for a limited time at vanta.com/twist. And Oracle, Oracle Cloud Infrastructure, or OCI, is a single platform for your infrastructure, database, application development, and AI needs.

• 1:22Speaker 00

  Save up to 50% on your cloud bill at oracle.com/twist. Hey, everybody. Welcome back to This Week in Startups. My name is Alex and today I am bringing you two startup interviews. Now, both of these companies share a backer, Valor Equity Partners. So if you want to know what one venture capital firm sees in the future, well, here is a good look at it. First up, we're going to talk to Republic. You may know them back in the day as an equity crowdfunding platform. But since then, it's gotten into quite a lot more activities. A little bit of venture capital, some secondary shares, tokenization of assets, you name it, they're working on it. I care about this company because it's democratizing finance and bringing more investment opportunities to more people.

• 2:02Speaker 00

  So that way, when my kids are older, well, they're going to have a lot more different things to put their capital into than I did when I was their age. Then we're going to talk to Positron, another company that wants to build chips for faster and more energy efficient AI inference compute, given that around the world, we're seeing data centers run into power availability problems. What they are working on could have enormous application, enormous revenues, and also may just challenge Nvidia a little bit around the edges in the next couple of years. These chats were a lot of fun. I learned a lot. I hope you love them. Let's get into it. Here's Republic. To set the stage now, making normally out of reach assets and investing methods more accessible to everyday investors is big business. Robinhood grew on the back of zero cost trading and bringing

• 2:45Speaker 00

  access to more exotic financial trading tools to regular folks, to pick an example. Now, Republic, in contrast, has long been known for its place in the crowdfunding market. Spun out of Angelus, the company has raised more than $200 million, including a well-known $150 million round in late 2021, led by Valor Equity Partners. Now, in the intervening years, Republic has expanded its feature set far beyond traditional crowdfunding. So to tell us more about the state of alternative investing and where Republic sees our democratized financial world heading, please welcome to the show. It's Kendrick Nguyen, co-CEO of Republic. Kendrick, how you doing?

• 3:20Speaker 02

  Alex, thank you so much for having me. I'm doing fantastic.

• 3:25Speaker 00

  I also love that we have in your background, the, and I say this with nothing but love, the de facto Silicon Valley office. It is standing desks, multiple monitors, and people typing, and that is exactly my happy place. I love to see it. How many folks do you have behind you?

• 3:39Speaker 02

  You know, maybe about 40 or so, and we in Silicon Alley. So New York has become quite the tech hub as well, and we're super proud to be here. But our heritage, our roots, go all the way back to Silicon Valley a decade ago.

• 3:53Speaker 00

  So this is off topic, but I'm just curious, while I have you here, we're talking about the geographic split, because I'm on the East Coast too. As the co-CEO of a leading technology startup, do you ever feel that magnetic drag back to San Francisco? Because the way I hear it told, in the AI era, SF has once again regained its primacy as kind of, if you will, the New York City of tech.

• 4:14Speaker 02

  You know, when we launched Republic back in 2016, Naval Ravikan, my

• 4:20Speaker 02

  Mentor and boss at the time was like, hey, everyone is, you know, based in or come to Silicon Valley. Why are you looking to move to New York? And my view is twofold. One is that we already have Angel List and San Francisco as the roots, right? That's how I got to know a lot of the VCs from my days at Angel List. And secondly, if you're building fintech, then I think it's a good thing to be based out of the financial capital of the world. And that's obviously New York City. So in the particular industry that we in, in the business that we in, I think that us being headquartered in New York with a lot of trips and events and a network back into

• 5:05Speaker 02

  the valley is a very ideal way, an ideal setup for us.

• 5:10Speaker 00

  Yeah, I don't even want to know how many frequent flyer miles you've racked up flying to SFO over the years. Okay, but let's get down to brass tacks here. I know Republic, I've known about the company since it was founded. I've always thought about it as a crowdfunding platform. Clearly, you guys do a lot more, and we'll talk about that in a second. But just to get people caught up, what is the state of crowdfunding today? It was pitched as a way to really break open a lot of private companies for folks. So has it lived up to that potential? And is it still growing?

• 5:39Speaker 02

  Alex, if I may, just define crowdfunding first and foremost. It's that is so, you know, it's so broad. Kickstarter is crowdfunding, Indiegogo is crowdfunding, Angel List is crowdfunding, right? You have accredited investors coming in to co-invest in a deal. Republic only took advantage of a law that changed in 2016 that allow for anyone, no matter what net worth, what income can come in and back and invest and have equity upside in a wide variety of businesses. So fast forward nearly 10 years now, there's no doubt that the business model has proven to work, not just for early-stage tech companies, but late-stage movie financing,

• 6:23Speaker 02

  music financing, crypto, that is digital securities being fractionalized. So I think that we're still in a long first inning of this next era, but there's no question that is well underway. But I think the true potential, the size of it, is something that is to be seen in the months and years ahead.

• 6:47Speaker 00

  I appreciate the clarification. I probably should have said equity crowdfunding versus crowdfunding writ large. I mean, one of my favorite bands, Archspire, is crowdfunding their next record. That's not the same thing as what we're discussing here. Okay, so fair enough. But there was a change in late 2020 that raised the cap that a company could raise in equity crowdfunding dollar amount from, I think it was, Kendrick, 1.1 million to five. And so I'm curious now, four or five years past that point, what impact did that have on equity crowdfunding? And also on Republic's business?

• 7:17Speaker 02

  It brings later stage companies because $1 million is still a lot of money. But for companies that's already in this Series A, Series B, Series C, $5 million as a cap is a little bit more meaningful. They can engage in bringing more customers, more community members. And you know, I'm optimistic that that cap will continue to go up to perhaps 20 millions. And even there's been talk in DC of potentially expanding it to $100 million and beyond. So again, just the very beginning, but there's no question that currently as is,

• 7:56Speaker 02

  we're working with large enterprises across a wide range of sectors and industries who want to engage the public through this regulatory framework in the US.

• 8:07Speaker 00

  So generally speaking, I'm in favor of the cap going up. I'm curious, though, is there a ceiling that you would put on it? Is there a dollar amount that's probably too big? Or is there no real limit here in your view of how high we should raise that max limit?

• 8:22Speaker 02

  You know, I am definitely a firm believer in free market to do so sensibly. But I think this arbitrary cap on 5 million or 10 million, I think it's a little bit artificial. I think there are other ways to address, to protect investors' interest, right, against arbitrary thresholds. So if it were up to me, I would not put a threshold on it, similar to under Regulation D or if you're going to go public, for example.

• 8:53Speaker 00

  Yeah, no, I'm with you on that. I was curious if there was any technical or hidden risk that I wasn't thinking of, but I'm glad that we're aligned there. Now, equity crowdfunding, one part of the business, clearly not the entire thing. You also have Republic Capital, which I believe has something like 500, 600 million AUM. Is that a traditional venture capital vehicle that I should kind of think of as in competition with Sequoia? Or does it have a different posture on the market that I should keep in mind?

• 9:21Speaker 02

  Alex, it's a great question, and allow me to define what we are today. And I think it ties everything in together. The business of Republic is building the infrastructure that can accommodate capital raising and community investing, next generation. What does it mean? It means that the regulatory framework to take in non-accredited retail, the ability to accommodate institutional as well to SPV or direct, the ability to do secondary trading, the ability to tokenize and put a, whether it's a small business or a, you know, a certain crypto project on a revenue sharing basis on chain, right?

• 10:04Speaker 02

  So when we first launched, the first piece was equity crowdfunding. We added on capital and of course to generate revenue with the syndication and other things, but think of it as different components that when all in together enable an enterprise to fractionalize, tokenize, and engage with the retail public globally with liquidity. That's obviously a very ambitious plan, and it took us 10 years to get to where we are today, which is the first year that we have a completely functional infrastructure for RWA real world asset tokenization for true retail participation. We're the only one, as far

• 10:47Speaker 02

  as I know, in the market that have that complete regulatory framework. Now, we do have, you know, we add on business line, business model, but no, we're not looking to compete with Sequoia. We do SPV, we do syndication, I'm wearing a shirt from one of our portfolio company called K2. But, but the business of Sequoia's venture capital, the business of Republic is in financial infrastructure and asset tokenization.

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• 12:25Speaker 00

  Okay, it's almost like you read my notes because that's where I was going to take us after a couple of questions. So we're going to loop back to the tokenization and the platform element of this. I just wanted to break down the different things you're doing today, and then we'll talk about how they come together. So Republic Capital then is a tool to collect retail and probably family office money and put it into SPVs. Is that the main work that it does?

• 12:49Speaker 02

  Correct, that you aggregate larger check side from family offices and institutions into SPVs and deploy them into more mature companies, that's typically private equity or venture capital. And that is Republic Capital.

• 13:06Speaker 00

  And then you have a separate product called Republic Venture that is only open to accredited investors, so not the masses, as it were, but folks that meet a certain test or threshold set by the government. And you and I have probably similar views about that. But I'm curious, can Republic Venture customers invest in the Republic Capital product?

• 13:25Speaker 02

  Yes, I mean, Republic Venture is just earlier stage deals and Republic Capital aims at larger track in later stage companies. Again, so you can see within Republic that we have businesses that almost replicate miniature version of the entire ecosystem, right? In the broader market, you have venture firms, you have PE firms, you have platforms, we have all of these things just so that we then can replicate and change and bring forth the entire financial ecosystem.

• 14:00Speaker 00

  And another element that you're bringing forward is secondary trading. You guys purchased Cedars, which is now Republic Europe, and you've also moved into secondary trading. We talk a lot about secondary shares here on Twist because, you know, there was the venture liquidity crisis. Everyone was desperate to get a little DPI. I'm curious, Kendrick, how big of a business is the secondary trading part of Republic today? Is it large? Is it small? I'm just curious relative to the rest of what you do.

• 14:25Speaker 02

  Yes, so today is still relatively small. That is, you mentioned Cedars, which is now Republic Europe. That's our UK European side that does both primary and secondary trading. For example, if you go on Republic Europe, you're gonna see Revolut, one of the largest private, you know, neo-bank out of Europe. You're gonna see shares of Revolut being actively traded among non-accredited investor on Republic Europe. In the US, the regulatory framework is a lot more complicated. That's why Carter failed at it. That's why Forge and Equity Day and Equity Zen only deal with, you know, high net worth investors. That's why Angelis,

• 15:07Speaker 02

  there's some secondaries. I mean, I think we did the first one when I was still there and structured it. But that's only for high net worth individuals. One has managed to do secondary trading for non accredited investor in the United States. A company that we are in the process of acquiring called INX. And INX is a very unique set of licenses as an ATS that has the ability to do so. So we're very excited to roll out at scale through secondary trading of private securities for non accredited investor sometime over the next six months or so.

• 15:45Speaker 00

  Kendrick, when I hear the acronym ATS, I think applicant tracking system. I presume that's not, that's not what it means. So can you define that acronym for us?

• 15:52Speaker 02

  It's automated trading system. So

• 15:55Speaker 00

  Ah, that makes way more sense.

• 15:56Speaker 02

  Notice, you know, an exchange, I think this is just a legal thing. For, for most people, there's no difference, but regulatory wise, you call yourself an exchange. You take on a certain regulatory obligations. If you call yourself an ATS, which is like an exchange light, then, then you're, you know, under a different regulatory framework.

• 16:17Speaker 00

  So one, I don't mean to be a spoil sport, but one thing that has always made me a little bit leery of secondary investing is a lack of information. And there seems to be a pretty big disparity at times between what primary investors get in a venture round and what secondary investors may get later on if they purchase those shares on Equity Forge or on Republic, et cetera, Equity Zen, I guess, or Forge Global. How do you fix that? How do you make sure there's enough information coming out from these companies to allow for a retail investor to take a position in Revolut, for example, without just gambling?

• 16:50Speaker 02

  Yeah. Alex, it's a great question. First of all, the same information disparity or the lack of, or the insufficiency of applies to venture capital, to the traditional private and public market. I'll give you an example. As the investment on AngelList, right? So you have a doctor coming to AngelList to look at a YC company. Information available literally fits on one page. There's not really a ton more information there. And of course, we trust that that doctor can make The investment sensibly. So about 10 years ago, the SEC and Congress already made a decision that hey,

• 17:33Speaker 02

  just because you're not a millionaire, it doesn't mean that you shouldn't be able to participate in the private market. In the same way that, you know, the information on Amazon, when you buy a product may be different than if you were to buy a product directly on the company's website. But you got to trust in the market and in the public to make sensible decision for themselves. Now, the law does add on a few things. You know, ATSs and exchanges and broker dealers have the obligation to do their own due diligence and make sure that they introduce things that are suitable for their customers. But I don't think that the public disclosure requirement for the

• 18:16Speaker 02

  public market to go IPO is some sort of a gold standard that lasts forever. I don't know about you, but I, even though I'm relatively informed and do have a, you know, meaningful public equity portfolio, I can't remember the last time I read a 10K or an 8K on SEC filing before making an investment in Tesla or Facebook. So at the end.

• 18:41Speaker 00

  Sure. But Kendrick, you're taking on, so one, I appreciate the clarity here. This is very useful. But it sounds like you, as a company, take on a lot of responsibility then to not bring trash essentially to your, your customers. So the due diligence is in, in some ways mediated by Republic.

• 19:01Speaker 02

  In a way. So our goal is to make sure that we have the legitimacy lens. And of course, we also apply an additional lens on suitability. But we don't aim, nor is it feasible to say that these are high quality deals and if you invest in hand and you're going to make money on night. That's not how the industry works. I do think that market maturation in the face ahead is through education, onboarding the investor base, and then present legitimate, that is non. Fraudulent, ideally companies that don't make misrepresentations and leave it to the general public to make that decision that if they have a thousand dollars to invest, they should invest,

• 19:46Speaker 02

  you know, fifteen or twenty dollars in fifty different companies and not putting a thousand dollars in one. So it's about education and access, not about some curative lens of any one particular firm or fund.

• 20:02Speaker 00

  Right. You make sure it's a suitable investment and not a clear fraud, but after that, it's up to the people to make their own choices. That's why that makes sense to me.

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• 21:21Speaker 02

  And there's a clear parallel to commerce and e-commerce as well, right? So like forty years ago, what people purchased was just basically what was produced and made locally. With the advent of e-commerce, Amazon one-click purchase, now we all buy things online with a magnitude of volume and choices. Many things

• 21:46Speaker 02

  We don't even need, and I think they've got to be the same lens that applies here, which is when it comes to commerce or investing, you make things accessible, you do require the company to post fair information, but you got to leave it to the general public to decide and participate in shaping the technological and the economy of the future that we are going to be living in.

• 22:11Speaker 00

  We could solve this, by the way, by just having greater disclosure requirements for private companies, but that's my own hobby horse. I won't bring that up today. Okay, now, Kendra, we have to talk about a couple of things here. So there is Republic Note, which is a quote revenue sharing digital security that allows you to benefit from the economic upside of select Republic portfolio companies. And you also have a tokenization business and a Web three console business. And my question before you kind of brought this up earlier was going to be, are you eventually going to have a single infrastructure layer that would underpin or undergird each kind of like pillar that we've described at the business? Because it seems like you're doing many things that all kind of point in the same direction. Is that where the company is going?

• 22:57Speaker 02

  For sure, Alex. It's already that way. So from the technology and legal framework is one system that is the, you know, let's say to do, for a company to be traded secondarily, you have to have primary issuance. When you deal with primary issuance, you deal with some are non-accredited, some are accredited, some are institutional, some are non-US. They require different regulatory accommodation and frameworks to bring them in. So we, over the years, all of these things are part of the same operating system

• 23:36Speaker 02

  where in the early days, we add on distinct business lines only to take in revenue. But the legal infrastructure and the technical infrastructure is one cohesive one that underpins a wide array of industry, again, to use, you know, poorly. Amazon as an analogy, once you have the e-payment and e-commerce infrastructure, you can sell books, you can sell grocery, you can do movie streaming, widely different sectors in exactly the same way. It's just that when it comes to finance, it takes a lot longer to build because it's much more regulated.

• 24:15Speaker 02

  But we are building the e-finance infrastructure similar to Amazon as an e-commerce infrastructure for that work.

• 24:23Speaker 00

  Kendrick, would a better analogy be you're building the AWS for investing? Like you're building a base level of like infrastructure to allow a variety of different things to happen on top of it?

• 24:33Speaker 02

  Yes, no, but there are different components. I think when I think of AWS, I think just like storage. In this one case, when you talk about investing, primary issuance, secondary trading on a cross-border basis, then you have to deal with banking, disbursement, all of these components that that that that that the power Wall Street now is being redone pieces at a time by Republic and a few other firms in the space. So yes, we're building one singular operating system, but we're not Elon. So we have to generate revenue and not just, you know, for 10 years. But but yes, we still in the very early days of about to launch.

• 25:17Speaker 02

  Nothing that we've done today is meant to be, you know, the business of Republic. They all did a version of what we're about to launch ahead.

• 25:26Speaker 00

  Kendrick, an absolute pleasure. Republic.com is the URL and we'll have you back on in six months or a year to see how far things are going. But I would not bet against you. Thanks for coming on.

• 25:36Speaker 02

  The pleasure is all my, thank you so much, Alex. I keep really looking forward to next time.

• 25:45Speaker 00

  All right, so we're going to need enormous data centers all over the world, the size of Manhattan, the size of Wyoming, they're going to eat all the power, take up all the space and drink up all the water, or maybe not. There are a couple of companies out there that are working on different types of chips that are going to reduce the overall electricity demands of our future AI world. I'm very excited about this because I'm a big AI bull and also I live in a state where we're trying to expand green power and it's pretty tough. So to learn more about these chips and the companies behind them, please join me in welcoming the CEO of Positron, Mr. Mitesh Agarwal. Mitesh, how you doing?

• 26:17Speaker 01

  I'm very good. Thank you so much for having me here. I'm really excited to chat, actually. And I do have a comment about the power, if you want.

• 26:24Speaker 00

  Oh, I'll never take an offer. I'll never not take an offer, so go for it.

• 26:28Speaker 01

  Well, one thing I want to clarify is as much as, and we'll go into Positron and how we are trying to be very power efficient. But I personally am a very big believer in we should generate as much power as we can. So there should be no restrictions on power generation. Just use that power that we're generating a lot better. So that's, that's the comment.

• 26:47Speaker 00

  So you're an all the above kind of guy. I presume small module reactors, solar, wind. Okay, for fun, what's the most exotic form of power generation you favor in the next 10 years? I have one.

• 26:59Speaker 01

  I have one too. Electricity generation on wave, like wave movements, ocean wave.

• 27:06Speaker 00

  Tidal power, essentially.

• 27:07Speaker 01

  Tidal power, exactly.

• 27:09Speaker 00

  Okay, there's a company called.

• 27:10Speaker 01

  Exotic, but that's.

• 27:12Speaker 00

  Well, no, no, I appreciate that. I think Exo Watt is a company that I've talked to and they're doing storing industrial heat via lensed solar power. And I was like, that, that's awesome.

• 27:24Speaker 01

  Okay, that's even more exotic. The company that, that popped in my head was Pantalassa, that is doing the tidal power thing, where they're deploying data centers in the middle of ocean and using the electricity.

• 27:36Speaker 00

  Okay, so free cooling, essentially, right? I mean, if you do data centers in space, it's harder to have to radiate it and all that business. But the oceans are big. Here's my concern, though, and we'll probably cut all this out of the episode, but who cares? If you put all the data centers in the ocean and we're dealing already with rising ocean temperatures, are we? Are we just slowly boiling all of the remaining ice in the broader ocean?

• 27:58Speaker 01

  The effect is very, very, very minimal compared to what the heat is. But yes, that is actually a really good question. I'm concerned because that will 100% come up in their investor deck. Luckily, I don't have to answer that.

• 28:09Speaker 00

  No, not at all. Okay, so Positron, let's talk about it. So Positron is a company that's building basically digital brains to run transformer architecture to power large language models as we understand them today. But to make people care about that, I think we need to talk about why GPUs are not always the best computing choice for AI workloads. And people will be surprised, Mateesh, because Nvidia is worth $80 trillion today, and everyone cannot get their hands on enough GPUs. So why are GPUs not the be all end all for AI compute workloads?

• 28:43Speaker 01

  Yeah, so the first thing I'll start with is GPUs are like, especially Nvidia GPUs, are the major, like over 90% deployment of compute today, right, across both training applications and inference applications. So training is when you're, you know, making the model learn and inference when you're generating, whatever you're generating, tokens, videos, images, right? And today, training is the majority of the compute spend. So it's still well over 65% of the compute spend. Inference is the 30, 35% remainder of the compute spend. Nvidia GPUs, super well defined and designed initially for shader processing back in like 10 years ago when AI was not as hot, but then really found out that for matrix, matrix multiplication, which is similarly in that realm, and which is exactly what is

• 29:25Speaker 01

  used for training application, absolutely phenomenal. You are generally flops bound, so the amount of transistors and compute bound. So the more you can push flops through, the more you can really do more training workloads and much more efficiently, more efficiently. And, and that's what Nvidia GPUs are phenomenal at, training applications. On inference side, though, inference is, is a much more nuanced or more nuanced beast in the terms of it depends not only just on the flops, but it depends on memory capacity and memory bandwidth. And at any given point of time, depending on how you're running the workloads, any of those three could be your bottlenecks. So Nvidia is really good at inference, but is it the most efficient at inference for certain types of workload?

• 30:08Speaker 01

  No, there, there can be always, you can always push the envelope just by existing silicon technology, whether that's by maximizing memory bandwidth or memory capacity on that. And that's where Positron steps in. This is where. We are coming in and we are saying for inference applications, especially the frontier inference applications, so really large, like really in multi-trillion parameter model sizes, Yeah. video generations, which require massive amounts of memory capacity and bandwidth. We can effectively really scale out our memory capacity and memory bandwidth through our architecture. And because of that, we can be very efficient at inference. The last thing I'll end here where the reason why Nvidia is not the most efficient at inference is because although Nvidia is using the latest cutting edge high bandwidth memory, what is

• 30:52Speaker 01

  called HPM in, you know, general terms for their memory thing, which is the fastest memory bandwidth on theoretical specs that is available. Unfortunately, when you run inference workflows on Nvidia, you are not able to fully utilize the available theoretical bandwidth.

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• 32:23Speaker 00

  That's where I wanted to go with this because I did talk to the guys over at Etched back in March and a really good chat, learned a lot. I would say their Probably your closest competitor, but they were really big on how Nvidia GPUs don't end up using all their compute power when running inference workloads. They were like, you know, thirty, forty percent efficient. I presume it's the other bottlenecks that come into play there that you just mentioned.

• 32:42Speaker 01

  Yes, correct. Exactly. So because you're not able to, because of the way the silicon and memory architecture is laid out and basically how the movement of bytes happen, Nvidia is not effectively really having the same ratio of memory movement to the amount of compute available. So they have a lot more specked out on compute rather than the memory movement. So they end up consuming less than what is efficient. And there are many software tricks that all of us try to do, whether that's spec decoding, flash attention, all those things. It can drive up the needle, but it goes up from twenty five percent to forty percent. It doesn't.

• 33:16Speaker 00

  Which is still terrible. Yeah, that's just super inefficient. Okay, so let's get to what you guys have built to start, which is the Atlas system. It has eight archer accelerator cards. It has up to two terabytes of system memory. It looks like a server rack. Tell me why it's awesome.

• 33:31Speaker 01

  Yeah. So the main reason why we really wanted to do the Atlas system was if you look at silicon companies, you know, any silicon company, any silicon company that over the last ten years, they've all gone down the route of, okay, we're going to design our ASIC or custom silicon and it takes them three, four, five years to get a product out in the market. What we did with Atlas system was we chose FPGA as our baseline product. We knew that Atlas has limitations in terms of specs, like it only has thirty two gigabyte of HBM memory or the number of flops on it is like one thirtieth of H one hundred GPU. But what we knew is that if we put, FPGAs are programmable gate arrays, like think of them as.

• 34:10Speaker 00

  Programmable gate array, if you will.

• 34:11Speaker 01

  Exactly. Yeah. So it's basically the best way I can simply put is like a C of transistors, right? And you can shape how you want to structure the C of transistor however you want it. So you can basically.

• 34:21Speaker 00

  It's the versatility that's so important in FPGAs.

• 34:24Speaker 01

  Yeah, exactly. You can inflict your.

• 34:26Speaker 00

  FPGAs.

• 34:27Speaker 01

  FPGAs, yeah. Onto your, inflict your hardware architecture on top of the FPGA and really show that as a proof of concept that your architecture is worth something to the world and it's, you know, it works basically. So that's what we did with Atlas is. The point was it allowed us to get from the out of the blocks quickly, get a product out in eighteen months from from the launch. And and get an Atlas system out, which is a typical four U server. So this is a very standard. Uh, four U system with eight GPUs. This has kind of been the standard so far until the GB three hundreds and two hundreds came about. In in the AI accelerator world and you can stack a rack of them. Uh, it's very, very energy efficient. You know, each server is only two kilowatts for comparison and H one hundred GPU, sorry, H one hundred. System is ten kilowatts a power.

• 35:11Speaker 01

  And what you can do is today with our Atlas system, and this is in production today, we're shipping it out. En masse in in in thousands of quantities. Basically, you can run. Transformer architecture workload, so all types of L L M's. Uh, and what we've shown with our architecture is we can drive the same amount of memory bandwidth that we were talking at thirty five, forty percent for Nvidia. We can drive over ninety three percent of available theoretical bandwidth uh and use that. So. Even with FPGAs, which are, as I said, limited cards in terms of specs, we can drive a a comparable performance to H one hundreds. And because we consume a lot less power and they are a lot less cheap, they're cheaper than H one hundreds, we can.

• 35:52Speaker 01

  Really say that we are performance per dollar and performance per watt. We are two and a half, three, three and a half X better than H one hundred, depending on each of the inference workflows.

• 36:01Speaker 00

  And is i- if you're curious why he's saying performance per watt also, it's because, as we talked about at the top of the show, we are often not just compute limited, but.

• 36:08Speaker 01

  Yes.

• 36:08Speaker 00

  Electricity limited. So both vectors really, really matter.

• 36:11Speaker 01

  Yeah.

• 36:11Speaker 00

  So I'm glad, I'm glad you brought up the the ASIC versus um. FPGA point, because I was curious why you started there. I know your next system is going to be ASIC based. But you said that it's a good proof of concept to show that your approach works. So essentially, the way that I'm reading them, but actually, correct me if I'm wrong.

• 36:28Speaker 01

  Yeah.

• 36:28Speaker 00

  Is that you had, you had an idea and you applied it via um FPGAs to start, because it's faster, it kind of quick and dirty, get it up, show that it works. And then you take the same overall concept, map that to an ASIC, takes longer, but you'll have higher, more performance technology down the road. But using the same overall principles that you proved out with Atlas and field programmable caterers. Is that, is that fair?

• 36:49Speaker 01

  That is very, very fair. And and the big part there is you get immediate customer feedback. So we have the systems deployed in production right now.

• 36:56Speaker 00

  Yes.

• 36:56Speaker 01

  So people are testing their workloads on us. We understand, like, so, for example, you know, attend, like, when Deep C came out, the attention mechanism changed from MHA to MHLA. We got that real time feedback. How does that impact our architecture? How our architecture performs for that performance rather than just doing some kind of emulation or simulation? You have real world performance feedback and and and numbers that you get. So that was a big part of of getting the product out quickly. And then the second part is, you know, you talk about like a lot of, we all of us hardware companies talk a lot about hardware. The other big kind of feature is software. You have to be really, really good at it. It influence the the the pressure of being CUDA compatible or or being very easy is a lot lesser than on the training side. On training side, you cannot, you know,

• 37:40Speaker 01

  anybody absolutely like, you know, you cannot be not in the CUDA ecosystem. On inference, it's lesser so, especially for bigger workloads. But still, you want to make it as easy where, you know, as long as you don't make people change their code, that should be your baseline kind of target is that you don't want to make people change their code. And this allows us to not only test that out real time, we can literally import that same stack over on on on to our ASIC. Because the fundamental elemental unit or the compute unit and the architecture stays the same. What you're really boosting on the custom silicon are the massive amount of specs because you get to go to the latest process node. You get to do real new memory architecture and technology and then you can really drive memory capacity and bandwidth up. So that's kind of our goal.

• 38:21Speaker 01

  And then lastly, I'll say to just one point add, it also helps with getting investments because you can really prove out your your chip works to to customers and and so on.

• 38:30Speaker 00

  It it also doesn't hurt that it drives a a good chunk of revenue. So Atlas as a bridge over to Asimov, which will be your ASIC system that comes out. Uh, I think you guys said two thousand twenty six. How much does Atlas cost? How many of them are you selling? And are they mostly like proof of concepts with customers that want Asimov and are waiting? Or are these people that really want to use Atlas for what it is today to run inference workloads?

• 38:52Speaker 01

  Yeah, so we we have now booked revenue. Oh, sorry. So when I said booked, it means it has not converted into revenue because we have to produce and ship it to them. But but we have now booked in tens of millions of dollars for Atlas Systems. Uh, so as you said, gives us real revenue. And and the big part here is that the people that are buying today are are basically two major things. One is they're buying because The system already performs and gives them enough of of a performance leverage for their use cases that they're thinking, okay, this is worthwhile investment to get my return on capital in, you know, eighteen, twenty four months. That's kind of the time period.

• 39:28Speaker 01

  And then the second thing is, you know, people want optionality, or people at least want to give a chance to non-Nvidia accelerators. And I think this is where they're like, okay, this also serves as a test for your company. Can you productionize a system? First thing. Second is your architecture worth anything? Like, you know, does it actually work? And does it actually scale out? And the way you predict and simulate and it and and it really drives. So what we call like, maybe we're using the term, maybe we are using the term wrongly, but like, we we call it the wrong socket strategy. It's like, hey, you you you buy Atlas, you you pay for, you know, we make money, we make revenue, we make profits.

• 40:05Speaker 01

  And and most importantly, with Atlas, we're not saying that we're going to scale out revenue to hundreds of millions or billions, right? I think it's it's it's unfair to expect that off of the Atlas system. But what we really want is the customer list that will spend that much money on the Asimov and Titan system. And that's kind of how we really approach it. So we call it the wrong socket kind of approach there.

• 40:28Speaker 00

  Well, no, I I really appreciate it. It's always good to see a company that has a vision, not just for their first product, but their second and their third. I mean, I mostly prepped on Atlas and Asimov Titan. I was like, that's going to come later. We'll talk about that next year. Um, but I I appreciate that. Now, the customer mix is interesting here because I'm curious if you guys are selling these into the the hyperscalers? Or if your early customer base for Atlas is more like, I don't know, companies that want to stand up their own inference stack because they're tired of paying someone else's margin on their compute needs.

• 40:55Speaker 01

  Yeah. So so the two public companies that we have, so so the two companies that we publicly announced are Cloudflare and Paracel. Uh, so Cloudflare is a content distribution network. Paracel is an inference service provider, right? So so kind of different use cases, but they both are looking at it from uh, cost effectiveness and with Cloudflare, power effectiveness, because they have these data centers in metropolitan cities, they can't, you know, supply more power to it or can't liquid cool it, right? So they need in air-cooled set them, how to drive more tokens from a given amount of power. So that's kind of where they get interested. In terms of other customer bases that we have not announced, it is a mix of a NeoCloud, a hyperscaler to your point, and, and, you know, we'll, we'll announce that in due time and in coming months.

• 41:39Speaker 00

  So Lambda or a CoreWeave and then in AWS or in Azure, just to put some names to the categories that you're describing. I'm not saying this is the actual customers.

• 41:47Speaker 01

  Correct.

• 41:47Speaker 00

  I'm just trying to explain to people, yes.

• 41:48Speaker 01

  Yeah, yeah, to the categories. And then also we are now starting to get some traction in the financial trading ecosystem, right? So that's kind of where, yeah, so those are the, the ecosystems that we are really going after. And again, the goal is to, to get them really excited about what it represents for Asimov. Because where we are going with Asimov is somewhere no other chip will be like. So if you draw on a map like, you know, okay, memory capacity, memory bandwidth and, and the computability, the big claim to fame for Asimov for us is it's going to have two terabytes of memory capacity. So just to give you a comparison, Asimov is launching end of 2026. Nvidia will be on the Ruben generation, which is the one after Blackwell.

• 42:31Speaker 01

  So that's the next generation that they will be around, starting to ship it out then. Ruben initially will have 384 gigabytes. So that's point three eight terabytes of, of memory will have two terabytes. So you're looking at a 5X memory capacity differential between Nvidia Ruben and, and Positron.

• 42:50Speaker 00

  Is, okay, at the top of the chat, you said currently AI workloads are like 65% training, 35% inference.

• 42:58Speaker 01

  Yes.

• 42:59Speaker 00

  I presume that was 9010 a few years back.

• 43:02Speaker 01

  Yeah, a couple of years back, yeah.

• 43:03Speaker 00

  So how long until it's 5050?

• 43:06Speaker 01

  So what I do know is what that rate of inference spend is building. So this year roughly $105 billion of compute spend, like not data center, not just the compute spend that goes in an inference data center. That will be spent on inference 105 billion. It's estimated that it'll reach 300 billion, so 250 billion in 2027 and roughly 350 to 400 billion in 2028. So that's kind of where it'll grow to the inference compute spend. The training spend is already well over, you know, 300. Three fifty billion today.

• 43:36Speaker 00

  Yeah.

• 43:37Speaker 01

  And and the thing is, if XAI keeps on increasing the colossus, if OpenAI keeps on actually spending the Stargate full on project and throw up expenses with trainium. I actually see the trainium as a training spend go up about a five hundred billion to six hundred billion mark by twenty twenty eight. So you're looking at a trillion compute spend and on train. So it might maintain that ratio of sixty forty for the foreseeable future or in that kind of territory. But there will be an inflection point someday. I don't know whether it's three years, five years, ten years, but but it will have that.

• 44:07Speaker 00

  That's very interesting because I listened to you talk. I was like, why isn't Nvidia trying, you know, getting off of their GPU train and having a second effort to just focus on inference? Because what you guys are building and what Edge is working on and a couple of other companies really seems to me like the right approach to handling inference compute. Because you don't need to have the same stack that you use for training precisely.

• 44:28Speaker 01

  Yeah.

• 44:29Speaker 00

  But if if the training market is going to stay the majority case for AI computer for the next couple of years, never mind. I take it all back. Nvidia is barking up the right tree. Uh, but you know, in ten years, though, we're not gonna, I presume, we'll spend a lot more on AI inference than AI training. And so I think that that long term, you guys are going to end up with the majority of the market. It just may be a little further out than I thought, I guess.

• 44:50Speaker 01

  Well, yeah, and like, look, two things. I I think I'm going to be grounded about this and and actually not even humble, just for the sake of being, I'm going to be very humble about it. Nvidia not only is the most valuable company in the world, they are one of the smartest companies in the world. It's not like they do not understand this differential between where the training workloads and inference workloads and efficiencies are, right? What they're saying is there is no option in the market today that can even compete with the GPUs on inference like, and they're absolutely spot on, right? Like, that is true, like that is that is actually a true statement today. And they're saying, so why divert our margin stack where they make seventy five percent margins on this this very high end cards? To like, you know, to create cheaper or or more efficient interest. I'm not saying that they're not trying to make it more efficient.

• 45:33Speaker 01

  And then the second part, which is where the humbleness comes in, is the proof is in the pudding. None of us or Ash or any other company for that matter, literally zero other companies have proven out a better. Cost efficiency setup than Nvidia today in the market, right? Like we can, we can claim for, for Atlas, for example, we can claim it for certain transformer models or certain types of transformer models. But if you look at the overall market, no one has come out and said, yeah, like, oh, everything, you know, we can beat Nvidia across the board because no one can. And, and even for the foreseeable future, that's not going to be the case. It's about finding the right niches, like for example, for Asimov, two terabyte of memory. What does that immediately get us? You can fit a frontier in models. So like Grok four is expected to be 2.4 trillion. That's what the rumor mill suggests.

• 46:17Speaker 01

  You can fit that on a single chip of our card, uh, uh, of Asimov. Whereas you need like four or five GPUs to fit that weights of the model. Or video generation, today, video generation is limited to like eight second or 10 second or 20 second clips. With that much memory, now you can generate a contiguous video to a minute, two minute, you know, even multiple minutes long. So, so you have to find your use cases where you stand out against Nvidia and go for that. And this is also why Nvidia wouldn't be like, hey, we're going to create a single chip for every application. Because why are they going to sacrifice the margins for, for, for their?

• 46:48Speaker 00

  But the, the cool thing is because Nvidia is going after what is currently the majority case and the high margin case and the proven out case and the no. They're going to make a lot of money, but they're going to leave some gaps that you can step in.

• 46:57Speaker 01

  Absolutely. Yes.

• 46:58Speaker 00

  So I want to ask about that because, uh, for fun, I looked up Nvidia's R and D spend for the last quarter and it was 4.3 billion dollars. Uh, your last round was about 50 million. So about like, I don't know, 27 minutes of Nvidia's R and D spend. Uh, I know companies like Valor are backing you, but I'm curious. Are you guys going to need an order of magnitude more money to pursue the Asimov vision and then putting them together to make Titan? Or is it going to be more capital efficient than Nvidia's R and D spend might lead me to think to get you through your 2026, 2027 ish roadmap?

• 47:31Speaker 01

  Yeah, two things on that. First, an internal kind of introspection. We are very capital efficient. So we got our Atlas out with only few single digit millions of money raised. And, uh, and with Asimov, actually, and, and look, Valor is, is a well-known fund for their diligence. They, they looked at our kind of capital spending plan. They like capital efficient companies. Uh, and, uh, you know, when they. Looked at it and they saw that with the series A, we could tape out our azimuth chip. So you are right, generally or traditionally, if you look at silicon companies, they have raised three hundred, four hundred million dollars to tape out a single chip. We are going into market with a total raise so far of seventy five million dollars, including our seed and series A.

• 48:11Speaker 01

  And we are saying that we're going to tape out uh our first generation ship in that seventy five to eighty million dollar uh kind of range. And then to your point, after that, when it comes to production scaling, it doesn't mean that we are done raising. I I I would be lying to you if I sit here that's saying that, hey, like, we're not planning on raising more. We absolutely will raise more. Uh but I think that will come for scaling off our azimuth into tightened systems, as as you said, and and really going out to market. But, you know, we don't like personally, we have a very strong personally, both my strong belief and and and the company, we have a very strong belief that, you know, you're only showing market validity and valuableness if you are both uh doing that at a at with a very huge amount of capital efficiency and

• 48:54Speaker 01

  all and your chip is only worth it if the market wants to pay for it. What I mean by that is what we really want is third parties and, you know, hyperscalers and cloud providers and companies buying our chips. We, what we don't want to do is like, just like, hey, build out our own cloud and then sell token as a service, right? Because that's kind of where the economics values, like whether the companies are economically profitable or not, it's not clear. So we really like, we want to do what Nvidia AMD does, which is sell to third parties. Uh really? And then that's kind of where the vision goes. And if you're selling to third parties, which means you're making revenue, which means that profit can uh fund your pre uh future tape outs. Uh so that's the introspection side of things. With Nvidia's, yes, uh with Nvidia's four point three billion number, sorry, just quickly addressing that.

• 49:38Speaker 01

  Obviously, they're not just spending that on on their silicon. They Nvidia Nvidia is a company that is just even, you know, it's not just a silicon company. I I know that two hundred billion dollars worth of their revenue is driven by silicon, but they're spending R and D on robotics, on on networking. Actually, one of the key things-

• 49:54Speaker 00

  Self-driving with their Thor system, et cetera. Yeah.

• 49:57Speaker 01

  Yeah. And and like, one of the things that I was like, I genuinely think where their R and D expense has has been really worth it is looking at- Their at least projection of their optical networking and and what they have the CPO, the the the that they have projected at the GTC. It was really phenomenal to see what they're uh kind of scratching on the surface there, right? So they spend the money on a lot of things. So actually, again, I come back to it. I'm I'm very both, obviously, like, I'm very bullish about Positron, what it can achieve from market share perspective. Because the market is like going to be 300 billion. You know, we're going to achieve a big part of that influence market. Obviously, that's the goal. That's what we're trying for. Uh, but I also see kind of where Nvidia is spending R and D on different areas of domains of market. It's not just training. It's not just inference. You know, you talk to a self-driving robotics, other things as well.

• 50:42Speaker 00

  I guess what I'm trying to drive at is like, is there enough private capital in the market that is available to companies like Positron? To do the work you need to do to earn your spot in one of these niches that we're describing? Like, does does Valor and Friends have enough gumption to give you the capital you need when you need it? Because we, there has been a little bit of a sentiment shift about AI in the last couple of months. I think GPT five, I like GPT five.

• 51:06Speaker 01

  Me too, I love it.

• 51:08Speaker 00

  Less impressed. Uh, and so it makes me wonder about like how how open the wallets are as we look into 2026.

• 51:14Speaker 01

  Yeah, I think uh the interesting thing is like, you know, obviously, if you if if I speak about Valor, like I have to give them kudos. And it's the same thing with Valor and Atreides, you know, Antonio Gavin and and these guys. They backed unfashionable thesis early with us, right? You know, like, you know, you know, it the fashionable thing is obviously Nvidia is going to keep on being the 95% of the market or 92% of the market. But even if they are, the market is growing so rapidly that the rest of the market is still pretty large. Uh, but the the second thing that I will say is the answer is absolutely unequivocally yes. Like, you know, with even just like, you know, like, if you look at Valor and Atreides and and DFJ, who are backers for Series A. Generally, these are growth funds, right? Like, they they generally back companies in Series B, Series C, like in your raising growth rounds.

• 51:58Speaker 01

  They came in early into Positron. Uh, and and that's not because, you know, uh, they just fashioned one day that we want to be early-stage investors. I think it's it's because they basically decided that, hey, this company, if it actually proves and and delivers and executes on what they're showing and promising. Is actually going to be on a very fast growth curve and you're going to be there to back them up. Because again, that's where their actually main capital comes in is like especially valor. If you if you look at Antonio's thesis, right? He he he doubles down every single time on a company wants to show execution and success. So so that's that's that's we have no doubts about that, both on our ability to to prove it out to the market.

• 52:39Speaker 01

  And and that there's capital available for us to keep on growing and and really finding a very, very good revenue stream for us.

• 52:47Speaker 00

  Okay, one last question before I let you go. And it's just about the the durability of transformer based AI. Like you guys clearly have made a directional bet on transformers staying at the forefront of AI progress. And as I alluded to a second ago, some people are a little bit worried about training walls or, you know, token prices not coming down as we burn more of them in reasoning models. And there was that MIT study that everyone, I think, kind of misread, but took to the fences. So tell me a little bit about your just your and Positron's confidence in transformers staying at the heart of AI. And hopefully as well that there's still good performance gains to be had out of transformer based L L M's in the future.

• 53:26Speaker 01

  Yeah, can I say one thing to to begin with in terms of clarification there like? We are fundamentally a linear algebra accelerator, right? What we are accelerating is the matrix multiplication and and so and, you know, if you if you look at back thirty years ago, and I think if you look forward thirty years ago. The fundamentals of deep learning will be in the matrix multiplication. So linear algebra will still be the driver of the different whether it's transformer architecture, whether it was CNN and whether it will be whatever the future architecture of the world is like.

• 53:55Speaker 00

  I would. Are you working towards saying that I am being overly specific by saying transformer, but really what I'm talking about is just matrix multiplication?

• 54:02Speaker 01

  You are being I'm saying you are being overly specific that our our marketing word is yes, we focus on transformers because that's where the use cases are. And that's where we actually really stand out as well. But if tomorrow something changes, like, you know, a new model architecture comes out for us, we are not actually specifically tied to transformer. What we can do, we we are a. Just like GPUs can adopt a new architecture, we absolutely can adopt to the new architecture. It'll just be we have to, we'll have to spend more time on instruction sets and, and you know,

• 54:33Speaker 00

  But that's a software point, not a hardware point.

• 54:36Speaker 01

  Correct, yeah, from, from a, from an architectural perspective, we can actually serve much more wider. Like we, even today with our ASMR and with our FPGAs, we can serve diffusion, we can serve other types of models. It's we have focus on transformer because that's where, you know, over ninety percent of generation or inference use cases today lies. And also that's where people are really deploying it. So from our side, we're, we're making some decisions to optimize for it. Like for example, transform, big transformer models require lots of memory. So we are going and chasing after having a lot of memory on our chip rather than.

• 55:07Speaker 00

  So you can run those two, two trillion parameter models for, yeah, exactly.

• 55:11Speaker 01

  On a single chip, exactly, right? But, but from an architectural perspective, we can actually run much more broader ecosystem of models for inference, not training.

• 55:20Speaker 00

  Well, um, normally I, I end these chats with, uh, well, thanks for coming on. Where can people find you online and what's your job you're hiring for that you're having a hard time filling? But instead I'm going to ask you this, can I give you five dollars in your next round?

• 55:33Speaker 01

  Absolutely, yes.

• 55:34Speaker 00

  To be clear, I'm kidding, I'm just, I'm, I'm very impressed and I'm also, I'm also very, I'm still a big AI bull.

• 55:40Speaker 01

  Yeah.

• 55:41Speaker 00

  And I think that when I think about the ASMR of, you know, system being put into like groups of eight and soulless titans. It's all, it just strikes me that we're going to have a future that's going to be a little bit more efficient than people think. We're not going to boil all the oceans, we're still going to have power for our appliances. And we're going to have essentially infinite intelligence in our pockets and all of our screens. And to me, that's a pretty exciting future, so.

• 56:04Speaker 01

  And we better, we, we will need to have infinite intelligence and that's the whole goal. Like if you ask Nvidia, if you ask us, if you ask any other silicon company, I think they're all trying to say that, you know, we are, we are approaching it with some different approaches, but basically it's like, how can we bring like cost per token or cost per generation down so that we can afford to have infinite intelligence? While increasing our energy, energy production as much as we can.

• 56:28Speaker 00

  Well, I'm totally here for it. I appreciate it so much. It's positron.ai if people want to go take a look at it.

• 56:33Speaker 01

  Yes, please.

• 56:34Speaker 00

  When Asimov comes out, will you come back on and tell us how it's going?

• 56:37Speaker 01

  I absolutely, I would be the happiest person to come back on because that would actually put us into an untouched territory. As I said, in terms of a couple of the specs that you're targeting there.

• 56:47Speaker 00

  Late 2026, you said, right?

• 56:49Speaker 01

  Correct. We are taping out Q3 end of 2026 and then production. So initial sample systems will be late 2026 and production scaling in early 2027. That's kind of the schedule.

• 57:00Speaker 00

  Well, that means you have quite a lot of homework to do. I'll see you in 365 days.

• 57:04Speaker 01

  Thank you so much. My pleasure. Thank you so much for having me.