Recorded live at Carnegie Mellon, university president Farnam Jahanian and Arm CEO Rene Haas discuss the future of innovation and education in an AI world.
In this President Lecture Series conversation, Farnam Jahanian flips the script and asks host Rene Haas about his path to becoming CEO of Arm, how his time working with Jensen Huang at NVIDIA helped shape his leadership, and the evolving role of AI in our world.
The two also discuss the future of education and how universities like Carnegie Mellon can best prepare students for an AI-inclusive tech landscape.
Tech Unheard is a podcast from Arm. Find each episode in your podcast feed monthly.
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Rene Haas 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, your host and CEO of Arm. Today, our podcast sounds a little bit different than usual. Carnegie Mellon University kindly hosted me for a conversation with Farnam Jahanian, the university’s president as part of his President Lecture Series. Farnam Jahanian came to Carnegie Mellon from a background in computer science and engineering, joining the university over a decade ago as the vice president for research. Before his tenure at CMU, Farnam spent time at IBM’s research center and Arbor Networks studying Internet growth and stability. He also led the National Science Foundation’s Directorate for Computer and Information Science and Engineering, home to many programs focused on building cyber infrastructure and a computing and information technology workforce. Farnam and I had an excellent conversation and I can’t wait for you to hear it. We actually recorded on the CMU campus in front of a live student audience.
Farnam Jahanian 1:09
Well, Rene, first of all, thank you for joining us. Thanks for being here.
RH 1:13
My pleasure. No, it's great.
FJ 1:15
Really happy to have you here. Can you talk a little bit about your professional journey, in particular, you had an incredible career being part of a couple of startups. You moved to NVIDIA, and of course, you've had an amazing run also at Arm in various capacities, including serving as CEO. So tell us a little bit about your professional journey, and also, what have you learned during that that's influenced you, particularly your approach to leadership as CEO of Arm.
RH 1:42
One of my favorite speeches is if you've ever seen a Steve Jobs commencement speech at
Stanford, and he's got this classic line about connecting the dots, and you can only connect the
dots when you look backwards at your career. I was very fortunate with my first job out of
school was at TI in Houston in semiconductors. And back then, TI was the number one company in semiconductors and I was very intrigued in, when I was in university, around computer engineering and computer design, but also felt semiconductors seem like they're the base technology that makes all of this go. So I chose semiconductors, which was very fortunate in hindsight, because now, particularly when you look at what's going on with the CHIPS Act and tariffs and semiconductors now are front and center to everything. So, I think starting there was
incredibly fortunate. I can absolutely tell you, in 1984 it was not something I thought was going
to be a super strategic decision. Houston was also very warm compared to where I went to university, but I will tell you where I failed the IQ test. I had never been anywhere south
of Cincinnati, and I remember I took a job trip to Houston in February, it was 72 degrees, no
humidity, and if you know what Houston's like, you're gonna see where this is going. And I came back and I told my parents, I said, “Houston's like California. The weather is no humidity, it's blue skies. I'm going to Texas.” And there's no internet, so there's no ChatGPT, there's no
fact checking on how stupid the thing that was to say. But I remember getting down there with
my mom, and in June of ‘84 and it was 98 degrees, and I thought, “Oh, my God, what have I
done?” But that was a great choice in hindsight, because big company, which meant lots of
opportunity. I got to work in fabs, I got to work in design, I got to work in product engineering, I got to work with customers. And one piece of advice that I think I would give folks, or what I
learned is that experimenting with something new and getting out of your comfort zone and
just trying something because you think it's curious, that benefited me. And one of the things
that I put a very, very high premium on in terms of people development and people that we
hire is curiosity. Because you can teach a lot of things in terms of skills, you can teach a lot of
things in terms of how to get things done, but curiosity is that pilot light that keeps you going,
and if you’ve got a natural curiosity for doing things, you're gonna go pretty far.
FJ 04:02
So how did you end up at NVIDIA? And how was that experience?
RH 04:07
Yeah, so I had now been doing seven years of startups, none that had gone anywhere. So I
I moved to Silicon Valley in the mid 1990s. I wanted to do a startup - didn't really know what a
startup even meant, to be quite frank, I just thought it was an interesting thing to go off and
try. And I did a couple of them that subsequently had done okay, but in the time that I was doing it, it was a very difficult time. I joined right before the dot-com boom.
FJ 04:31
So this is in the late ‘90 or early -
RH 04:33
Late 1990s, around your Arbor Networks time frame, right? So late 1990s I joined Tensilica,
which was a company doing, of all things, semiconductor, microprocessor IP. Again, back on
how the dots connect, that's a company I'm leading now. What we were doing was a processor
generator, where you could generate a custom CPU, you could add custom instructions. It was
a mix of replacement of RTL and code, and the internet's taking off. So as a result, there's tons
of companies doing custom chips for packet forwarding, all the things relative to security for
the internet. We hit the dot-com spike – doom – and as a result, we had to lay off 40% of our people, we had to cut back revenue. And then I had a chance to do another startup, again, doing 10 Gigabit Ethernet, which, back then sounds like, ‘oh, that's a no brainer’, but the world was not ready to put down infrastructure for 10 Gigabit Ethernet, let alone one gigabit, and we were running out of money. And then, you know, at the time, I thought, “oh my gosh, I've got two small kids. I don't really have a medical plan. I should go join a real company that's got actually a 401k and can pay its employees.” So I got a call out of the random from a recruiter for NVIDIA and I first thought, you know, my background at that point in time had been around chip design, product engineering, microprocessors. I couldn't spell GPU if you gave me the G and the P. And I met with Jensen – NVIDIA was not a very old company at that time, probably 10, 12 years, and by the way, was doing well, but not – nothing close to what they are now. And I remember asking Jensen, I said, “Look, I'll be very honest with you. I can do a lot of things, but I don't know anything about GPUs.” And he looked at me, I remember, I can recollect very well. He said, “We invented the GPU. If you think I need you to help me with a GPU, you must be kidding.” That was my first learning of Jensen, and it holds true. But yeah, I landed there in 2006 and it was a fantastic experience.
FJ 06:28
And after a few years at NVIDIA, I think seven years or so, you were recruited to go to Arm.
RH 06:34
Yeah, so this is 12 years ago.
When I started, it was $20 billion so the market cap of NVIDIA was basically flat for seven years. It went up and went down a little bit. And we were trying to find our way, right? We were dabbling in mobile. We started CUDA in 2006 and I like to say this, you know, CUDA was a solution looking for a problem. Because at the time, Intel's dominant, everything's being done through x86, programming on a GPU, who wants to do that? And then secondly, what we were trying to do in NVIDIA was there was a lot of effort around that time, around something called OpenCL, which was a language for openness for GPUs. And I think had OpenCL taken off, GPUs might have been adopted sooner, relative from a programming standpoint, but NVIDIA would not be in the position that they were today. But make a long story short, it wasn't obvious that NVIDIA was going to be in a big growth trajectory, and I was doing a lot of work with Arm, because at NVIDIA, one of the things I was managing at that time was all the SoCs that were Arm-based. This was not only for mobile, but for Windows devices. And I just thought, “we'll see if I'm right. I think Arm's got a great long-term projection, probably better than NVIDIA's”. And the job I took wasn't kind of a real job. I remember it was, you described it very eloquently as Vice President of Strategic Alliances. But it was kind of a made up job. I liked Arm. I liked the company, the CEO at the time, Simon, and the guy I was working for, Ian, they said, “This is a job that we really haven't had before. How do you feel about doing it?” And I again, back to the curiosity thing. I wasn't really hung up on the “Oh, it's got to be this title, and I've got to have this many reports.” In fact, I had no direct reports, which was just fine off the bat, but I just thought Arm was going to be involved in a lot of compelling technology going forward. I feel very lucky that I think that's true, but I think it also came true for NVIDIA. So NVIDIA and Arm have got this kind of weird interlock over the last 20 plus years.
FJ 08:26
Absolutely, it's gone sort of full circle. Now you have a great partnership with NVIDIA.
RH 08:30
We have a great partnership with NVIDIA. And in fact, one of the big benefits that we got with NVIDIA was all of their accelerators were connecting to an external processor, x86, and for a myriad of reasons, performance, power, efficiency, flexibility, they went to an integrated chip approach. The first one was Grace Hopper, Grace being the Arm CPU, and Hopper being the NVIDIA GPU. And then now with Grace Blackwell, which is all fully integrated. So they're doing things with Arm that they really couldn't do with anyone else. So it's great for us, it's great for them, and they're a fantastic partner.
FJ 09:06
By the way, for those of you who may not be familiar, go look up Grace Hopper, and you'll see
something about the history of computer science and technology.
RH 09:14
Absolutely.
FJ 09:16
You're at CMU, so we’ve got to talk about AI. Throughout the history of AI, of course, we've seen periods where AI has gone through a rise and then what people of our generation would refer to as winters of AI. And there have been multiple cycles. Now, over the last several years, of course, with generative AI and a new generation of AI-based systems, it seems different. It seems very different from the AI of the 1970s and 1980s and even the 2000s. What's different about this latest generation of AI-based systems, and what has surprised you about AI today and where it's going potentially?
RH 10:01
Oh, boy, that's a fantastic question. This has sort of been the Holy Grail, right, in terms of, can computers think? And then you get into a definition of, “well, what does think mean, right? Is thinking passing the bar exam where there's a known answer, or is thinking invention, creation, developing something that no one has ever developed before?” So we've been in and out of these winters. And I think back to the NVIDIA example. I think you had the perfect intersection of the huge amount of work done, maybe it's the AlexNet moment, the work that Demis and the DeepMind guys had done, combined with a processing tool that is pretty well suited for this kind of problem. Because I can assure you that when we were working on CUDA, thinking about deep neural nets in the early, early days wasn't one of the application areas. But, you know, look at the early days that work was being done – they were using NVIDIA gaming cards. They weren't actually even using anything that was purpose built. So I think it is the intersection, as many times happen with technology, where a lot of work is taking place on the software side, and then a lot of research is being done in areas like Carnegie Mellon and you had an intersection where now the tools are there.
FJ 11:15
That's right.
RH 11:16
And that's where we're at. Intellectually, I always felt like this was going to happen. We would see this kind of work. I just didn't think I would be able to work on it in my lifetime. I didn't think I would be able to be experiencing it. But now it's moving at such a remarkable pace, it's going to be fascinating to see where it really goes and how it really goes. On one hand, I think there's a little bit of sky's the limit in Stargate and the things we're doing are around that. Then there's another part that says, “well, I don't know yet. Are we stochastic parrots with these LLMs, or are they really, really thinking?”
FJ 11:48
You know, I'm so glad you mentioned the computational resources that were needed to achieve what we're achieving today. You're right that advances in algorithms and LLMs - undeniable in terms of how far we've come over the last couple of decades - but without advances in computational resources and being able to do this, we would have still essentially been looking at AI and not seeing the kind of transformation that we're seeing. So, a follow up to that question, how is Arm meeting this moment? You talked about the challenges, you talked about the speed at which technology is moving, how is Arm meeting this moment?
RH 12:31
So we're putting a lot of energy, no pun intended, into this. So today we are involved with
NVIDIA's Grace Blackwell, and most of the work that we do in that is, the GPU handles a lot of
the heavy workloads, you obviously need the CPU to do a lot of offload management of the
system, you know, etc, etc. But at the highest level, the good news for Arm is that we're now
involved in the data center. There's a lot of workloads that are being run around AI. They're all
running through Arm. That's great, and we're spending a lot of investment about how to make our CPUs more energy efficient and how to solve that problem in a better way. On the flip side, basically the client-cloud model, which has existed for decades. AI is not a client model today. It's not even close. And history has taught us that ultimately everything on some level of compute, moves from the Cloud to something that's smaller. Doesn't mean that the cloud is replaced, absolutely not, but it means that some level of hybrid computing will take place. So we are spending a lot of time on that, and the reason for that is that you were mentioning earlier about Arm's footprint, 30 billion devices a year, 300 billion devices, etc. etc. But what that means is pretty much every compute device that we have today, whether it's your security camera, your earbuds, your mobile phone, your PC, Arm is inside. We believe that all those devices are going to run AI, obviously, but to what extent will they run the major workload of AI, how much of that can be done locally versus offsetting the cloud? How much of that inference takes place locally versus having to go back to the cloud every time? So we are spending a lot of time on that. By the way, it's a great time to be in technology because it's a very hard problem to solve, right? AI is heavily compute intensive. It's heavily memory bandwidth intensive. It takes a lot of energy, sucks up a lot of power. Those are all things you hate when you're trying to build small devices that have to run off batteries and, oh, by the way, these devices also have to run the operating system that they did in the past. On the flip side, I think there's incredible opportunity for another huge shift in our personal devices. And if you think about your mobile phone today, your mobile phone is a ubiquitous device. The generation can't live without it, but it was only invented in 2008 so it's not that old of a device. It's basically a pull device. You have to go into it to pull information everywhere you can. Your phone is full and littered with apps. Imagine a world that a lot of the things that you need get pushed to you. Now I'm not saying the phone goes away and maybe the form factor doesn't, but there could be other things that supplant that, that AI pushes to. You're on holiday and you're trying to figure out a map and where your dinner reservations are. It just knows, in other words, it knows you're lost, right? It knows you don't know, actually, where the restaurant is. It actually knows you don't know where the tour bus is. And that information whether it's through a wearable, whether it's through something in your ears, gets pushed to you, versus having your head down and having to look at the phone. So I'm hoping that there's a generation of not heads down anymore, where it's a bit of a heads up. And I think that's actually quite possible.
FJ 15:58
And honestly, I don't think we're too far from that model that you're describing.
RH 16:04
I don't think we're too far, and my intent is that Arm is the center of it, because we're running those compute workloads already locally. So what we're really looking to put effort and energy into is how to make that happen on the Arm platform.
FJ 16:18
So this notion of edge computing becomes much more of a reality now, combined with AI, that
makes sense. You brought up energy, and I want to just touch on it. You briefly alluded to what
Arm is doing in this context. You actually, you've been quoted saying that companies need to rethink everything to tackle energy efficiency. We all know that energy efficiency, sustainability,
are topics that you know, the entire industry is concerned about, obviously, not only in terms of
energy production, but also the impact potentially on the environment and so on. We have to be cognizant of it. Beyond the specific things that Arm is doing, what are your thoughts about energy efficiency? I mean, these data centers are going to consume more and more energy. We know this. And some data shows that in the United States, from 2% of our electricity quickly, within a few years, about 12% of electricity is going to be going toward these data centers that are powering essentially AI systems. What are your thoughts on it? What words of wisdom do you have about the issue of energy and AI for us?
RH 17:19
Yeah, it's a great question, and, on one hand, the industry got a respite, maybe not in a good
way, from the standpoint of sustainability in that in the previous administration, getting access
or getting permitting for new data centers was pretty hard, and a lot of companies had edicts
where they were going to be carbon neutral by sometime near the end of the decade, which is
really hard to do, by the way. But I think in the long term for the planet, it has to be solved. Ultimately, the rate of growth of these data centers and what's required, it's not sustainable, which again, brings me back to the role I want Arm to play is moving these things to edge devices, where you're not having to run all these giant workloads in the cloud, where you can run things in a more efficient way at the edge.
FJ 18:09
You know, I think there's no doubt that not only we need to invest, not just in data centers, but
we need to invest in the energy infrastructure of the country, the world actually, to be able to sustain it. And then at the same time, lots of research that needs to be done, to look at the potential environmental, climate impact of, and also double down on research that allows us
to move down the path of decarbonization, move down the path of much, much more efficient
systems that we have, not just computational resources from that point of view, but also the
physical systems that we have that rely on digital technologies to be much more efficient.
RH 18:50
And I think that's a huge opportunity here, at universities, because it’s not the kind of thing corporate is very good at. The corporate world is working on roadmaps. We're working with supply chains. We're working with lots of different people inside the ecosystem. And if you're a young startup company working in these areas, it's hard, because how do you think about the productization opportunities around that? So I think the research done by universities in this space around sustainability and energy efficiency, it's a huge, huge opportunity for it.
FJ 19:21
I am so glad you mentioned that, because here at Carnegie Mellon —
RH 19:25
That wasn't a setup. That wasn’t a setup.
FJ 19:26
You knew that was coming. But candidly, I think absolutely, we need to have a very expansive research agenda for energy. I do want to put a plug in about three dozen of my faculty colleagues, right before the summit that we had, got together and in fact, developed a couple of dozen white papers on issues surrounding energy innovation and AI, covering a range of topics, from, for example, how do you cut energy demand? How do you use, essentially, AI to develop more sustainable, more energy efficient systems? To the issue that I mentioned about decarbonization of various industries, from construction to steel to transportation, and also issues surrounding accelerating development of innovative new solutions, including new materials that can be used for batteries and so on. So the range of topics that you can touch, as far as energy is concerned, and AI innovation is almost limitless. I want to go back to innovation and Arm. You know, when technology is moving so fast, when the speed of technology, and we have to admit that, in fact, again, people who are of a generation during the early 2000s, we thought the digital revolution - we thought the internet technology and so on, is moving so fast, how is society going to keep up with it? Well, it turns out today, in fact, we've seen a much higher acceleration of emerging technologies, whether it's quantum, whether it's AI, whether it's wireless technologies. How is Arm prioritizing and managing the trade-off between the speed of innovation, and being there to serve your customers and so on, engineering quality and at the same time, profitability of the business itself. How do you tackle this?
RH 22:21
Yeah, that's a topic we talk about a lot inside the company, and we were chatting earlier about
my NVIDIA time. I think one of the things that I learned probably most from working with Jensen was speed. And quality, obviously, you don't want to compromise on and ultimately, as CEO of a public company, you are accountable to the shareholders in terms of financials and profitability. But speed makes up for a lot of sins. What do I mean by that? The pace of innovation is so fast
that ultimately, mistakes are going to be made relative to the risks you take and how you
ultimately decide what to invest in and what not to invest in. To be able to move very, very
quickly and decide what your priorities are, but more importantly, when that priority is no
longer a priority, is really important. Case in point, we do long range planning. We have three
year plans, five-year plans, 10-year plans. We rip them up all the time. All the time. There's no sacred cow relative to, we'll do an annual planning cycle, and two months later, it might be completely changed, because the world has changed. And I am a very big believer of a mix of long range planning, but being able to pivot in the moment. So I've been the CEO for three and a half years, and that was one of the very first things that I talked about when I became CEO, and I had a slide that said, “if things feel comfortable to you, you're not going too fast.” I don't want people to have a sense of comfort in terms of where things are, because once you kind of have a sense of comfort that you're going to speed limit, people will pass you, And 40 years of
technology innovation, and you can look at the great companies that sat on top of the world,
whether it's Nokia or Blackberry and some of these companies, and then people say, ‘Well,
what's wrong?’ Kind of got too late to pivot. So back at Arm, that's the highest priority I put. I
drive my people a little crazy with it, but that's the priority I put.
FJ 23:21
You mentioned Jensen a couple of times. In an interview that you did, you referred to Jensen as a mentor, a friend and a former boss. So when you think about your relationship with Jensen, what are some of the lessons that you learned from him during your career, and how has that influenced your leadership style at Arm in particular.
RH 23:45
So for those who have probably watched Jensen's GTC keynotes, they are very long and they are very in depth. He has an incredible command of the technical depth, and knows his subject matter really deeply. And that's what I learned from him in terms of working with him, and that I instill. So here's a story. I had been there maybe three or four months, and he wanted to do a business review of my business. And classic big companies, you want to do a business review. You put together a bunch of slides. Your business analyst puts together a bunch of financials. You have some people helping you with the technical specs and the road map. So you’ve just got a mountain of data that you know, no matter what the CEO asks you, you know he's not gonna stump you and you usually come into the room with three or four of your team members. So he sends the invite, and it's just me, and I'm thinking, “well, wait a minute, why am I the only person in the room?” And I remember checking with his EA and said, “no, Jensen just wants you.” And I remember this so vividly Farnam, when I got up to his office, he said, “why'd you bring your computer?” I just didn't know what to say. He said, “There's a whiteboard there, go describe your business.”
FJ 24:54
Wow. And so what did you do?
RH 24:59
That's a very classic question. Where would you even start? And by the way, that is the mastery of that question, right? “Go describe your business.” And what he wanted to try to understand in first principle is how you think, to teach you to think about what's important. And it's very liberating when you're on a whiteboard with nobody on your team around you, to try to explain to the CEO of the company. And I was running – at that time, I was running a big business for him, and he would say, “you are the CEO of this business. You need to tell me what's important.” And by the way, he did that with everybody. But back to the example of the GTC, and now how I operate, it was “know all the details, be able to talk about your strategy extemporaneously.” And the key thing, this was probably the biggest learning, and he used to say this all the time, and my team, I think, can attest to this: “Your strategy is not what you say. Your strategy is what you do, your actions, how you're actually leading your team, how you're communicating. That is your strategy.” You may say what it is, and you may put a bunch of bullet points together, but if you have to think about conjuring that up, it hasn't come to you naturally. So that was the biggest learning. I do the same thing with my team now too, by the way.
FJ 26:11
I'm going to try this on my leadership team next week. Folks, you know, I know you agree with me that I can sit here and listen to Rene talk about not only his personal and professional journey, but also his perspective about the industry, about Arm and also his leadership philosophy. But I did promise him that we will turn the table, and he can ask me a few questions, and then I have one final question for Rene before we wrap up.
RH 26:37
One of the things that, and it comes up a lot in terms of when we think about AI, you know, inside the company. And I'll just give an example, that we're starting to use it a lot inside Arm, and we're using it in the GNA functions, finance, we use it in legal, but we're starting to use it inside engineering. And again, Arm's products are basically the building blocks for doing chip design. We don't build the chip, but we basically design the intellectual property. So we create the RTL, we do the verification, we do all the documentation. So we're a virtual chip company, if you will. There's a lot of work involved with that, and the company's about 80-85% engineers. We're now starting to use these tools a lot for work that our graduate, what we call in the UK, graduate students, would have done, stuff that's like year zero through four, and are even to the point where our head of people came to me and said, “Hey, I think we have to probably have a conversation somewhere down the road of, do we hire as many graduate engineers as we have and/or what do we have them do if a lot of the work that they were being trained on is being done by AI?” Which then leads me to, how do universities help us in this? And what are the roles of universities, and what happens with AI in terms of developing our next great engineers?
FJ 28:04
That's really a great question, and I know that this is something not only CMU is intensely
focused on, but I know many academic institutions, particularly research universities, are
looking at this issue. If you look at some of the studies that are out there, six in 10 business leaders are saying AI is going to transform their organization within a few years. There’s data that shows that 70% of the skills that are required for average jobs will change within the next five to 10 years, and a shift that's in fact, fueled by AI. So when you step back and think about it, the question is, when a student comes to Carnegie Mellon today, what do we teach them such that they're going to be in their professional lives for another 40, 50 years, maybe even longer, depending how medicine progresses and so on. So the issue becomes, what do we do in the short term and what are we going to do in the long term? In the short term, there's no question that we need to be very intentional about bringing AI, not only teaching AI to computer scientists, as we do now. And by the way, Carnegie Mellon was the first university to have an undergraduate degree in AI in 2018, way ahead of everybody else. But also bringing AI actually to other disciplines, not in terms of teaching how to teach other disciplines, but also integrating AI into other disciplines. For example, our engineering college has developed a bunch of masters programs where AI is brought into civil engineering, chemical engineering, and different disciplines – how AI is, in fact, changing those disciplines. So we're bringing that to bear. And I think in the short term that's extremely important.
RH 29:56
There's a lot of debate in our world whether, five years from now, 10 years from now, some of
the white-collar jobs, will we need as many of them, right? Let's take engineers and scientists,
putting the professional debate aside. Do you think the enrollment for those curricula at CMU looks the same five years from now? 10 years from now, do you have the same number? Do you have more? Do you have less?
FJ 30:19
The issue that you're raising is an important one. Would engineers in 10 years, 15 years, have the same set of skills as the engineers do today? I think fundamentally that's going to change. There's no question in my mind that, fundamentally, even the scientific discovery process itself is going to change as a result of advances in technology. So what that really means is we're not just teaching students skills that they need for the next three years, five years, 10 years, but we have to teach them foundations that they're going to be used to learn new things. If you agree with my premise that the half-life of any skill is five years, that means what we really have to teach students is how to learn new things, and constantly to be able to learn new things and embrace, essentially, new technologies, new science, new disciplines and so on. The second point related to that is, we are teaching our students also many of the soft skills. I think what we have embraced at Carnegie Mellon is, while it's important to focus on disciplinary expertise, teaching students what's known as soft skills, which I think really doesn't do justice to it, problem solving, creative thinking, entrepreneurship, communications. These are the skills, when you talk to employers, they will all tell you those are as important as any skills that we teach our students. So that has become very much integrated into our curriculum. It has been very much integrated into our learning experience, not just for scientists and engineers, for students who are studying arts, for students studying social science and humanities, policy and so on. And the final thing that I want to say about all of this is that while students are going through this process, we can't forget that the traditional disciplinary silos you were asking me about would, for example, a civil engineer in 10 years or 15 years need to have the same set of knowledge as civil engineers that has today? I think likely there is some set of knowledge that's foundational that a civil engineer needs to know, or a chemical engineer needs to know, or a chemist needs to know. But what's more important is that many of the problems that we're facing and tackling are interdisciplinary in nature. In other words, not only do you need to have some disciplinary expertise, you need to be able to connect to other disciplines. So, for me, in terms of the future of education is – how do we educate students such that they're going to be very comfortable, not only being comfortable with their disciplinary expertise, but be able to connect with other disciplines, because that's going to be as important as anything else.
RH 32:52
Makes sense. Where I think we are at Arm, I'd be very curious on your viewpoint on this. Back to the graduate example, whereas I don't think we are slowing down hiring engineers. In fact, when I view the way that we could use AI inside our company, it's to develop products faster. It's to take a product today that could take three years to develop. We could, you know, cut that in half to 18 months. The step up would be ideas, and so another way to put this is, I think as long as we have enough ideas, we're going to have a lot of jobs for people. There's no doubt about that. But to what level, how far away do you think we are with today's AI that it can truly think and invent and create?
FJ 33:37
That is really a question of, what do we mean by creativity? What do we mean by invention?
Because there are really different definitions of creativity and invention. Do I expect that our
computer systems, our AI-based systems, are going to mimic human intelligence? I don't think so. See, we don't actually understand what human intelligence is. We actually don't understand what creativity is. So to suggest that somehow we can mimic it, suggests that somehow we have a model of what intelligence is, what creativity is, and we actually don't have a model for it. So I've always believed that these systems, and we've seen this over the last several decades, they will help human beings by augmenting our cognitive and physical capabilities. So we've seen quite a progression, if you notice what happened in automation and manufacturing and so on. We've seen this in physical labor, where technology and automation and AI and robotics has come to bear, such that we're augmenting human physical capabilities, such that a lot of the work that used to be laborious, to be grunt work, is going away, and it's becoming essentially handled by machines. I think the same thing is going to happen – is happening I should say, when it comes to cognitive issues, when it comes to creativity, as we call it creativity. So a lot of stuff that may have been sort of routine is quickly going away. When it comes to doing research, there is no reason to spend so much time to do research if you have an AI-based system that can help you accelerate that research work that you're doing so that you can then pay attention and divide your attention and put it in areas that are much, much more interesting, much, much more fulfilling, and so on. So I'm not one of those people that believes that, oh, jobs are just going away. We do know that some jobs are going to go away. A lot of jobs are going to be transformed, as I mentioned earlier, that we need to be ready for that AI-based economy. But without any doubt, a lot of new kind of skills and jobs are going to be out there that we haven't even dreamt of. So that, I think, is going to be the future of work, if you want to fast forward anywhere between five to 10 years.
RH 36:02
Do you think the question of cognitive intelligence and how we come up with ideas is too complicated a problem for AI to figure out, or why can't AI figure that out?
FJ 36:12
No, I don't think it's actually, this is what I was getting at, is that, I would be hesitant to say that
AI-based systems can mimic human intelligence. By the way, there are people who disagree
with this. What I was really getting at is that, if we can't model human intelligence, and we can't
quite model human creativity, it'll be hard to say that we have essentially replicated it. However, the question that you're asking is a fascinating one: can these AI based systems help us come up with new ideas, new concepts, that potentially human beings could come up with, or perhaps human beings cannot come up with? The answer to that is absolutely yes. There's no question in my mind that the entire scientific discovery itself, process, it's being turned upside down as a result of access to AI and as a result of access to technology, and it's going to transform the way we do discovery, it’s going to transform the way we do research. It's going to transform, in fact, even in a way that we take innovations or inventions, from research and prototype, to translation, to actual commercial systems. I think all of that is going to be impacted by AI-based systems, because they're going to help human beings actually do a far better job than we've done. Can we come up with new ideas based on just AI based systems? We're already doing it. I think it's already happening, and we're seeing it, and I believe that is going to be an acceleration of it moving forward.
RH 37:45
Do you see an opportunity, as President of Carnegie Mellon to harness that in any way, to accelerate how you do research and teaching here?
FJ 37:53
Absolutely, and we're actually doing that today at CMU. Here's the way we were approaching
actually scientific discovery. We've actually created a lab. It's a wet lab, essentially, an experimental environment, where we're automating, essentially wet lab experiments remotely. So a chemist, a biologist, a material scientist, they can do essentially, should be able to do their
experiment remotely by having access to hundreds of essentially potential scientific equipment. Now imagine taking that, Rene, being able to do experiments remotely and augmenting that with machine learning and AI based system such that you can essentially reduce the search space that you're searching for a potential new drug, for a potential essentially new, essentially, discovery, if you're able essentially to bring AI and machine learning and add that with automation and bring all of that together, which is really bringing simulation-based systems, if you will, with physical essentially, manifestation of this, and bringing all that together. I believe we can accelerate scientific discovery. I think we're going to come up with new things that are going to be totally revolutionary that we have not thought of. Of course, this will have impact in life sciences, in medicine, obviously, in other areas, from new materials to new battery systems and energies and so on.
RH 39:18
Amazing, amazing. I think, Farnam, since this is your lecture series, you get the last question.
FJ 39:24
Alright, thank you. What I want to pose to you as our last question is, if you go back a few decades and you put yourself in a position of students, what are one or two pieces of
advice do you have?
RH 39:37
I would say, experiment often and be completely comfortable with failing. Mistakes are made,
we're human, and Farnam, I'm sure, can speak as well – when you get longer in your career, the mistakes you make are the lessons that you learn, and they move you forward. So I would say, experiment often and be happy with failure, because that will give you the advancement that you're going to really deserve and want.
FJ 40:01
Well with that, please join me in thanking our distinguished speaker today, Rene Haas, CEO
of Arm. Rene, thank you for being here.
RH 40:09
Thank you. Thank you.
RH 40:19
Thanks for listening to this month’s episode of Tech Unheard. We’ll be back in the studio next month for another look behind the boardroom door. To be sure you don’t miss new episodes, follow Tech Unheard wherever you get your podcasts. Until then, Tech Unheard is a custom podcast series from Arm and National Public Media. And I’m Arm CEO, Rene Haas. Thanks for listening to Tech Unheard.
Credits 40:44
Arm 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 Merriweather, and Kelly Drake composer, Aaron Levison. 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 Chief Executive Officer Rene Haas.