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![Just in $AMD Anush "Speed is the moat"|ROCm๐๏ธ 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)
![An interview by VERY DARK AND CORRUPT Wall Street Journal aired today [1] WSJ's terrible "journalists" (and I use that term lightly) made many false statements about Sarepta's worthless, dangerous drug and Vinay Prasad's firing [1,2] I explain how the FDA sausage is made in excruciating detail Buckle up To get readers up to speed -> In June, corrupt pharma company Sarepta Therapeutics paid $40,000 to lobbying group Michael Best Strategies (MBS) to deal with a problem [3] -> MBS had recently hired Chris LaCivita, who had close connections with "MAGA" influencer Laura Loomer [4] -> With stock down 88%, Sarepta needed to sell their very bad, very dangerous drug or the company would go bankrupt [5] -> After several deaths from the drug this year, FDA official Vinay Prasad said "no way" and kicked the drug to the curb [2,6] -> Sarepta panicked and paid MBS (we believe) to deal with Prasad [3,4] -> If this story is right, LaCivita recruited Laura Loomer to take down Prasad [4,7] -> Loomer said she was defending Trump, but she was lying [7] -> She was defending taxpayer-funded payouts to a worthless, corrupt company [7] -> Laura Loomer so brave A history of bad drugs and regulatory failure -> This is one of the worst pharma scandals in American history and corrupt mainstream media isn't covering it -> Sarepta has a very long, troubled history [8] -> For more than a decade, every major Sarepta FDA drug approval has required INTENSE political intervention [8,9] -> Scientists at FDA have been repeatedly overruled [8,9] -> Many scientists have resigned, very publicly, over these POLITICAL decisions, some writing scathing public criticisms of these terrible decisions [10,11] -> The most recent resignation by Vinay Prasad is not something new; it follows in a long tradition [2,10] -> In fact, standards have dramatically deteriorated since the first controversies about the company's drugs in the 2010s [8,9] -> Prasad was trying to hold the line in the face of rapidly deteriorating standards at the agency [2,6] -> For that, pharma launched a coup--a literal coup of a drug regulator [4,6] -> This is unprecedented -> Banana republic sht, unbelievably corrupt 2016: first Sarepta drug approval and the "highly unusual" decision -> The first Sarepta drug approved by FDA was called Exondys 51 [8] -> This drug was for patients with mutations in dystrophin, a muscle protein [8] -> This is a debilitating and fatal disease affecting children [8] -> Exondys 51 increased dystrophin by 0.2% of normal levels [8,12] -> Unsurprisingly, there was no good evidence the drug worked [8,12] -> Why would it? It increases the protein from zero to 1/500th of normal levels -> One reviewer wrote: "I can find no precedent of an accelerated approval for a marketing application where the effect size on the surrogate endpoint is as small as 0.3%." [12] -> The study submitted by the company included no proper control group [12] -> The techniques used were so bad not even a first-year PhD student would do a study that way -> This the level of work you would expect from a mediocre undergraduate with no guidance -> It's almost like it was so bad on purpose -> (Narrator: it was on purpose) -> Nerd time: -> One reviewer wrote: "The Western blots submitted by the applicant for Study 201 were oversaturated, unreliable, and uninterpretable." [12] -> Another wrote: "Because CDER also determined that the conditions under which the original IHC analysis was performed were inadequate, including that the reader was not masked to sequence and time, the Center requested a re-reading of the stored images by three masked pathologists under different conditions. The IHC results from the reread were not nearly as favorable, as compared to the initial IHC results reported by Sarepta." [12] -> "The lack of concordance between the IHC and the Western Blot results is 'striking'" [12] -> "Study 201/202 had fundamental flaws, including baseline biopsies from external controls who could differ in unknown ways from study subjects, Week 180 biopsies from different muscles than baseline, and potential protein degradation in stored baseline samples." [12] -> And on and on. -> FDA commissioner Robert Califf wrote at the time: the submitted study was "characterized by major flaws in the clinical study design" and "Blinded experts assembled by the FDA fundamentally debunked this study, which has yet to be retracted and continues to be cited" [9,12] -> That's right, the FDA commissioner expressed dismay that the study that the company used to gain approval hadn't yet been retracted, it was so bad [9] -> Senior FDA official Janet Woodcock decided to approve before scientific review team had even voted [9,12] -> Woodcock be like: yeah i'm going to decide before you guys can because i know what you're going to say lol -> Despite external intense pressure, FDA scientists voted against Exondys 51's efficacy [9,12] -> They then voted against its accelerated approval [9,12] -> The review team filed an appeal with FDA commissioner after "passionate" disagreement with Woodcock [9,12] -> One reviewer called Woodcock's decision "unprecedented" [12] -> In a 126-page report, FDA commissioner Califf called Woodcock's decision "highly unusual" [9] -> The FDA board wrote: "[Woodcock's] involvement here appears to have upended the typical review and decision-making process. ... Care should be taken to avoid the appearance of interfering with the integrity of scientific reviews at the lower levels of a Center." [9] -> Again, the data were unbelievably bad, literally every technique in the study was inappropriately used [12] -> I would fire an undergraduate student who did science like this, immediately -> FDA's chief scientist accused Sarepta of "serious irresponsibility" for selectively publishing only some of the data [9] -> Even Woodcock, who approved the drug, called the research "seriously deficient" [12] -> Yes, even the person who approved the drug over the heads of FDA's scientists said the research was horrible [12] -> Still, FDA tried to bury their heads in the sand and beg that, basically, Sarepta pretty please do a better job next time -> FDA commissioner: "The utmost attention should be paid to optimizing the methodological rigor of [future] trial[s]" [9] -> FDA also demanded a clinical trial "to verify the benefit" of the drug [8] -> Welp, this was in 2016 [8] -> The trial results are supposed to be available in 2026, maybe [13] -> Or maybe later, depending on how much money needs to be made first -> As an article published in Nature three years later despaired of the decision: "The approval was conditional on the company agreeing to conduct a two-year post-approval trial to show Exondys 51โs efficacy. But by August 2019, the company had yet to begin such a trial and in the meantime had profited from sales of $300 million in 2018." [13] -> If it sounds like Sarepta used political pressure to get its drug approved and then tried to avoid actually publishing the study showing it didn't work, it sounds that way because that's exactly what happened [13] -> FDA commissioner after deferring to Woodcock: "I am confident this unique situation will not set a general precedent for drug approvals under the accelerated approval pathway, as the statute and regulations are clear each situation must be evaluated on its own merits based on the totality of data and information." [9] -> This statement was profoundly naive, and the historical record bears this out [8,14] -> Three FDA scientists resigned, including the lead reviewer of the drug, understanding the grave implications of the collapse of scientific standards and where they would lead [10,11] -> One was John K. Jenkins, M.D. Director, Office of New Drugs Center for Drug Evaluation and Research/FDA [10] -> In a presentation given just before his resignation, he wrote: -> "Path taken by Sarepta NOT a good model for other development programs" [10] -> Crucially: -> "Upholding statutory standards for approval in face of hopes and desires of patients, families, sponsors, and investors is a very difficult job" [10] -> "Personal attacks on FDA reviewers creates an atmosphere of distrust and isolation rather than collaboration" [10] This brings us to WHY Sarepta's drug was approved Facebook FDA -> So why did the drug get approved? -> Basically, Sarepta propagandized extremely desperate patients [9,15] -> They used miraculous snake oil promises and patients believed them -> Remember that this is life or death for patients, and they are extremely vulnerable -> Sarepta also professionally trained some patients to give testimonials to FDA and congress [15] -> The patients then went to congressmen who don't have time to understand the science [15] -> They gave emotional stories to congressmen [15] -> The result: -> Letter from 109 House members [15] -> Letter from 24 Senate members [15] -> And a media circus documented in the New York Times [16] -> Patients screaming at scientists during meetings [9] -> 2,792 emails written to FDA urging approval [12] -> One of them: "Dear Dr. califf: How is it that everyone in and around DMD understands this simple Idea and the science geniuses at FDA don't? You stupid fckers are costing each and every DMD kids days of their lives with your Moronic Dystrophin dance. Time to get a fcking clue" [12] -> Upon approval, a journalist for Reuters wrote: "owing to pressure from patient advocates, the U.S. Food and Drug Administration on Monday approved a treatment for Duchenne muscular dystrophy even though an outside panel of experts and the agency's own reviewers questioned the drug's efficacy" [17] -> A commentary in Nature Medicine was also published called "Railroading at the FDA" [9] -> Its author wrote: "In the words of one FDA committee member, Exondys lowers the agency's evidentiary standard for drug effectiveness 'to an unprecedented nadir.'" [9] -> A highly critical commentary was also published in Science, titled "Sarepta gets an approval - Unfortunately" [18] -> The article's author pharma veteran Derek Lowe wrote: "The company... called up Duchenne-affected boys and their families to plead with the FDA, and won over Janet Woodcock, and that appears to be enough. Is this going to be the new way to get a drug approved? Run a trial in a dozen people, generate unconvincing data, and then lobby Janet Woodcock? I share the worries that this might open the floodgates, because after all, Sarepta got their drug through." [18] -> One FDA reviewer ended in an equally grim note: ". Approval of this NDA would send the signal that political pressure and even intimidation โ not science โ guides FDA decisions, with extremely negative consequences. The public is well aware of this development program: the meager size of the study population, the marginal (at best) effect size, the Divisionโs dim view of the efficacy data, and the robust activism of some members of the DMD community. Many would be amazed at an approval action, because other DMD drugs, recently turned down for approval, appeared to provide stronger evidence of efficacy. ...The ramifications here are profound. The public will perceive that it was their unprecedented lobbying efforts that made the difference and earned eteplirsen its accelerated approval. For the future, this will have the effect of strongly encouraging public activism and intimidation as a substitute for data, which is one of the worst possible consequences for communities with rare diseases. This type of activism is not what was envisioned for patient-focused drug development." [12] -> A new era was born -> Activism had replaced data -> Facebook had fried people's brains -> And now Facebook-fried brains had fried FDA too -> FDA's credibility as a regulatory agency would now be hollowed out -> FDA's Facebook age had begun -> But the worst was yet to come Sarepta approvals: 2016 to present -> Three more drugs were approved from Sarepta on the same shoddy basis, proving Califf's promises that Exondys 51 was an isolated case empty [8,14] -> But things would take a turn for the worse with Sarepta's newest drug Elevidys in 2024 [19] -> At last a rigorous clinical trial looking at actual clinical outcomes was published [19,20] -> All would be put to rest -> At long last the issue could be resolved with HARD CLINICAL DATA -> There was only one problem -> The trial failed to show any benefit according to the primary outcome [19,20] -> The surrogate biomarker of micro-dystrophin meant absolutely nothing; it wasn't actually helping patients [19,20] -> What did FDA scientists do? They voted against approval. Of course [19] -> How could they not? The drug didn't actually work in the clinical trial [19] -> It's the only thing that made sense, since FDA is a scientific agency -> AND THEY WERE OVERRULED AGAIN BY PETER MARKS [19] -> YES THAT'S RIGHT, OVERRULED YET AGAIN -> PHARMA WINS AGAIN -> HAHAHAHAHAHA PHARMA ALWAYS WINS YOU FOOLS -> What happened is that Marks crossed his eyes somewhat, trying to make the words on the page blurry -> He prayed really hard, "my god please give me a sign, something, anything, I need this for my career" -> lzzosolsolzzolzozlslzolosllslozllzlzl -> Marks was trying really hard to see SOMETHING, come on come on, give me SOMETHIGN he said -> And he said: wait, look, there are these secondary, exploratory endpoints and a two of them look pretty good, I'LL APPROVE [19,20] -> AHAHAHHAHAHA YES PHAMRA WINS AGAIN -> And Marks said, "Thank you pharma go- I mean god, not pharma god, why did I just say that, FCK" -> The trial was explicitly designed for what Marks did NOT to happen [20] -> Once the primary endpoint was not met, the secondary endpoints couldn't even be statistically tested [20] -> And the trial explicitly said that they could not be interpreted the way Marks interpreted them [20] -> They were not adjusted for multiplicity and they were, like expression of dystrophin, simply bad endpoints [20] -> These two secondary endpoints were time to rise from lying on the floor and the 10-meter walk/run tests [20] -> Subjects who received the Elevidys performed, on average, about 0.5 seconds better than placebo recipients on these tasks [20] -> However several facts must be borne in mind when interpreting these: -> 1. At the time of testing, patients receiving the drug were receiving more corticosteroids than placebo patients, biasing the results [20] -> 2. Blinding might have been broken because those receiving the drug experienced lots of nausea and vomiting from the drug (~70%) [20] -> 3. These differences were tiny and may be attributable to chance, since the natural course of the disease varies widely [20] -> Marks knows this but who cares? Pharma I mean Facebook needed to be placated Elevidys: the drug -> To understand why this is so messed up, one must understand a few things -> On a Bayesian basis, one must assume that Elevidys is harmful until proven otherwise, for two reasons: -> 1. All drugs are potentially "toxic", but some toxins heal: by default you must assume it is a toxin that does not heal because this is what is actually usually the case; you need evidence that it actually heals -> 2. Elevidys IN PARTICULAR must be assumed to be harmful until proven otherwise because of the very nature of the drug -> Let's do a breakdown of the basic science of Elevidys that supports this (Bayesian) hypothesis: -> Gene therapy that permanently integrates into human genome [21] -> Meant to replace dystrophin, the protein that these patients cannot produce themselves [21] -> Preferentially targets muscle but gets expressed everywhere [21] -> Killed three people this year [6,21] -> Costs $3.2 million per injection [21] -> Truncated version of the protein it is supposed to replace [21] -> 3X shorter than the real protein [21] -> Has to be truncated because the technology cannot create the full protein [21] -> Because it's an abnormal protein, it's foreign, so immune system attacks it [21] -> Patients injected with drug are basically given an autoimmune disease [21] -> Patients have to be given anti-inflammatories to fight the disease that the drug causes [21] -> Causes terrible muscle inflammation [21] -> Inflames the heart, heart walls thicken because of the inflammation [21] -> Blows up the liver, causes acute liver injury and death [21] Drug should actually be assumed harmful, not beneficial -> Given all of the above, since the drug failed to meet its primary endpoint, it should actually be considered harmful by default, not beneficial [19,20] -> In other words, what we would actually expect if we added more patients and did an even larger study... -> Is that the drug would do worse than placebo, i.e., patients taking the drug would do worse than those taking placebo -> Why isn't this the default interpretation? -> They are reading the study with an intervention bias -> An intervention bias is natural, which is why "do no harm" is such a central tenet of medicine -> If I may put forward a thesis: most of Vinay Prasad's 500+-paper body of work has been dedicated to demonstrating the "do no harm" principle empirically [22] -> Rose-colored glasses study interpreters are simply not applying this principle properly and are thus failing scientifically in the most fundamental way -> Incomprehensible -> Back in 2016, scientists were adamant that the approval of Sarepta's first drug indicated the profound deterioration of scientific standards [8,9] -> But this latest approval is even worse: actual clinical data is now being overruled -> No standards at all are being enforced anymore; anything can now be approved based on any evidence whatsoever -> What Vinay was trying to do was simply to stop the unrelenting downslide -> And his firing punctuated that downslide for what it was The WSJ segment -> When Elevidys was approved, former FDA chief scientist and one of the original reviewers of Sarepta's first drug Luciana Borio said: -> "I donโt know what to say. Peter Marks makes a mockery of scientific reasoning and approval standards that have served patients well over decades. This type of action also promotes the growing mistrust in scientific institutions like the FDA." [23] -> To return to this video, these two WSJ reporters show an incredible level of ignorance and arrogance -> Finley says that the drug is "clearly" beneficial by misreading the secondary endpoints, just like Marks did -> An FDA memo from last year says about these endpoints: "Under these circumstances, they are misleading and cannot guide any stakeholdersโincluding patients, family members and caregivers, and prescribersโin making informed decisions about the potential benefit of treatment with ELEVIDYS." [20] -> It really doesn't get any clearer than that -> But these two journalists are overruling the actual scientists, just like Marks did -> One of the most incredible comments during this interview was the complaint that "90% of clinical trials fail", as if that's bad thing [1] -> It's actually a good thing; most drugs suck; failing in clinical trial actually allows us to use only the drugs that don't suck -> These people don't understand the most fundamental purpose of the clinical trial -> They think clinical trials failing is a bad thing, as if it means that patients now won't get to use a useful drug -> No, it's a good thing, because it means that patients won't be exposed unnecessarily to a useless drug that might harm them -> The level of ignorance really is unbelievable -> What's worse is that these "journalists" defend their decision -> But what they did is exploit social media hysteria caused by Laura Loomer [1,7] -> Following up on her heels with editorials, using her as pharma attack dog [1,4] -> This is a huge blow to WSJ's credibility, and they know it -> Unbelievably shameful Where do we go from here? -> The Vinay Prasad firing creates a serious crisis of credibility at FDA [2,6] -> Up to this point, we could call these approvals a difference of opinion, but as we've seen, that's a huge stretch -> But any illusion of that is now shattered: the firing shows that drug regulation is explicitly political -> Janet Woodcock: approve, keep job -> Peter Marks: approve, keep job -> Vinay Prasad: block, transparently fired -> Make a decision that is anti-pharma and lose your job: that's the message -> Who can trust any decision at FDA anymore? -> RFK Jr. and Marty Makary both stand behind Vinay Prasad [24] -> Trump went along with lockdowns, he went along with mask mandates, he went along with all of the Covid pseudoscience that he now decries -> He should reverse course and not go along with this -> Trump has created a profound crisis of credibility at FDA and needs to fix it](https://image.24vids.com/tw-1952294845248114806/amplify_video_thumb/1952292444621451264/img/SPXp7ZpvQZ3FQV7_.jpg)