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, 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.
Show Notes
- Follow Anush LinkedIn | X
- AMD ROCm Software: github.com/ROCm
- AMD Developer Cloud
- Learn more at: amd.com
Transcript
(Disclaimer: may contain unintentionally confusing, inaccurate and/or amusing transcription errors)
[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 united.com? 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?
[00:21:43] Anush Elangovan: Or there's a tax credit and even that should be automated. Really, it should just be like, great, here's all of your EOB and you are eligible for this amount of tax credit. Great Go. And whether it's tax credit or whatever it is, right? So stuff like that seems very mundane, [00:22:00] but will drastically change people's lives.
[00:22:04] Anush Elangovan: I know we, we are super focused on the halo possibilities and probabilities, right? It's like, hey, I write a prompt and you know, I get a amazing, uh, video with audio and it's, you know, it's going to revolutionize, uh. How movies are made, or, uh, green screen technology, which is great, right? We need that aspirations those aspirations for us to be able to look forward and like connect our brain to be like, wow, just imagine the possibility that now I am I can make my own movie.
[00:22:32] Anush Elangovan: but then I also don't want to deal with this EOB, right? Like, so I, I want to get this EOB off my table. I, you know, so if, if you can actually save the EOB like 10 EOB letters, uh, not that I get EOB letters that much, but I'm just randomly picking on something that's mundane and, and annoying not necessarily useful, but is, uh, very applicable for us to kind of like, uh.
[00:22:57] Anush Elangovan: Give back time for us to enjoy [00:23:00] the beautiful hillside, uh, with our friends
[00:23:02] Andrew Zigler: I think that's the, a real opportunity in front of us about, it's about externalizing. These co this cognitive load that doesn't serve us in some capacities, like up until AI is on the scene, you know, how did you deal with your EOB and knowing what's in your health plan and you know, you get it in the mail and then you maybe decide to shred it.
[00:23:23] Andrew Zigler: And so later when you need to know those tax intricacies, oh, too bad. It's not on hand. Now you gotta go hunt it down. But, but maybe, maybe you're like me and it goes in the filing cabinet and you have three of them for the last three year, and then now you're like. Which one's even the right one. So everyone creates their own problems, but end of the day, no one needs to know what's in that EOB until, unless there's an on demand on in within context problem.
[00:23:46] Andrew Zigler: Right? And this is where the AI compute. Making our lives easier, solving problems for us. Getting rid of toil really comes in because you can imagine a future where that information, it's available to you. It's no longer printed out on [00:24:00] paper and killing a bunch of trees, but it's maybe somewhere that you can access and where you can easily ask questions against it.
[00:24:07] Andrew Zigler: I, I, I know I would surely prefer to ask. My EOB uh, questions then to try to sit down and read it or use the appendix, right? So that's a, and that's a principle's first re-imagining of that entire process. Because up until now, humans, us, the people on the healthcare plan, we're the ones responsible for reading that thing that comes in the mail.
[00:24:28] Andrew Zigler: Uh, you have to throw away those assumptions and build for the new common ground that we have available. With the new resources that we have and I, that's what's really exciting about the, the open ecosystem becoming a dominant model and you know, how do you think that it being open changes the kinds of problems that we can solve as developers and as organizations.
[00:24:49] Anush Elangovan: I think um, the way, uh, you should look at open is is like published literature, right? The reason that you publish [00:25:00] literature uh, scientific literature is that. It moves the industry forward, right? So you, you, you cite something, you do your research, you say, Hey, this is what I've done. And then others are like, oh, refer to this paper and this publication and because of this and my experiments, we move the ball forward like two millimeters, right?
[00:25:20] Anush Elangovan: And then someone else says, oh, look at that ball that moved two millimeters and I'm gonna build another one. And it's collective good for humanity. Right. Uh, it is to permissively share your creations so that the next generation is able to build on it. Right. When it is created with the intention of not being shared, it is made in a, in a way that it is, you know, exclusive for.
[00:25:50] Andrew Zigler: Mm-hmm.
[00:25:51] Anush Elangovan: for gain, right? Uh, and, and it may work in the short term, which is fine, right? I'm happy for everyone it works for. But [00:26:00] philosophically it is not what moves me, uh, or like our strategy at AMD, which is because we want to be in the forefront, like you said, put those arms out. But there are like 10 other arms pushing the same ball around the same.
[00:26:17] Anush Elangovan: Uh, way so moving the ball forward, if you will. And it's not like, Hey, we know everything. You guys can sit on the side, we'll move it forward, and then you can come, you know, uh, you know, kind of like play in our, our, uh, little walled garden. Uh, it's not little, but still it's a, it's a walled garden, right?
[00:26:32] Anush Elangovan: But when eventually everything is open and we get to that, the, learnings will be, uh, much understood. Right. And, and, and if you look at it. Retrospectively, it is not easy to say that all what is closed and built. Closed is purely grounds up built, closed, right? Because that closed walled garden was built. On this [00:27:00] open, uh, countryside, right? It's like, because you, yes. They took the learnings of Linux and Apache and this and that and whatever else. And then yes, we've built a oasis that's fenced, which is like, okay, great. You know, it works for them. It's not you know, interesting for like, uh, like I said, for me personally at least, but our, our thesis is just that let, innovation flourish and, and let common knowledge, uh, frontier knowledge be shared so that that innovation moves forward, uh, for everyone.
[00:27:32] Andrew Zigler: there are two different bets and they're, they're going in different directions. 'cause one, the closed one, right? It's, it's implying that you have the best way of doing it and there has to be done in a certain way, and it needs to be, you know, you're, you're controlling the flow of the usage as much as you are the usage and how it's done itself.
[00:27:50] Andrew Zigler: Whereas the other bet is. Betting more on the baseline assumptions that you opened with at the beginning about acknowledging that something like AI is like [00:28:00] electricity and like electricity requires a lot of infrastructure. Um, I loved that metaphor at the top of our conversation because I think of Benjamin Franklin and because I think of early inventions with electricity and.
[00:28:11] Andrew Zigler: Beyond just discovering it or, or, you know, being shocked by a key on a kite or whatever stories they tell us in school these days. I, he, he had to then go and build a whole new world and he had to sell people on it. He had to create Christmas lights to show people that you could have a Christmas on your Christmas tree that lit up electricity instead of dangerous candles.
[00:28:32] Andrew Zigler: He had to go into the city and he had to build generators inside of old buildings and convince people to let him run these weird machines inside of the homes that they were used to. He had to fundamentally realign people around this idea, and he was making the bet. That this is gonna be something that transforms how everyone uses it, and everyone is going to require this as a baseline, right?
[00:28:52] Andrew Zigler: And so in that world, that infrastructure, it's not open in the same sense as, you know, ROCm can be open. That because it's code, [00:29:00] it can exist in a, in a context, we can all share and use it. But it's those same kind of philosophies that people use to build. And so I think it's a really powerful metaphor that you kind of shared with us.
[00:29:10] Anush Elangovan: No, no, definitely I, and, and, and just, you know, playing off of that, right, it's, it is like, especially when you're going in to show this new way of doing things, right? Uh, using that generator example, right. If you go in and show them that, hey, this is what electricity is and this is what I'm doing and this is how it's gonna solve your, problem, then you can bring people along.
[00:29:32] Anush Elangovan: But if you go in and say, here's the electricity, here's a black magic box, and the outcomes this, uh, thing, they'll be like, okay, fine. It may be useful, but there's this black magic box that you're telling me you gotta pay $4,000 a month to make sure this black magic box only I can go in and fiddle wires.
[00:29:49] Anush Elangovan: Like, what is this thing that's in my basement that's, you know, doing something sure it me value, then there's someone else who comes in and says, Hey, here's what we have. This is what it is. If you wanna go in and [00:30:00] tinker and wire up a couple other things and you want a little meter to show you what's going on, go for it.
[00:30:05] Anush Elangovan: Innovate, right? So that's, that's, you know, at least for me, it'll speak more if someone showed up with that black box versus like a, a open, open, uh, philosophy.
[00:30:17] Andrew Zigler: Yeah. And so the big, the big takeaway I think is that, you know, speed and that openness, it's, those are, those are key things and, and speed in this case, you know, it doesn't just apply to hardware metrics. We're talking about the speed of innovation within an ecosystem for builders, right? And those companies that can unlock that.
[00:30:32] Andrew Zigler: Provide the right platforms for those engineers and for the things that they're shipping. Uh, those are the ones that are gonna define the next decade of development, especially with ai. And, uh, I've really enjoyed this tour, uh, and this kind of a glimpse inside of your world. And Anush, before we wrap, where can listeners follow your work
[00:30:49] Andrew Zigler: and learn more about AMD and the ROCm ecosystem?
[00:30:53] Anush Elangovan: Yeah. So, if you are a developer, github.com/rocm is a good start. Uh, we do provide our [00:31:00] developer cloud, so if you don't have access to AMD machines. You can log in and get access to it. We do have amd.com, which is obviously our, you know, overall company portfolio of, uh, hardware and software that you could, you could, uh, get to.
[00:31:12] Anush Elangovan: And you can find me on, uh, X and Twitter and LinkedIn obviously. uh, if there's anything, you know, drop us a note and we'll, we'll reach out.
[00:31:21] Andrew Zigler: Amazing. Well, we're gonna link everything in our show notes, you know, so our listeners, like always you can tune in and follow. And to those that have been listening, thank you so much for tuning in to Dev Interrupted. And if you liked what you heard or or our conversation, please subscribe on Apple or Spotify or wherever you're consuming this.
[00:31:38] Andrew Zigler: So, uh, you know, we can keep dropping these in your inbox and leave a rating or review. It really means a lot to us and we'd love to hear from you. And you can also check us out on the Dev Interrupted YouTube. You can see this full interview there. And, all of these clips that we've been talking about are gonna be shared on LinkedIn as well.
[00:31:53] Andrew Zigler: So be sure to reach out to Anush and I and thanks so much for listening and Anush, thanks so much again for joining [00:32:00] us.
[00:32:00] Anush Elangovan: Thank you for having me.
