Tech Unheard Episode 1: Jensen Huang

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Rene:[0:00]

Welcome to Tech Unheard, the podcast that takes you behind the scenes of the most exciting developments in technology. I’m Rene Haas, CEO of Arm. At Arm we’re shaping the future of computing with the industry’s most powerful and energy-efficient compute platform designed to unlock the full potential of AI. Our technology is at the core of innovation for leading companies across the globe. In this podcast, I’ll be sitting down with some of the brightest minds in the industry to share insights, stories and vision for what lies ahead.

Today, I have the privilege to speak with Jensen Huang, the CEO of NVIDIA. A true visionary, my former boss and a personal mentor of mine. We’re going to dive into his journey, the future of AI and how NVIDIA’s unique culture of relentless innovation and ambition continues to push the boundaries of technology. We sat down and met at NVIDIA’s headquarters in Santa Clara to talk.

 

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Rene:[1:02]

Ready to go?

 

Jensen:[1:03]

I was ready the moment I walked in.

 

Rene:[1:05]

It’s great to be back.

 

Jensen:[1:06]

Well, thank you. Yeah, it’s great. It’s great to see you.

 

Rene:[1:08]

It’s great to be back here at NVIDIA. This building did not exist when I worked here many, many years ago.

 

Jensen:[1:14]

How many? How many years ago now? 20? (When you first started?)

 

Rene:[1:17]

I started in 2006. I left in 2013.

 

Jensen:[1:21]

Yeah, see? 20 years.

 

Rene:[1:22]

Yeah. 20 years ago. These buildings did not exist. It’s a, it’s a nice feeling to be back, though.

Familiar. Thanks for, thanks for spending the time.

 

Jensen:[1:27]

Thanks for having me.

 

Rene:[1:28]

So now that you’ve grown so large, one of things I’ve always been curious about, Jensen, with NVIDIA is hiring. The culture is one of a kind. The company does things in a one-of-a-kind way. How do you identify folks who are going to be successful inside NVIDIA?

 

Jensen:[1:42]

We’re not always successful in doing that. Look how you turned out. [laughter] That’s [laughter] it’s always a shot in the dark. I think that the interview process is not an excellent way to judge whether somebody is a good fit. I mean, obviously, everybody could pretend to have a very constructive conversation. You could learn a lot from just watching YouTube on how to interview. And so, you know, the technical questions, of course, people even share what NVIDIA technical questions are. And we try to be as rigorous and difficult as possible. But – but it’s hard. I think that my method is always I go back to reference checks, you know, and I ask them the questions that I was going to ask the candidate. And the reason for that is you could always make make for a great moment, but it’s hard for you to run away from your past. And so I think those are good. I like asking one in-depth question  and just thinking about how they reason through it.

But I think in the final analysis, NVIDIA has been successful for a lot of people. Our attrition rate’s very low, as you know. And and so it’s a really diverse environment with a lot of really interesting people in the background. And we have people from from just about every great company in the world and somehow, we’ve made them successful here. And so I think that that on the one hand, building a great company is about getting great people. On the other hand, building a great company is really about creating the conditions by which those people could do even better than they thought they could. And, you know, a lot of that has to do with being transparent about explaining what NVIDIA’s vision and strategy and what makes us work. As you know, I spent a lot of time doing that. And our company has always been known for its transparency about explaining what what challenges we have, what opportunities we have, what strategies we’re executing. And information is flowing fairly readily inside the company with respect to, you know, what is it the company’s strategies are. I always find that it’s strange when companies have too many silos and, you know, need to know basis. I think obviously, you know, people don’t need to know what they don’t need to know. But the more that they know, the more they’re empowered to be able to make good decisions on our behalf. And so I try to err on the side of transparency. I try to err on the side of empowering people.

And as a result, you know, the company is one of the, I think we’re the smallest large company in the world.

 

Rene:

For sure.

 

Jensen:

You know, I think that’s just that comes with the the incredible productivity of the people. And we have 30, 30,000 people or so maybe a little bit more than that now. And they’re making hundreds of decisions a day. And if all 30,000 of them are, you know, statistically moving in the direction, making decisions that are ambiguous decisions oftentimes, but they’re making it in the direction of of what is in the company’s best interest long term. It adds up really fast.

 

Rene:[4:37]

One of the things that always amazed me is that– you know, back to that point–and again, I don’t know whether it was hiring the right people or self-selection, but by having senior leaders who are extremely comfortable with ambiguity and the fact that you would reach down into different layers, the organization, i.e. the project, is what’s most important.

I just wondered how did that happen? Is it just something that as you grew the company and you had senior leaders who were aligned with your vision, that it grew up that way? Because it was just amazing that so many of the senior leaders here when I worked with NVIDIA, worked at NVIDIA, they were completely fine with the fact that you would just reach around and get the right people in the room to solve a problem.

 

Jensen:[5:14]

Well, first I didn’t ask them, as you recall.

 

Rene:[5:17]

I do recall.

 

Jensen:[5:18]

And the reason for that is because you shouldn’t have to ask permission for something that is that obvious, you know. And so the reason why we said it that way is that NVIDIA was designed to be a full-stack computing company, we were designed to be a company that would build GPUs and CPUs and networking chips and switches, and we would do architecture and design of chips and develop system software and create algorithms and even, you know, create solvers.

And so how would you organize such a thing where everything has to work together on the one hand, but you have to build it in parts on the other hand. [Rene: Yeah.] And so the way we solved the problem was, instead of having organizational silos, we thought of the organizations as a place where the leaders can groom people, create conditions for them to succeed, be of service to them, to help them remove obstacles and such. But the mission; the mission is the boss, and it cuts across the whole company.

So it can cut across systems and chips and networking chips and software and algorithms, and it can cut across all kinds of domains. And by organizing that way, we also created transparency, you know, all these silos became porous. And when organizations are porous, it tends to be better, you know, because you have a lot more people who are able to help you criticize it. You have a lot more people to help you prove it. And so I love this, the porosity of our company, if you will. I just love that everything is transparent and everybody’s helping me make it better. And, you know, [Rene: Yep] it’s not like everything is in some kind of, you know, dark silo.

 

Rene:[6:59]

You almost acquired us, which would have been would’ve been fun. But you acquired Mellanox.

 

Jensen:[7:02]

I know you’re still sad about it.

 

Rene:[7:04]

I’m still sad. [laughter] Every day I cry a little bit, but. But I’m here.

 

Jensen:[7:08]

Thank you. [laughter]  But you guys have done so well. You have done so well.

 

Rene:[7:11]

But you did acquire Mellanox, which has been not only an amazing acquisition in terms of your strategy, but it also just seems like seamlessly, to your point of a porous organization where the mission trumps everything… From the outside in it looks incredibly seamless in terms of execution. How did how did that happen? I mean, how did you make that so? I mean, M&A is so tough.

 

Jensen:

It is tough

 

Rene:

It is culturally very tough.

 

Jensen:[7:32]

Yeah, it is tough. Well, first of all, there are ten people, I think maybe more ten, 12 people on the Mellanox management team, the NVIDIA Israel management team that sits on E-staff.

 

Rene:[7:46]

That’s great. That makes a difference.

 

Jensen:[7:46]

We have architecture, we have research, we have software systems, the chips, we have nets and switches, we have NVLink switches now. We used to have just InfiniBand product line, but now we have a whole Ethernet product line. In the short time that we’ve been together, the product portfolio of Mellanox, well, quadrupled and they’re integrated into every aspect of everything we do. If you look at the transformation and you recall the acquisition, our vision was that the unit of computing was no longer going to be, for example, a GPU, which is really a peripheral. Arm, helped us, in fact, quite importantly, to transition into a company that was building an SoC. And now remember what an SoC is, an SoC is basically a whole computer, whereas a discrete GPU is the last thing that comes up in the computer. The CPUs come up, the boot ROMs come up, the operating systems come up, and eventually the GPU comes up. In the case of an SoC, you have to bring the whole thing up yourself. And so it caused NVIDIA to to evolve from being a algorithm company, which is really what a GPU company is to a computing company. That was our first entry and the SoC wasn’t easy for us in the beginning. We built some amazing ones now. And then the next evolution for us was building systems and DG X1 was our first. In fact, I’m still quite fond of of SHIELD, which is our Android TV computer and I’m very fond of it because it was really NVIDIA’s first full system that we created and –

 

Rene:[9:26]

The learnings on SHIELD must have been amazing. Now looking back, because I remember when we started.

 

Jensen:[9:30]

That, yeah, it is still the most popular Android TV box that people –

 

Rene:[9:34]

Back in the day. It was a PlayStation Xbox controller with a display and we were just think to ourselves, how do we do this?

 

Jensen:[9:40]

Yeah, it is still my favorite thing that NVIDIA has ever made.

 

Rene:[9:42]

I completely forgot about that. Yeah.

 

Jensen:[9:44]

That’s really good.

 

Rene:[9:67]

That was a system.

 

Jensen:[9:48]

Yeah, I learned a lot.

 

Rene:[9:47]

Yeah.

 

Jensen:[9:49]

I learned a lot. And to this day, we’re still maintaining the software.

 

Rene:[9:52]

It was utterly unobvious that there was a fit in the marketplace for this. And I remember folks inside the group suddenly having to source a whole set of components. [cross-talk]

 

Jensen:[10:04]

Exactly. It was my excuse to turn NVIDIA into a systems company and people will ask me, you know, the DGX1, which is the the computer that changed everything. Or, you know, how did that come about? Well, DGX1 one is just a very large SHIELD.

 

Rene:[10:18]

Very large SHIELD. Yeah. Yeah.

 

Jensen:[10:20]

And and so to to me, the fact that SHIELD was made out of plastic, and DGX1 weighs 600 pounds, you know, that transition wasn’t a big deal. The big deal was that we were now able to build systems and and then when we bought Mellanox, the big idea was that the computer was no longer going to be that node, but the computer is going to be the entire data center, that the data center is going to be the unit of computing. And if you don’t, if you don’t design GPU, the CPU, the NIC, the switches, all of the transceivers, and connect everything together and be able to boot that system up, you know, from nothing and get everything all wired up. Get everything all running and distribute workloads across it. If you don’t do that, you’re really not going to understand what it means to build these AI superclusters and that transition, that vision was so clear that it was necessary for galvanizing the two teams. You know, in order to galvanize teams, you have to have a very clear vision. And we had a very clear vision. And that vision was also very tangible because you could see it sitting right in front of you there, super cluster and got all the gear from both companies. And, and so the vision was clear and inspiring. It’s tangible or we have to make it tangible. As CEOs you have to make abstract things tangible. And we went off and built it. And so anyways, I also think that their culture is great.

 

Rene:[11:46]

Yeah. And that clarity really helps. But going back to kind of the vision thing for a second, and there’s another thing that I do when I tell stories about the company. SHIELD a good example. CUDA in the early days, chasing oil and gas, is a good example where it’s completely unobvious.

 

Jensen:[11:57]

People didn’t realize that. In fact, that was our first. Was first. Yeah that was.

 

Rene:[12:00]

Completely unobvious what the the real end quote killer app or end state is yet you have an incredible resiliency to experiment with ideas early and test them even though the market doesn’t either appear ready and or have the definition for it. What do you chalk that up to? Is that incredible intuition? Is that seeing around – [cross talk]

 

Jensen:[12:23]

We’ve had good intuition, you know, ten times in the company, as you know, and the benefit that that NVIDIA has is we are surrounded by extraordinary people. [Rene: Unquestionably. Yeah Unquestionably. I mean, yeah. These are the finest computer scientists, the finest strategists and business people in the world. And they’re egoless and they want to do great things.

And and so I think that that one, we start with that. I think the second part is we’re good at intuition. I think we have a good intuition about what problems need to be solved and how to get us from where we are today to becoming the company we want to be. And so I think our intuition’s good about what the what the various stepping stones are. And, you know, each one of the things that we did a lot of, I was asked, you know, why are we building SHIELD? I mean, what a waste of time. And I said, we’re going to be a systems company someday and all these systems are going to be connected to cloud services. Why go break our pick on the largest systems? Why don’t we go do this one first? And if we can’t do this one, we’re not going to do the large one. And so to create the conditions where the company could go go learn some new skill, fail, but not not damage yourself, you know, and so.

 

Rene:[13:33]

Can that only happen in companies where the leader is or was a founder? Because again, very, very few companies do what you just described, both in terms of being having clarity of vision, but also resiliency to continue to understand where to go. Is that – there’s been a lot written recently about founder mode versus manager mode, and obviously you’re a founder leading a company 30 years later. It goes without saying the amount of success you’ve seen. But can this only be done, what you described, by the founder leading the company?

 

Jensen:[14:03]

I don’t think so. I think you’re doing great at Arm. You know, when I watch you do your work, I’m very proud of it.

 

Rene:[14:08]

Well, I learned from you, which is not [Jensen: Appreciate that.]– which is being truthful.

 

Jensen:[14:12]

Yeah. And I love watching you do your work and it makes me happy. It brings me great joy and pride. I don’t think so. I think that it is true: you have to have great resilience and you have to have perseverance. And I describe it as pain and suffering, as, you know, [laughter] and

 

Rene:

Teaching moments.

 

Jensen:[14:32]

You know, yeah, pain and pain and suffering is how you feel.

 

Rene:

I felt it.

 

Jensen:

And yeah, and in a lot of ways you have to get used to it. You have to get used to the idea that there’s pain and suffering involved. And, you know, that the journey to success is not about one achievement led by another achievement and another achievement. It’s not like that. You know, there are big setbacks, sometimes there’s embarrassing moments, you know, when you’re a CEO and and you haven’t enjoyed any of that yet. But

 

Rene:[15:02]

It’ll happen.

 

Jensen:[15:03]

Well I hope it happens, because it will be good for you. But, you know, all those moments are, I don’t know what I learned from it, but it made me stronger, you know, and I know I could survive it. I know I didn’t like it at the time. But when I look back on it, those are the moments where you

 

Rene:

That’s where you grow.

 

Jensen:[15:23]

That’s right. You are most proud of yourself. You’re most proud of your company and that you survived it. And so, so I think the company- our company is strong because we have lots of stories like that. You know, in the halls of this company are just are just filled with extraordinary stories of one set back after another, set back after another set back.

 

Rene:[15:47]

And with many leaders who– who went through it.

 

Jensen:[15:50]

Yeah, most of them are kind of like, oh, this isn’t nearly as bad as when that happened. [laughter] You know, every time something happened, it’s like, oh, it’s not. This is nothing. So the ability to to be able to go directly to remember when that happened, this is nothing. And yet this is incredibly painful. It’s it helps the company move through these challenging times.

 

Rene:[16:11]

So you and I have been around this industry about the same amount of time and some of the stuff that’s going on with AI, I know I feel this way, were things that I just thought I would never see. That the future generation would be able to experience the kind of transformation that seems to be taking place. It feels like to me not to sound Star Trekkie, but this is the final frontier in terms of I can’t imagine what is next beyond what we’re seeing with artificial intelligence broadly. How do you feel about it? Are we accelerating so greatly the transformation of industry that we’ve never seen before? Is there anything next after this? It’s just unbelievable what we’re seeing.

 

Jensen:[16:46]

I guess I’ve always expected that computers would demonstrate intelligent behavior. That we would be able to write software so well. And I thought we would write it, that algorithms would eventually solve problems in a way that seemingly the computer is intelligent. I never thought that it would result in an industrial revolution. And what I mean by that is, and you’ve heard me say this, that for the very first time, the computer industry has now transcended beyond the traditional computer industry, that for the very first time we’re now no longer a tool, an instrument. But we’re now a manufacturing industry. And so what I mean by that is, you know, right now, while we’re talking, our phones are in our pocket. It’s not being used. And when I’m not using it, you know, I’m not using this tool. It’s not doing anything for me. And most computers are that way. My laptop’s in my office, it’s doing that. Most people’s computers are that way. If you need that tool, you go use that tool. However, this new industry of AI factories, which is what we’re building now, they’re running all the time. Whether you’re using it or not, they’re producing tokens, they’re ingesting data, they’re producing tokens, they’re generating intelligence. Intelligence is being manufactured at a very large scale. And the idea that this computer used to be an instrument, a tool, is now a factory, a manufacturing thing, and that is producing incredibly valuable things at very large volumes. And so this is a new time for our industry. This has never happened before. And the idea that computers are now the manufacturing instruments, the machinery behind this incredible thing called tokens, intelligence tokens, is just an extraordinary idea. And so we’re at the beginning of a new industrial revolution.

 

Rene:[18:33]

Is it racing faster than you thought it would? And you have been closer to it than anyone with AlexNet and DGX1 and have seen the pace of innovation. From where I sit and we’ve been looking at it inside Arm quite deeply since I took over, it has gone far faster than I would have imagined two and a half years ago. Far faster than imagined, even a year ago. You’re involved in everything around it. Is it moving even faster than you imagined?

 

Jensen:[18:55]

No, we’re trying to make it go faster. We’ve gone to a one year cycle, and the reason for that is because the technology has the opportunity to move fast. And because we are now not just building chips and we know that the rate of progress of chips anymore, if you’re lucky, with a new process node, you get a few percent. That’s incredible. And so how do we get X factors of performance with each generation, well the way we solve it is we designed six or seven new chips per system and then we use co-design to reinvent the entire system and invent new things like NVLink switches and new system racks that allow us to drive copper across the entire back spine of a system to connect all of the GPUs together in very large packages and 3D packages and such. We’re using all kinds of techniques to do that. As a result, we could deliver 2 to 3 times more performance at the same amount of energy and cost every year. And that’s another way of essentially reducing the cost of AI by two or three times per year. And that is way faster than Moore’s Law. And so you compound that over, right, five, six, ten years, we’re able to drive incredible cost reduction for intelligence. And the reason why we’re doing that is because we think that this is at a time when we all realize the value of this. If we can drive down the cost tremendously, one, we could do things that inference time like reasoning.

You know, today when you use ChatGPT, which is an amazing service, I use it every day. I used that this morning you hit enter and your prompt is loaded and it generates the output. But in the future it’s going to iteratively reason about the answer and come up with a tree search maybe, and maybe it does some kind of iteration and reflect on its own answers, and eventually it produces an output. It might have gone through a hundred, a thousand inferences, but the quality of answer is so much better. We want to drive the cost down so that we could deliver this new type of reasoning inference with the same level of cost and responsiveness as the past.

 

Rene:[21:03]

I have seen a demo of the OpenAI model that does reasoning and it was shocking to your point. It went through a logic tree. You could see the tradeoffs it was making simply the way a human would, yet at a pace completely unlike the way a human would. But then as you fast forward and this is what’s so fascinating to me about this, what’s going on now, is that exactly to your point, you’re introducing systems full data set and infrastructure at a pace the industry has never ingested at before. CPU’s bought every two or three years, they ultimately depreciated. Now you’re building systems on an annual beat. People want to pay for those systems and deploy them as fast as possible.

 

Jensen:[21:39]

Right now we’re talking it’s so easy to say, but, you know, we’re delivering new computers that are this room size thing each year. It’s all the cabling, all the networking, all the switching, all the software. Yeah, it’s really quite insane.

 

Rene:[21:52]

Do you see it? And I’m not asking you to forward forecast. But this is more just a technology ingestion question. Can it continue at the current pace?

 

Jensen:[22:02]

Yeah, I think so. But it has to be done in a systematic way in the sense that everything that we do, we do in an architectural way. And what that means is that the software that you develop for yesterday’s clusters like Hoppers and that software is going to run on Blackwell and that software will run on Rubin. And the software that you create for Rubin is going to run on Hoppers. Well, this architectural compatibility is really quite vital because the investment of the industry on software is a thousand times larger than the hardware. Not to mention no software ever dies. And so if you develop software, or you release software, you’ve got to maintain the software as long as you shall live. And so the architecture compatibility that the idea of CUDA is not that, you know, there are millions of people programming to it. The idea of CUDA is that there are millions of GPUs, several hundred million GPUs that are compatible with it.

 

Rene:[22:58]

Software doesn’t die.

 

Jensen:[22:59]

Yeah. And so whatever investments that you make on one GPU, you can carry forward to all the other GPUs and all the software you write today will get better tomorrow. All the software we write in the future will run in the install base. And so, number one, we have to be architectural and really disciplined about that. Second, even at the system level, we’re super architectural now. We’ll change pieces of the technology to advance system design without you having to leave everything that you did yesterday behind. And so, for example, you know, when we first came into the data center business, a hyperscale data center had power distribution that was like 12 kilowatts per rack, while Blackwell’s 120 kilowatts per rack. It’s ten times, ten times the density. Now, of course, it’s ten times the density, and it reduces millions and millions of dollars of servers and compressed it into one rack. And so the amount of savings, energy savings and, you know, space savings, it’s just incredible.

 

Rene:[24:03]

That is very similar to our story. You know, the Arm architecture has been around for 30 years and we have software that’s been written for it for decades. And that is what people sometimes don’t always understand.

 

Jensen:[24:11]

Everything we do out on every single Arm chip, we care for it. We just showed something the other day. Somebody did some benchmarking and Grace was four times the performance per energy per watt than the best CPU in world. [Rene: Bravo.] And energy efficiency is vital. That everything.

 

Rene:[24:29]

Do you see anything architecturally starting to break when you go from 500-megawatt data centers to five gigawatt data centers? Just relative to the network latency, things of that nature, without getting into proprietary stuff, do you kind of a high level physics standpoint start to see some things that start to break?

 

Jensen:[24:48]

Everything breaks, physics is obeyed, which is the problem, but everything breaks first. Of course, we’re moving up the power density curve very, very quickly, exponentially. And so from 12 kilowatts to 40 kilowatts, to 120, 200, and it’s going to go beyond that. And so we’re trying to compress, densify computing as much as we can. When we do that, of course, liquid cooling becomes more efficient. When we do that, we can use copper for longer. Copper using electricity for as long as you can is good. So that you don’t have to hop cross electrical to optical. We’ll ultimately have to go optical, but it we’ll stay with electrical as long as we can. And so as much of the data centers we have, it’s more cost effective, it’s more energy efficient, it’s more reliable. And so that causes us to densify. The other benefit of densification is that all of the GPUs that are in the particular rack or in adjacent racks can behave as one giant GPU. It’s really quite amazing.

 

Rene:[25:43]

Amazing, yeah. One of the things I’ve always been curious about Jensen, is keynotes that you did at Computex that I remember watching that when you did and it was on Sunday night, and the sheer volume of content that you go through is not only unbelievable, but as someone else who does keynotes that are not nearly as long or is in depth, I just marvel at how you pull it off. Do you do massive amounts of rehearsal for – remember back in the day when we worked together, I remember changing them at times the evening before and you still pulled them off. But now the level of depth that you go into, particularly given that you’re talking about the datacenter architecture, you’ve expanded it out. How do you prepare for it?

 

Jensen:[26:21]

Well, we’re preparing for it every day. You know, that’s kind of the nice thing about our job is we’re not actors. [laughter] And so so those are kind of our job. We kind of live in it. And so we’re number one, we’re preparing every day. But a lot of what you and I do, frankly, is teaching. In order to shape an industry, in order to shape the market and to introduce new ideas like what we’re trying to do, a lot of it is teaching, you know, it’s not advertising, it’s not, you know. And we’re a platform company, meaning we can’t really do what we do without other people doing it with us. And so we’re about teaching, inspiring, showing, maybe demonstrating, and hopefully step by step by step, you know, we get more and more believers in CUDA in the beginning and NVIDIA accelerated computing today in joining us in our journey and AI and now the next big thing that we’re working on is physical AI and how do we teach AIs that obey the laws of physics on the one hand, and then also understand the physical laws on the other hand. So I think the journey is fairly long and so, you know, GTC and Computex are opportunities for us to do that, to celebrate our ecosystem and the work that they’ve done, teach them, you know, we’re sort of inspiring about the next.

 

Rene:[27:31]

Quite similar, we’ll do QBUs, I’ll do presentations and my chief of staff will say, gosh, the slides are easy. That’s kind of what you say all day. And I’m thinking, well, how could it be different?

 

Jensen:[27:40]

It’s still hard, though it’s to be honest, because we don’t practice. So, you know, it’s not because we choose not to practice. By the time we get all the stuff put together, there is no time to practice. And so we just grip it and rip it.

 

Rene:[27:53]

Jensen, thanks much.

 

Jensen:[27:54]

It’s great to see you. Good job. Everything you’re doing. Proud of you.

 

Rene:[27:57]

[chuckle] Thank you.

 

[music comes in]

 

Rene:

I’m Rene Haas, CEO of Arm and it’s been a pleasure to have you with us today. And thanks again to Jensen Huang for joining us for our very first episode. We’ll be back next month with more exclusive conversations and insights from the world of technology. Make sure you follow Tech Unheard wherever you listen to your podcasts.

 

Rene: [28:18]

Thanks for listening.

 

[music rises]

 

Producer: [28:25]

Tech Unheard is a custom podcast series from Arm and National Public Media. Executive Producers Erica Osher and Shannon Boerner. Project Manager Colin Harden. Creative Lead Producer Isabel Robertson. Editors Andrew Meriwether and Kelly Drake. Composer Aaron Levinson. Arm production contributors include Ami Badani, Claudia Brandon, Simon Jared, Jonathan Armstrong, Ben Webdell, Sofia McKenzie, Kristen Ray and Saumil Shah. Tech Unheard is hosted by Arm CEO Rene Haas.

 

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