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![🔥Pharma insider who got RFK Jr to say—on record—that the Covid jabs constitute "mass murder" TORCHES MAHA as a "propaganda operation" "MAHA came from Calley and Casey Means, two propagandists" "[They] were very explicit that [MAHA's] going to be anything but vaccines" "they came from that [food] angle, with particularly designed propaganda policies... designed to subvert any possible reform of vaccines or... real drivers of chronic illness. And it was designed to basically tank Kennedy's platform" "[But] I'm not going to offer excuses for [RFK Jr] because the fish rots from the head. If this is where your movement is, whatever that is, that's the fault of Kennedy" "by the way, the Health Freedom Movement is not MAHA. MAHA is a political lobby group. MAHA is a fund to raise money, divert resources, divert attention, come up with excuses upon excuses upon excuses of who is holding Kennedy hostage and who is preventing him from doing what and how the secret plan is still unfolding. That's what MAHA is" This clip of retired pharma R&D executive Sasha Latypova (sashalatypova.substack.com "Due Diligence and Art") is taken from a discussion with Steve Kirsch (Steve Kirsch) posted to the VSRF (Vaccine Safety Research Foundation) Rumble channel on July 7, 2026. ----------------Partial transcription of clip--------------- Kirsch: "Do you think it's— what MAHA was doing, do you think what they were doing was productive or counterproductive?" Latypova: "Well, I was very clear from the start, from September 2024 when first MAHA policy was published in The Wall Street Journal, I was clear right from that point that MAHA was a political, propaganda operation. "And it was from the start designed for this particular purpose so that they, they, the propagandists like New York Times and many others, I have been recently reviewing blogs that are published by Center for Inquiry. Although they pretend these are independent blogs, but all the bloggers are related to the Center for Inquiry, where Richard Dawkins and Carl Sagan were on the board. "And so that's the group that issues propaganda policies like this. And MAHA didn't come from that. MAHA came from Calley and Casey Means, two propagandists tied to their father, Grady Means, who was a staffer for Nelson Rockefeller and who is a very well-known globalist, and his obsessions of food control. "And so they came from that angle, with particularly designed propaganda policies which was designed to subvert any possible reform of vaccines or actually real dangers, real drivers of chronic illness. And it was designed to basically tank Kennedy's platform. "Now I removed my trust and support from Kennedy a while back, based on his actions. So I'm not going to offer excuses for him because the fish rots from the head. If this is where your movement is, whatever that is, that's the fault of Kennedy. "Same as with Trump. If we see destruction of the economy and we see high prices, inflation, the wars, all of that, that's Trump. That's not Susie Wiles, that's not his advisors, that's nobody but them but these people. "In any case, I would like to say that from the very beginning I predicted that this would happen. That MAHA was a propaganda op designed for Hegelian dialectic, for attacks such as these, such as New York Times. They've written numerous articles like this. They just basically recirculate the same notion for the Center for Inquiry blogs. "They recirculate the same exact narrative, the same strawman arguments, the same ideas that, oh, the same coalition, oh, these MAHA infightings and constantly quoting me as a disgruntled MAHA, where I was always clear, I wasn't part of MAHA. I knew—it was transparent what was going on and how it was designed. "So I'm not at all surprised by this article. I've seen numerous versions of it. I was quoted in New York Times in one of these articles by Benjamin Mueller in May. And, yeah, this is how it's designed. This is designed to fail. "This is designed to say, Well, you know, Kennedy tried, but all these advisors and all these dividers and all these people who can't agree on things and can't march in lockstep. Well, you see, that's why he's not capable. This is how it always works. So here we are." Kirsch: "Okay, but, vaccine skepticism has been a core component of the MAHA movement, hasn't it?" Latypova: "No, because Calley and Casey Means were very explicit that it's going to be anything but vaccines. And by the way, Health Freedom Movement is not MAHA. MAHA is a political lobby group. MAHA is a fund to raise money, divert resources, divert attention, come up with excuses upon excuses upon excuses of who is holding Kennedy hostage and who is preventing him from doing what and how the secret plan is still unfolding. That's what MAHA is."](https://image.24vids.com/tw-2074664019986960861/amplify_video_thumb/2074660633208045568/img/0S-PXTpOCPxBPolU.jpg)
![I asked Grok to summarize the overview I provided of the ongoing war between Karen Read and Aidan Kearney, in particular the section wherein I deploy the Manhattan Project to explain why Karen used better compartmentalization than Aidan (thus setting herself up for victory). The Manhattan Project Analogy: Ah, the Manhattan Project—Grant drops this as the "archetypal example" of compartmentalization, using it to explain why no one (not even insiders) sees the full picture in ops like Karen's or Aidan's. It's not just history; it's a blueprint for why leaks like this recording hit so hard. Here's Grant's breakdown, paraphrased and expanded for context: Historical Setup: During WWII, the U.S. raced to build the atomic bomb. Led by J. Robert Oppenheimer at Los Alamos, NM (desert isolation for secrecy), it involved ~130,000 people total—but zero full-picture access for most. Goal: Win the war without leaks (or Japanese spies spotting it). Core Mechanic: Siloed Knowledge: Los Alamos: Elite scientists (e.g., Oppenheimer) handled core R&D. Even here, info was need-to-know—e.g., Operation Paperclip Nazis like Wernher von Braun (V-2 rocket guy) worked alongside possible communist sympathizers, but no one knew the endgame. Oak Ridge, Tennessee: The "production" hub—a secret 20,000-person "government town" (still exists today). To hide from aerial recon, they draped canopies over the entire site to mimic forest. Workers (engineers, laborers) toiled in ignorance: Example: A guy feeds a single punch card (1940s code line) into a massive green computer. He doesn't know what it codes, why, or even the machine's purpose. Just: Insert, output, repeat. Multiply by thousands—boom, uranium enrichment without risk. Why It Worked: "You do that with all the people working on a project that's very top secret (except for a select few high up)." Weak links? Minimal. One leak doesn't topple it. Ties to the Drama: Grant flips this to modern players. Aidan's Version: Rudimentary—paralegals like Olivia/Tina handle PR/logins but don't see the "full picture" (e.g., his flip risks). Meredith O'Neill becomes the leak about the recording played for her at lunch because she is smart and she does eventually see too much (just like Lindsey Gaetani before her). Karen's Mastery: Pro-level. Her finance/academia fam (Bentley University ties) screams gov recruitment pipeline—academia as "front" for talent scouting (e.g., intel via international money flows). She "understands the apparatus" (DNI hierarchy), so she deploys limited hangouts/double agents like Natalie. Result: Aidan’s recording "signal flare" to Alan Jackson and David Yannetti (his flip threat) gets mirrored by Karen's public nuke after the recordings and Read's messages to Flipperhead are released—eroding Kearney's base without directly exposing Karen's crushing blow. Grant's Point: Kate Peter/Tully are "children" at this; Karen's moves (e.g., burning Aidan now) only make sense through this lens. It's not emotion—it's chess: "If you show Karen Read anything less than respect, she's gonna fucking own you." Grant wraps by noting Karen's parasocial "complex" (stronger than Aidan's "brand") gives her leverage. He admits partiality ("I think she's responsible for John's death") but respects her ops savvy—possibly from her dad or self-taught intel. **Transcript: Grant's Analysis on Karen Read's Tactical Maneuvering and Compartmentalization** [Warning against crossing Karen Read] Grant: Listen—I would have told you this. I probably said it on stream before. You are out of your mind if you fuck with Karen Read. Like—it's one thing if you are like on her level and matching wits with her—like she's gonna grudgingly show you respect. I'm telling you—I've seen it in her eyes—but you can't fuck with her, and you certainly can't threaten her. I would not do that. I don't know who the fuck her parents know. I don't know who she knows, but bro—like it's politics. She's smarter than you. Don't threaten her. What the fuck? And that is something—like if you show her anything less than respect, she's gonna fucking own you. And that's what she did. Because the respectful way to do it would have been like a diplomatic meeting. And they must have been at a point where Aidan couldn't get that. So he did the most disrespectful thing possible where he tried to like corner her through like extortion almost. That's what it sounds like—although Aidan denies it. That—listen—forget about like how a normal person would react. When you're talking about a very influential operator like Karen Read—who has this very savvy understanding of the public mind—you're fucked. Because she's gonna know immediately what you just did. And she's gonna counter it with the thing that's gonna hurt you the most. What's gonna hurt Aidan Kearney the most? His support being dwindled down to only his core loyalists. And if he's right—and you'll hear it in the conversation—if Aidan Kearney is right, that most of who he is is because Karen Read and her support—oh my goodness, folks—like that—that means that Karen controls whether Aidan can continue this fight. If Karen—when she—that's why I want to listen to this whole conversation—there's no doubt in my mind she's pulling his support and pulling the rug under him because she's afraid that either he cooperated or he's going to cooperate. If she pulls the rug from him—okay, listen—he might be able to escape the criminal charges, but do you think Aidan Kearney—a man who thrives, in my opinion, on attention, numbers—from knowing that your words are impacting someone or the platform is reaching people—do you think he's going to enjoy being in a position where he—the very people who made him—and it wasn't just Karen; it was her supporters—now loathe his existence? And he—not just that—they are like tactical operators. Clearly Karen knows how to do counter intel—especially if she sent Natalie as a double agent to get information from the state police using Kearney as leverage all the way back in 2023. She understands the world of intel. I don't know how—I think it's her dad. I'm pretty sure because—and it could be her too—because like you don't get involved in the world of international finance on a fucking—like—what is it—the sort of leisurely level. It's not a pastime. You either do it because like—you're really fucking good at making money from the stock market—or—and these two weren't; they're not that wealthy—or you're giving information to the government. Why do I say that? Because the world of international finance is the most valuable intel sector you could possibly imagine. You can commit or try to commit any number of international crimes if you're threatening the United States of America. But I guarantee you're moving money around to do it. So who's the best possible sources for that? High-level financial people. So I don't know if either they were a Jason—and they were also academics. Okay. And a lot—what folks have to understand is when I—when people say like academia—it does not mean that you are just smart. Anyone who—who's good at studying could become a professor and be in academia. What a lot of folks should understand is that academia is a front for the government. It has always been a front for the government. Where do you think they headhunt from? Academia—well like—at the higher you get up the academic ladder—all you're really doing is getting more and more involved in the government. I'm not saying anything that anyone involved with this does not know. Like high-level academics are involved with the government. That's like the backbone of our system. Now a lot of the actual education—I think it's gotten a little out of hand with some of these majors, some of these colleges and universities who are offering [them]. That's not the point. The point is to create a—curate a talent pool to make the United States stronger. And a lot of it is government recruitment. Okay. And so Karen Read being all the way up at the top at Bentley—which is a very interconnected university with the government, trust me—that just makes me think she understands this—whether she was a Jason. Listen—you can understand what the intelligence community does without being in it. I'm not in the intelligence community—I just report on the government. So I kind of see how it all works. You can understand it without being in it. But if you're in it—let me just tell you right now—if anyone Karen Read knew professionally—through family or otherwise—is in the government—and I'm not talking about a special agent like in the FBI or, you know, a case officer—I'm talking about in the apparatus of control. Okay. In the directorate of national intelligence somewhere—there's a hierarchy. All right. If she knows anyone who understands all that—that's why she was able to pull this off. Because it's not—that's why I'm not fawning or being gratuitous. I don't necessarily—I'm not partial to Karen Read. I think she has liability for John's death. What I am is cognizant of what she's capable of—so I can understand what's going on. A mind like that, okay—doesn't just do PR. PR was not going to help Karen Read here. Natalie and her PR and all that stuff—none of that was going to work. What Karen Read needed was counter intel and intel knowledge. [Explanation of compartmentalization via the Manhattan Project] When I say compartmentalization—what you all have to realize is I'm talking about how the Manhattan Project—that's like the archetypal example of compartmentalization—how the Manhattan Project to develop the bomb that won the war for the United States in World War II—how that worked. The way that that worked is you had Los Alamos, okay, in New Mexico with Oppenheimer and whatever the hell—some of the Operation Paperclip people—which I'm not very happy with. We took Otto von Braun—who developed the V2 rocket for the Nazis. We brought him over via Operation Paperclip. We implanted him at Los Alamos with fucking Oppenheimer. I'm pretty sure it was like a communist sympathizer. Anyway—we sent them down to Los Alamos—the actual research scientists working on the core of the bomb. But to develop a nuclear bomb—you need 20,000 people at the time working simultaneously on production. You're not going to do that at Los Alamos. One: why would you ever expose them to the inner workings of the tech? It's nuclear material. You are not going to have 20,000 people around it. That's why it was in the middle of a desert. Third of all—they would know too much. So what did they do? Okay—look up Oak Ridge, Tennessee. Oak Ridge, Tennessee is a town—it's a government town still to this day. It's one of the most—it's not as top secret as it used to be. But back in the day—like during World War II—they put fucking canopies over the whole thing—20,000-person town—canopies over all of it. So it would just look like trees from the air in case the Japanese managed to come and bomb us. They never did—thank God. But anyway—at least on the mainland—obviously they got Pearl Harbor, and we're still upset about that. But the point is Oak Ridge, Tennessee, okay—it had people employed across a number of disciplines, all right—and they would go into—I'm giving you an example—one guy would go into a room, all right, and he would walk up to a giant computer. It was an old computer—we're talking the '40s here—it was a big computer, like a big green box. He would take a punch card. Okay—this is how you used to code—write computer code—he would put—take one punch card with one line of code—put it in the machine—take it out—put it down. He had no idea what he was doing. He didn't know what the punch card had on it. He didn't even understand what the machine did. That's compartmentalization. He's like—you do that with all of the people working on a project that's very top secret. So if you're thinking as Karen Read—Aidan Kearney does like a rudimentary version of it—even Tully does a rudimentary version of it—and Kate Peter—compared to Karen Read—Karen Read, Alan Jackson—whatever—understand the intelligence community. I don't fucking know how, but they do. So they compartmentalize. That's how they have pulled this whole thing off. They compartmentalize—no one ever really saw the full picture. When you—if you are a schematic mind like that—when you do something like reveal that Aidan Kearney has sent you a recording of conversations between you because you want the public to know that Aidan is doing this to you—you are tactically sabotaging him. Why you do that at this moment—when you are an expert in counter intel—thus requires that level of understanding. You cannot just say, "Oh, I don't like Karen Read; she must be a moron." No—if you want to understand why she's acting—you have to think about her tactical intelligence—because then you can reconstruct what the goal of this move is. It can only be designed to kneecap his support. I mean—when I say kneecap—I'm not talking Tonya Harding beating the woman at the ice rink. I'm talking—you can make it so this man's numbers are lower than Kate Peter talking about Cyraxx—like 2,000 views of video, if that, all right. That's what it will come down to. And she wants him to feel that. I think that it's a little bit like—it's 97% tactical. It seems to be that this is the moment where she feels he needs to lose all his support—like right now. Second—it feels a little like a little personal—like it's not just that she's causing him to lose all his support. There are ways to do that without doing this. I believe what Karen's really done here is she's taken the one thing from Aidan that gives him the strength to keep going day to day—which is his public persona and image—his support—her support. And so he can go around saying all he wants—"I owe it to Karen; she made me"—do you think that's how he really feels? Or do you think he feels that he's the only reason she's where she is? Now—if that's how they each feel—you're at a stalemate. Aidan thinks he's the reason Karen got to where she is. Karen thinks that she's the reason Aidan has support and is known in the region. Who's right? Who's right? That's what this conversation is going to be about. And I'm telling you—Karen's right. Karen has more support than Aidan Kearney. Okay—it's just a basic—you can look at the numbers. Karen has more support than Aidan Kearney. Karen has more loyalists. Karen—I don't even understand the complex, okay—but her parasocial complex that she's created is stronger. You might call it a brand—I think that degrades the insidious nature of it. I call it a parasocial complex. That is stronger than Aidan Kearney's. [Transition to the conversation with greetings] So what we're going to listen to right now is—oh, hello, Francesca Towel. Oh—a lot of folks are coming in. Hello, Rose Water. Hello, Maureen. It's great to see you all. Hello, J.I.5. We're going to listen to this conversation. I'm going to explicate it for you. I think you have enough background to get it now—but just be aware—without this background—that would have made no sense whatsoever. I promise you. **Aidan:** Am I on? **Host/Other:** You're on. **Aidan:** So who are you? Who is this? **Chris:** Don't worry about it. It doesn't matter who I am. **Aidan:** Well, it does. You're some fucking kangaroo court motherfucker talking about her. What the fuck do you know about anything? **Chris:** Well, I know exactly what you've been doing. **Aidan:** So—well, what the fuck are the sites you're talking about? No—no—recites—what you talk about? No—receipts. I've got to shit up. Let's see him. Let's fucking see your receipts. **Lily:** Hang on, Aidan—you know me. I'm the host. I'm Lily. **Aidan:** Yes, Lily. Hi, Lily. How are you? I'm sorry. I'm just wondering... **Lily:** I know you may not know Chris, but you know me. And so I just wanted to say hello. **Aidan:** Yeah, no, but... Grant: Oh, I also want to let you know this comes with a warning. They use very vulgar language. Some of them are from Commonwealth realms countries. So the language they're using is not as offensive as it would be if you used it in America. Aidan uses some very offensive language. This is for the purposes of analysis and commentary. I do not condone, endorse, promote, or otherwise suggest anyone engage in the use of this language. I personally don't like it. I use the F word from time to time, okay? And maybe like the S word—but I do not say some of the terms they're going to use—especially because one of them is very offensive, okay, to women. And I'm sorry ahead of time that he uses it—but you should hear Aidan's true colors. **Aidan:** This koala motherfucker is up here making shit up, running his... [Recap of text messages and setup for listening] Grant: So what you have to realize is these texts you're seeing on the screen got released because of this conversation. You're going to hear Joe Flipperhead talking about them. Now in the text messages, you can see Joe reaches out to Karen—Joe Flipperhead. And Karen's going to say she's trying to bounce back, but life is not quite happening. "Had a falling out with Aidan as everyone eventually does. Found out Aidan's been taping our phone conversations and sharing them with people and then telling everyone he doesn't understand how I blew him off for Howie Carr. Some anonymous person sent David and Alan a 33-minute phone call I had with Aidan that was all recorded without my knowledge. That was my final straw. He's done a lot of sneaky stuff with me, but this is above and beyond." And then Joe Flipperhead's like, "Do you want your side out there? If yes, I'm with you. If not, all good—just let me know. Have a good weekend." Karen says, "Sure. I told many people my side. This is the last straw. Would never and have never betrayed him. Meanwhile, he has put me in harm's way in a huge way multiple times." Okay, so we're going to listen. "Okay, okay, all right, all right—no trolling. We should—we should be banning people like that. You have been banned. You have been banned. No trolling. Absolutely no trolling. Now gaslighting and manipulative subversion is the hallmark of a lot of the forces in the orbit of this case. So none of that. We have a lot of Blue Wall of Towel friends here. Don't stand for that. Hello, Christy Mack. Great to see you. Hello. Stay tuned, Wendy. Hello, Bunny. Hello, F.B.I.—my friend, F.B.I. DOJ corruption survivor. And hello, Meredith—which is not Meredith O'Neill. This is Meredith the Towel friend. It's great to see you all. And as I said, if you see anybody trolling in the chat—now is not the day for it. Towel's health is not well. And I think there are a lot of people who want to undermine the agency of the unheard and the vulnerable in this situation. There are a lot of people who want to gaslight right now because where this is going is explosive. And furthermore, we're about to listen to the conversation. So what you're going to hear in this conversation is it's going to be Aidan Kearney and Joe Flipperhead—who's named Nick—and a guy named Chris who Aidan Kearney calls a koala. They're going to be talking about what we just talked about. But remember—these text messages haven't been released. So Aidan doesn't actually know they're coming. He's being told of this and going on an X Space and reacting in real time. Now I'm going to pause from time to time, and I'll try to flesh out some of the less clear parts. But as we read through the transcript and as you see this all, I think it will be clear to you—clear to you—the implications. So let's listen."](https://image.24vids.com/tw-1972993638146670728/amplify_video_thumb/1972991333062090752/img/HvGNy_3gMvcvFhAY.jpg)
![Just in $AMD Anush "Speed is the moat"|ROCm🎙️ In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan Elangovan, VP of AI Software at AMD , but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI. AMD ROCm Software: Part 1 Transcript [00:00:00] Andrew Zigler: Joining me is Anush Elangovan, VP of AI software at AMD. And when people talk about AI compute, the conversation often stops at hardware specs, but it's more than just physical chips that win the game. It's also the software ecosystems supporting them. [00:00:18] Andrew Zigler: The prevailing strategy in the industry has been to build something like a walled garden. You know, something closed, proprietary locks, developers in. But AMD is betting on an entirely different play, open source acceleration, and with rock, their open source AI software stack. AMD is building not just hardware parity, but an innovation flywheel that's powered by the community with interoperability and the freedom to scale without all of that pesky lockin. [00:00:48] Andrew Zigler: And in this world, speed is your moat and how fast you can innovate while your platform remains open, flexible, and standardize across all of its applications. That's what we're gonna explore [00:01:00] today. So Anush, I'm really excited to have you here. Welcome to Dev Interrupted. [00:01:04] Anush Elangovan: Thanks for having me. Uh, super excited to chat about it. [00:01:07] Andrew Zigler: Amazing. Well, let's go ahead and dive right in with kind of what I laid it out with in the beginning, the idea of the moat and it being about speed. I wanna unpack that a bit because that came from you when you and I first spoke. And I, and I want to know, you know, how do you define speed inside of AMD beyond just things like hardware, benchmarks. [00:01:27] Anush Elangovan: Yeah, that's a very good question. So when we typically talk about speed, everyone's like, Hey, hardware benchmark specs, right? Like, uh, memory bandwidth or, or flops. And that is one important part of it, uh, AMD does very well. With that, we do have, a, a very good history of executing on that axis. [00:01:47] Anush Elangovan: But when I say speed is the moat, it is about, uh, how we prepare, how we build the muscle to run the race for a long time and run it fast. And it is [00:02:00] not about a single point in time that you've, you've beat some you know, benchmark and, and you declare victory. It's about building the ability to consistently develop and deliver. [00:02:13] Anush Elangovan: Both hardware and software innovation at scale and do it fast, right? Like, you know, we we're increasingly getting to a point where models come out and they're, uh, you know, a year or two ago it was like, Hey, they work on AMD on day zero, which is great, but now they are performing on AMD the day it releases, right? [00:02:32] Anush Elangovan: So, what does it take to Prefetch where the industry is going? Be prepared to intercept. At that point is what you know, I, I refer to as you know, the, the speed factor in, in creating this mode, right? And the mode is just shed all things that hold you back and run as fast as you can. [00:02:53] Anush Elangovan: Uh, because the pace of innovation that is, uh, being seen in, in AI [00:03:00] industries is just. Amazing. Right? And it's like, it's transformational at at how you generate electricity. It's transformational as at how you build data centers. It's transformational at how you deploy compute, networking. It's transformational at what kind of use cases you, you know, uh, use AI for. [00:03:17] Anush Elangovan: Uh, and for that, you need to be prepared to, see what comes tomorrow and be prepared to run the race tomorrow. [00:03:23] Andrew Zigler: Yeah, it's a really great perspective because it highlights that it's not just like a checkpoint that you run through. I like how you called out, like it's not just hitting that benchmark or being the best in class at that moment, in that snapshot, it's about having a. The throughput and about having that dedication to the idea and continuing to deliver on it. [00:03:43] Andrew Zigler: It's not just crossing the threshold, but it's also being the engine. And that's what, that's what protects a business. That is the moat, because the moat is that innovation layer, the faster and more, uh, future forward. That you can work and think, [00:04:00] you know, the better. Uh, we, we talk a lot about like future forward work styles. [00:04:04] Andrew Zigler: Like what are the things I could be doing right now today that are gonna be like, way more useful tomorrow? Let, let's abandon those, workflows that are older and that kind of like, that translates into. An advantage when you work that way. You know, what kind of things have you learned working with, uh, like across all spectrums of people who would use ROCm, right? [00:04:23] Andrew Zigler: You have like the developers, but then you also have the enterprises and you have this large span of adoptees, right? So what is the, what does that look like that you learn? [00:04:32] Anush Elangovan: Yeah, so, so the way I look at it is there are gonna be pockets of different, uh, you know, cadences, right? Like, so people who are deploying in enterprises, for example, right? The validation and how long it takes for them to deploy an LLM that's secure. It's, with guardrails, et cetera, maybe longer. [00:04:52] Anush Elangovan: but you still have to go through the process and you have to be prepared to like, walk that walk to deploy an enterprises. That doesn't mean it's [00:05:00] not fast, that's as fast as you can do for that industry, right? And if you are deploying AI in healthcare, right, it's, it's got its own, uh, cycle. [00:05:07] Anush Elangovan: but in each one of these, you want to see how, like, go down to the essence of what is it that you actually have to do. And, you know, I, I, I like how you framed it. It's like it's, you shed your prior assumptions of how things are done, right. And, and you kind of build up from a, uh, first principles, uh, approach to say, this is how I could use AI to unlock, whatever I'm doing. [00:05:33] Anush Elangovan: And, and, some of it, you know, it's good to really step back and look at. Just question every part of it, right? Like right now you're getting chat GPT and, Gemini competing for like, math, olympiads and, and, uh, college, uh, reasoning, uh, tests. Right? And, and those are like that, that is amazing and increasingly like complex tasks that they're trying to do. [00:05:58] Anush Elangovan: But there may also be like. [00:06:00] More mundane things that AI could, could get applied to. Right? And, and so when we think about shedding old ways, you wanna shed it not just in like the tip of the spear. It's like, you know, I'm gonna see what's the frontier model. It's also, it could be something as simple as. [00:06:18] Anush Elangovan: How do you choose a, a movie, uh, you know, like a recommendation system, right? Or, or, uh, an automated, uh, flight, uh, rebooking system. So the moment, you know, your flight is late, uh, right now it's a notification, right? It's like, oh, you got a text message saying your flight's late. And I got that like three times this week. [00:06:38] Anush Elangovan: But anyway, uh, and, and, and, and, I was just like, okay, so if I were to rethink this. All this MCPs that we have that should be hooked up into an MCP that says, your flight's delayed. Here are your options. If you want, you know, these are the paid options. Yeah. Here are the free options. This will get you back into your you know, Toronto airport [00:07:00] tonight. [00:07:00] Anush Elangovan: Or if you stay, here's a hotel plus this, plus this, plus. It's just like, go ahead is all I should say. Versus now I'm like, okay, can someone, you know, can I call a travel agent? Can I do this? Can I go online and log into And you know, so we gotta fundamentally rethink even those like small, nuances of, things that we do that can be automated out and AI is really, really good at doing something like this, right? Maybe I just explained an AI startup idea right now. Somebody should just start that. [00:07:29] Andrew Zigler: I think you did. Yeah, you definitely did. Someone, one of our listeners is definitely going to lift that off of you. I, I, I, you know, I hate being on the receiving end of those. You feel a little helpless and then you have to like, follow the whole flow. So I know what you mean. Like I, I like how you called out that the build and this like. [00:07:45] Andrew Zigler: Where speed is your moat and the innovation layer is protecting you, is what makes you better than your competitors. How you scale that and you bring that to market. So by understanding the problems that you're solving, uh, throwing away those older assumptions, but also [00:08:00] recognizing that like. We're building every single day, new things and new ways of using stuff that we're still figuring out the implications of. [00:08:08] Andrew Zigler: And so when you have a lot of velocity and you're introducing a lot of new ideas, and maybe you have that workflow now that automatically rebook your flight off of your late flight text message, and uh, I know I would certainly use it, but you know, what kind of philosophies guide the way that y'all think about building this ecosystem to manage that stability while letting folks. [00:08:29] Andrew Zigler: Play with the speed and the assumptions and the airplane re bookings. [00:08:34] Anush Elangovan: so, so I think, you know, we need to peel one layer down, right? and the philosophy is, Hey, we, we just discovered electricity, right? And you know what we're gonna do? We are gonna make motors, uh, or dynamos, right? Like engines. Uh, sure. We don't know if it's gonna be a Ferrari that you're gonna make, or it's a a a a dump truck. [00:08:57] Anush Elangovan: That's good for doing this. But let's [00:09:00] let, which is also required, right? You need a dump truck. You need a garbage truck. And, [00:09:04] Andrew Zigler: Yeah. You need the [00:09:04] Anush Elangovan: course you need, uh, a Ferrari for a midlife crisis, right? So, [00:09:09] Andrew Zigler: precisely. [00:09:10] Anush Elangovan: But, but my, uh, point is what do we build next? And, uh, and this is what I meant by like, okay, let's, let's take those baby steps to build the. [00:09:20] Anush Elangovan: Infrastructure that's required that we know we'll have to use, right? So, so if I just discovered electricity, okay, great. Now one, how do I save this electricity and how do I use it? So there's battery technology, so you need to do something like that, right? Like so. But then you also want to make it into an actionable thing. [00:09:37] Anush Elangovan: You want to make it for like automobiles, or you wanna use it for, you know, powering, uh, entire cities. So it is that transformational. So, uh, AI is that transformational. So, if you distill down, it'll, it'll come down to how do we think about, what we can do with this this fundamental technology that, We may not be aware of what it [00:10:00] is gonna unlock next, but at least you know the next step is clear, right? It's like a dense fog, you know, it's gonna be like, it, it's the right path. You see the light, but it's kind of like out there and, and the steps you're taking are concrete and you're like, okay, this is good. [00:10:16] Anush Elangovan: I, this is better than where I was or where we were. So we are moving forward. So you can build with the. Intuition from what you see in the short term and a tactical view, but towards what you think the future is gonna be. [00:10:28] Andrew Zigler: Right. You almost like we're all in this like fog of war, right? And like you said, you're reaching out and you're trying to step through it. You could think of it too, as like you're in the dark and your hands are up in front of you and you know that. You're, you're not gonna run your face into a wall because your hands are out in front of you, but you're not gonna maybe do much better than that. [00:10:45] Andrew Zigler: So that's kind of like, I think the eco, the, the industry, the world that we find ourselves in, uh, and we all have to, then this becomes the power of an ecosystem, of a group of people working together to create that layer of, [00:11:00] uh, of establishing the [00:11:01] Anush Elangovan: exactly. And I, I, I just, instead of, you know, saying fog of war I describe it as like, you're in this. Beautiful valley with like a morning, uh, fog that's in. You can smell the flowers. You, you hear the birds. You are like, okay, it's, we are in like, uh, utopian paradise and yes, I just need to like, continue the walk, right? [00:11:24] Anush Elangovan: and then move forward with that, conviction that you're in the right spot. [00:11:27] Andrew Zigler: Yeah. So let's talk about that ecosystem world. This nice, I love how you describe it, this grassy side of a hill in the morning that's covered in some mist and maybe we can't see 30 feet in one direction, but it sure is a beautiful hill and it smells nice. And so we're all here. And why is, in that world, why is. [00:11:44] Andrew Zigler: You know, open source, their strategic advantage that y'all are going for in the AI hardware market. And, and then how does like ROCm turn that into wins for people within that ecosystem? [00:11:56] Anush Elangovan: you know, the, the way we look at it is this, is kind of like how I view [00:12:00] AI and the ecosystem, right? But, but it is for everyone to enjoy. Uh, and so we do want to make sure that. You know, it is, uh, beneficial for everyone. [00:12:09] Anush Elangovan: The ecosystem can come in and, and innovate. It's an open innovation engine. and uh, it is very different from, you know, having a walled garden with, Hey, only I know how to do this and I'm gonna do it and throw it over the fence and you can use it or keep walking, right? So we'd like to be good citizens that way, but also. [00:12:30] Anush Elangovan: Uh, it is self-fulfilling in a way, right? Like it, the, the pace at which we innovate with open source is unmatched. Like, you know, our serving engines are like VLLM and, and sg l. Those things, uh, those frameworks are like super, super aggressive in terms of how fast they come out with features and how fast they can you know, get performant models out. [00:12:52] Anush Elangovan: And that compared with what, uh, you'd get from, you know, the likes of like T-R-T-L-L-M or something is always lagging, right? Because you [00:13:00] just can't keep up with you know, 200 commits a week just on one particular model to get that model really performant [00:13:06] Andrew Zigler: And, and, and in that world where, you know, everyone can enjoy the winds of this, what kind of customer stories or innovation stories have really stood out to you and excite you about building and creating this place for developers? [00:13:19] Anush Elangovan: Yeah. So I think the parts that are super exciting for me are when when we get to see a customer that is first skeptical. Then they start a little like, okay, fine, we'll give you a chance. Uh, we do a simple, uh, POC and then they're like, huh, this seems to work. Yeah, we told you it works. [00:13:42] Anush Elangovan: You don't have to change one line of code. Really? Yes, no need to change one line of code. Okay, let's try a production workload. So then they try it. Oh, you're more performant than the competition. Yes. We're more performant than, than the competition. So how much does it cost? And we're like, oh, it's your TCO is better with, uh, [00:14:00] AMD. [00:14:00] Anush Elangovan: So again, they're like, wow, okay, good. So now how do we deploy at scale? And then we go deploy it at scale. And when they give a thumbs up on that and they say, this is good, right? That's when you know, you, you see it go full circle from like, oh, we, we've never heard about AMD to like actually deploy to tens of thousands of GPUs In the order of a few months, right? It, it, it really is fascinating to see and very exciting and invigorating to [00:14:28] Andrew Zigler: Yeah. At like a great exposure to a lot of interesting problems. And, and then people using the infrastructure, the, the technology available to solve those problems. Really specific problems by the way, that's often why they're bringing their data and AI to it, uh, is because it is really specific and important for them. [00:14:45] Andrew Zigler: And there's a, a lot I think that other engineering orgs can learn and even emulate from AMD's success and, and having this open source ecosystem and it causing this acceleration within. You [00:15:00] know, uh, customers and enterprises that use and adopt the tools and, and, and that creates an advantage. And that goes back to why we're talking and like the real thesis of our conversation today. [00:15:10] Andrew Zigler: So how do you think engineering leaders that are listening to this and obviously tapping into this great success AMD has from an open source flywheel, how do you think other, other folks building in the same space can foster that open, first, that open source oriented culture in order to, you know, accelerate their innovation goals? [00:15:29] Anush Elangovan: Yeah, that's a very good question. So the startup that um, was acquired by AMD we, we built, I mean, we started off doing iot stuff and you know, smart ring and all that, right? But in the, the end of like, uh, and not the end, the last six years of the company was building ML compilers. [00:15:47] Anush Elangovan: And ml, ML compilers are like super, uh, complicated, sophisticated, advanced algorithms, dah, dah, dah. but it was all open source, right? So our VCs were like, wait, what do you mean your core [00:16:00] IP is open source? And um, the speed is the moat applied even then, right? It was just like, yes, if you have an idea that. [00:16:08] Anush Elangovan: Because someone saw this idea that you are, they're gonna be able to catch up, then you probably have the wrong idea anyway. But if they are, you know, you execute and they're gonna catch up, that you should assume they're gonna catch up. Right? So you gotta move forward. So keeping it open source is super important. [00:16:25] Anush Elangovan: But also to your question on like, you know, the learnings from an AMD standpoint, right? If there are, hard problems, I'd say dig in and work through it, right? Like there's no way but through it, right? That should be the simple mentality. And more, uh, frequently than not. you'll see that you'll just make it through in a, in, in good form. [00:16:52] Anush Elangovan: But if you doubt it and you're like, oh, I don't know if I should commit, if I'm, I, you know, what should just commit to do the right thing [00:17:00] every step, right? Every step, and just keep taking one step in front of the other. And in no time you'll see that you'll be running. Right. And, and yes, the first few steps will be like, yeah, everyone's complaining about your software quality. [00:17:15] Anush Elangovan: Everyone's complaining about this and that, and it doesn't work. And, and a few steps in, you know, you get, you get the hang of all the complaints that are coming in. You get the feedback loop. You're like, okay, what, what are you prioritizing again? One step in front of the other, right? You just keep knocking that out and then you get to a point where you're, it just becomes second nature, right? To do the, to do the right thing. And, and then yes, if someone gives you two options, you'll be like, fine. This is, uh, you know, there's always the resource trade off. There's always a human capital trade off, but what's the right thing to do? of course, I, I'm pragmatic about what we choose, but, but if the right thing for your long-term success is dig in, go first, principles, make it [00:18:00] happen. [00:18:00] Anush Elangovan: Well. Then just go for that. There's, there is no shortcut to [00:18:04] Andrew Zigler: acknowledging, you know, how it aligns with your mission, your core company goals, and what you're looking to achieve. And, and I, I love how you rightfully called out that in the open source world and you know, you have your technology that you've built, what you think is your moat upon, right? [00:18:22] Andrew Zigler: It's your code and, and to open source that, or to just make it where anyone could peer in is, you know. Scary in one regard, but two, it just kind of feels like you're handing away your throne room in some kind of sense, a very direct feeling sense. But the ultimately, you were really right to call out, and this is something I think about all the time, that the real power there is still the speed This the speed. [00:18:42] Andrew Zigler: That was the moat at the beginning of our conversation. It's the speed in combination with your. Very specific domain understanding of what you're building and what you're creating, and your new role as the steward of that world and how people plug into it, which [00:19:00] has frankly, a lot more influence and power than lording over a closed. [00:19:04] Andrew Zigler: You know, repository or an ecosystem, and like you said, like throwing things over the wall. Sure. There, there might be people always on the other side of that wall, but you're not gonna have a great connection with them. You're not gonna be able to really clearly understand them. I, I like your metaphor of the side of the field of the mountain a lot more. [00:19:23] Andrew Zigler: But, but in the, in this world, you know, where. That speed is, is the power and, and open source is just one way that you can harness that speed to get really far ahead and to innovate. , There's other parts of this equation that you can be experimenting with too, and I'd love to pick your brain about them as a software leader and, and, and one of them is about looking forward and kind of understanding that future that we're all building towards and beyond today's models and hardware. [00:19:48] Andrew Zigler: You know, what do you see as the next major bottleneck or opportunity in the AI compute space? As, as you know, enterprises and folks start to get a little more mature about what's available to [00:20:00] them. [00:20:00] Anush Elangovan: Yeah, I think, the bottleneck and opportunity is, uh, what I'd call, call walking the last mile of ai. Right. Uh, and like I I, I gave you an example, uh, previously, but, but it's similar to that. It's like there are cases where Humans have so many, uh, things to do in your day. You know, like the, if we sit down and actually had a customer focus like, okay, these customers lives, I'm gonna save four hours of this customer's life. And if you actually sit down and look at all of that, it'll be. Easily automatable, easily you know, uh, applicable, uh, for ai, right? [00:20:39] Anush Elangovan: Like, but then making it happen is gonna take a little bit, right? It's like maybe it's, uh, paying your utility bill, right? Or something like that, right? Or, or, your healthcare explanation of benefits. Uh, like, I'm sure you get an explanation of benefits, and I'm like, I, I don't even know what that thing is. [00:20:55] Anush Elangovan: It's just like EOB and like. [00:20:57] Andrew Zigler: it's a big, a big old PDF. Yeah, [00:21:00] exactly. [00:21:01] Anush Elangovan: Like, like, I'm like great straight to the, uh, shredder, right? And but that could be, you know, automated with the ai, right? It, it, it'd be like, Hey, the summary of this thing is you went and visited this day. Everything is okay. Everything is paid for, so don't worry, it's not a bill. [00:21:17] Anush Elangovan: That again, the same, uh, thing, but the sense of what that information overload is could be. Digested by ai, uh, accumulated over time and retrieved when you need it. Like, I don't, I actually don't even need to know this EOB right now, unless of course, whenever I need to know it, that maybe, you know, like for some benefits I need to figure out what do, what did I do over the past year and how do I apply it? Source:](https://image.24vids.com/tw-1994972575768727743/amplify_video_thumb/1994972220930621440/img/_5CCZJyz89oDfqQJ.jpg)
