How QQQ Defines Tomorrow’s Tech[What was discussed at CES 2025]

Engines of Innovation: How QQQ Defines Tomorrow’s Tech, presented by Nasdaq and Invesco QQQ

Good afternoon everyone. Welcome. My name is Mark Maritz. I'm a senior director at Nasdaq in index research. For those of you that don't know Nasdaq. Licenses. Indexes with assets over $600 billion globally. Tracking them today. And our flagship product is the Nasdaq 100, which is on the verge of its 40th anniversary. Later this month. So thank you very much for coming.
Good afternoon. My name is Ryan McCormick. I'm a senior factor and core equity strategist at Invesco. Which is just a fancy way of saying I cover around 110 of our equity linked ETF products. Two of which are our largest strategies that we have in the firm. And they track the aforementioned Nasdaq 100 index. And that would be QQQ and QQQM.
Are the two tickers. We're really excited to be bringing this panel to you. And Nasdaq is arguably one of our most important partners. Just given the assets, the track, the indexes, the Nasdaq provides. And again, to for today, the Nasdaq 100. We're just really excited to try to bring this index story to life.
So I hope you enjoy the next 40 minutes. And, Mark, let me turn it back to you.
Yeah. And I guess we'll be sort of playing a dual role of kind of being panelists but also co moderators. So let's do some more moderating and get into the rest of the intros. Amy, tell us about your background and a little bit about ahm.
Yeah sure. So, great to be here with you guys. So my background is, I was an investment banker with, probably some of the folks on this panel in the financial space, then spent the last probably, almost two decades in tech, both big companies, small companies. Before joining ahm, I was at Nvidia for a number of years and then joined ahm about a year ago and I lead, all things marketing arm.
And just a little bit more about ahm for folks that don't know who we are. We're essentially, a compute platform for everything chip related. So the core of our business is actually CPUs, the brains of every computer or intelligent device. We have about 300 billion chips in market today that are based on ARM. So pretty much everything that you interact with, whether it's your tablet.
So you're interacting with your smartphones, smart thermostats, every intelligent device. I presume you you have interacted or are interacting with ahm. And so we provide energy efficient compute chips for pretty much every intelligent device on the market. So that's a little bit about ARM.
Thank you Robbie. Let's go over to you.
Thank you. Great to be here. I'm Ravi Subramanian. I'm the chief product officer for synopsis. My background is primarily been, starting off as a engineering technologist in the chip space. Specialize in my doctorate on designing chips and getting, semiconductor VLSI signal processing. That evolved into a career half spanning semiconductor design and half in the era of EDA or electronic design automation.
How to automate the creation of chips as more and more complexity can fit into a single die? Synopsis is, the leading company providing design solutions. That is the design, the verification and the manufacturing of semiconductors and, the intellectual property building blocks that go around and complement what Tommy and ahm bring to the table. So enabling customers to build and solutions for various different markets.
And, really the opportunity of the decade is about pervasive intelligence and our view, which is, as we said, intelligent devices. They're becoming pervasive. And what we see is the ability to rapidly design, verify and manufacture these products and bring them into the systems world is a very, very key opportunity for the decade.
Thank you so much. Okay, let's go over to you.
Thank you for having me. My name is Ken. Ken Soo. I work for a company called Autodesk. I hit up our strategic partnerships. My background. Venture capital startups. Investment banking. I've been with a company for 18. This is my 18th year now. It's a very exciting company. Autodesk, we provide cloud solutions for people to design and make things and literally things of all kinds.
If you look at the the virtual world movies, special effects games that you play, people use our technology to design those games to make, to produce those movies. If you look at the physical world, consumer products, buildings, bridges, industrial machineries, people again use our software solution to design these products as well as manufacture, these products or construct buildings.
Company's been around for 43 years and we're constantly reinventing ourselves. So I'm very glad to be here and share share the stage with, with all you guys.
Yes. And Autodesk has the, has the distinguishing, claim to be the longest running member of the Nasdaq 100 index. I saw that you guys joined the index all the way back in 1989, when I was about three years old. So, we're just very excited to be here again, having three, really, I think stellar examples of innovative, pioneering, kind of forward thinking companies, in the technology space that are just very typical of what you find in the Nasdaq 100.
It's not just Amazon and Google and Microsoft and Nvidia. It's 100 really, really special companies that are doing, a number of incredible things, that we're going to cover a bit on this panel. Like I but before we get there, Ryan, I'm going to pass, you know.
Well, you know, I think, you know, these these three companies are a member of this very exclusive club, the Nasdaq 100, and, you know, pretty much the qualifications to be there or you need to be listed on the Nasdaq exchange, and you need to be a non-financial company, and that's it. So you look back at some of the success that the index on the whole has had, but also the companies that are within it.
And you almost have to reverse engineer the story a little bit. Meaning, you know, it's a quantitatively driven index. So the only rules that exist are the ones that I just outlined for you. There's not a portfolio manager or somebody making the decision that, oh, we should invest in this company, or we should make sure that this company is included.
Right. As long as they hit those two parameters, they're eligible to be a member of the index. And so I say we have to reverse engineer the story a little bit to try to figure out what. Well, why has this group of companies been so successful over the near 40 years that that the indexes has been around? You know, I think when we look at the drivers of that success, it all leads back to when I like to joke is a very nebulous term innovation.
You know, I think arguably it's the most overused word of probably 2020 to 23, and we kind of moved away from it in 2024, but it seemed to some extent let everybody it was the most innovative company. Every every financial firm was the most innovative firm. But unless you can really quantify that innovation, it's it's really aspirational.
I mean, it's it's just words in a speech or on a page. So for us, like what we really look to find, you know, ways that we can quantify innovation and what sets apart those members of the Nasdaq 100 from other large cap companies, let's say in something like the S&P 500. And from our stance, like the first way that we try to quantify this innovation is a focus on research and development.
And we found that research and development spend has has been kind of the hallmark of most of these Nasdaq 100 companies, certainly more so in the past two decades. But I think you can make the argument that over the past four decades that the index has has been in existence. Generally research and and development is kind of a longer term spend, right?
You don't throw money at a project and, you know, expect it to to hit the bottom line in a year. Right? It's three years, five years, a decade down the road. But all the same, we found that that these companies spend in order of magnitude. You know, when compared to those S&P 500 companies not in the Nasdaq 100, I mean, the average Nasdaq 100 company is spending ten x what its competitors do on R&D.
So again, we think this is sort of the hallmark of of of this success. The fuel that kind of drives, you know, the innovation that we've seen from these sort of companies. So for us it's really exciting. I would love to hear from all three of you. This is a very long lead in but effectively, how do you look at, at R&D?
I mean, how important is it to you? You know, when you're looking at business decisions and maybe help to bring the light, bring the story to life a little bit? Like how do you view R&D from the world of human capital, let's say from from operational capital, from intellectual capital? And what sort of commitment do each of you and your respective companies, kind of look at when it comes to R&D spend?
So maybe, maybe, Ken, let's start with you.
R&D spend for a software company, human capital, it's, it's super essential. The end of the day is is all about talent. And that's the reason why real estate prices are so high in San Francisco. Human capital. It's, it's something that we we consider ourselves. And that's one of our competitive differentiators. R&D talent is something that's unique to us.
We'll continue to invest in that. If you look at it from a R&D philosophical approach, you're right. A lot of these investments is not like you make an investment this year. You're looking for a return next year. I as an example, we started doing our research back in 2009. We have a special team, started doing research there, published in I think so far they published 75 scientific research paper on AI.
With regards to CAD. We're now the largest publisher on that particular topic or domain, and that laid the foundation for us to introduce our first, also industry's first generative design technology, in 2018. And now fast forward to now. You see AI capabilities throughout all the product offerings, that we provide, whether it's in the media, entertainment sector, manufacturing, architecture, engineering and construction, operations.
You see, I just permeate through all the technologies that we offer to people. And behind that, that didn't just happen because I it's hard for the last 2 or 3 years. Let's hurry up and come up with something cool. It's actually built on a foundation that we started 2009 even even probably before that.
And how about from the synopsis on?
Sure. So the way we think about innovation and R&D is really driven around context in the industry we serve. And just to give you an idea for the better part of the last 25 years, Moore's law has been driving how semiconductors have evolved, and Moore's Law is really doubling complexity every 18 months. And that has been the equation that's driven how electronics can get captured into semiconductors.
And more computing power is delivered in the last five years. The equation is completely different. The equation now is complexity. Growing two x every three months. And you ask what is that? It's the number of parameters in an AI model that is being used. And to compute and train and infer means, you have to be able to support innovation at a rate of two x every three months, compared to two x every 18 months, which has been, as I said, the history for the past 25 years.
So that really drives what the pulse or pace of innovation needs to be. And ultimately, just as Ken said, human capital is our number one asset. It is the asset that drives all the innovation. And today, Synopsys spends 34% of their revenue on R&D, just over $2 billion, significantly greater than what a typical semiconductor company would spend in order to enable semiconductor companies to do what they do.
The second thing that's changing in terms of innovation and how and where to innovate is more and more products are see, our customers and different industries are, serving out of the 1.2 trillion GDP of the world, a greater and greater portion of those products are software defined. So more and more R&D needs to go into software defined semiconductors.
And those are two key trends that drive the innovation mantra. It's all about people and talent. And I would say that's the biggest challenge for the future, which is where do we find the talent to drive all the innovation?
That's super interesting. And I mean, I'd love to hear the perspective from more.
Yeah. I think, probably it's useful to understand ARM's business model and so one of the things we do is we license where, as I mentioned, we're a compute platform company and we license blueprints for folks to create, compute chips. And then every time those compute chips go into different products, whether it's your smartphone or otherwise, we get royalties on that business on each of those devices shipped.
So, you know, I kind of mentioned that because as we design blueprints, the lifeblood of the company is all about engineering. And so even if you look at kind of just from my numbers perspective, total employee count, about 80 to 85% of our business. Yeah, of our employees are our, come from R&D. So it's really all about innovation.
And as Ravi mentioned, keeping pace with innovation, especially in the sort of fast paced world with AI. So one of the things that we focus on around, you know, human capital. So as I mentioned, a lot of our employees are engineers, and we focus a lot on hiring the best talent, because through the best talent, we can keep pace with innovation.
So it's a huge focus for our business in terms of R&D, making sure we're optimizing both on the hardware side and on the software side, making sure we're optimizing for AI for now and in the future. I think you mentioned, operational capital and we spend we don't do as much on the operational capital. It's not a significant part of our business, but we partner with folks like synopsis and other, EDA tool vendors for, for for things like design emulation.
So as we build out these architectures or these blueprints, we have to test and validate them before we actually start, shipping that. And so we spend, a bit on operational capital. But the vast majority of our business is on human capital. We don't do a lot by, by means of, contracting out a lot of that IP, because that's the lifeblood of our company is really about IP and licensing out those designs.
So much of the R&D is sourced in-house. And I think to Ravis point, one of the the key points that we see in the future is how do we keep pace with hiring the best talent so that we can really get ahead of, of the market?
I mean, for all three? I mean, is it fair to say that the R&D spend is with the end goal of building the IP portfolios kind of building and guarding the IP portfolios? Because that's another way that we try to look and and figure out, well, how do we quantify innovation? We look at patent filings across a number of different disruptive technologies.
And again this exclusive club fires files, a pretty staggering percentage of global patents across a lot of these, these themes. So I mean, is that what the end goal is when you think about R&D spend or is there something that I may be missing?
Yeah, I would say the end goal is solving high value problems and patents, trade secrets. All are a means to essentially supporting a sustainable competitive advantage. But today, because of the pace of change, we are in many cases doing R&D with the customer because it's the first time they're ever trying to do something in a certain period of time at a certain pace.
And innovation then, is at a furious pace. And so you don't have the luxury of saying, well, I'm going to look at this area, make some patents and then see what business can grow from it. It's almost like time is literally compressed and you need to innovate at a completely new pace.
Yeah, I'll double down on that. I think, innovation happens, co-creation with customers. You see that more and more at Autodesk. I guess we when we look at R&D, I can give you almost three different pillars. We have teams dedicated focusing on different things. We look at if you look at our customers, our customers, our engineers, designers and makers of all things around this world, we need to be able to see things really fast.
So we have teams that actually started thinking about how the world is going to be different ten, 20, 30 years from now, not just from a technology perspective, is just the general trend of what's going on with the world. Global warming, shortage of resources, etc., etc. sort of a macro economic point of view. Then you move down to say, hey, from my industry's perspective, whether it's architecture, engineering, construction industry, media, entertainment industry, manufacturing industry, how will these industries evolve to adapt as New World all the way down to sort of a near term, what customer need and want today, and how do we help them to meet their challenges.
All these three pillars, they're all connected with each other. And because reality is the products that we provide, frankly, the same thing for you guys, our end users are actually literally shaping this world the way we live. What you do best, mobile phone you, you use the environment you live or work in. And this is the power of technologies.
And how do we make sure that we're not shortsighted in terms of what customers need and want today, but making sure that we're helping our users to be ready for for the future? I think this is I think this is something that's I guess would be something different, because typically software companies don't necessarily have a macro economic, department or specialist to try to forecast what's going to happen in the world.
Yeah.
That's super interesting.
Yeah. You know, just to, to, just to kind of, level on the point here. I think it really stems beyond IP. I mean, I think it's broader than than that. I think if you sort of look at R&D, probably across all of our three companies, I would say we're probably solving some of the world's most complex problems, whether it's climate change or otherwise.
You know, and in order to do that, it's IP is, of course, core to all of our businesses, I'm sure, but it's also about delivering product, delivering on software code. It's all about quality insurance, making sure that you have you're able to keep pace again with that innovation. So it's really the entire R&D, lifecycle end to end, not just about IP, particularly.
Yeah, that's I mean, it's pretty much the definition of forward thinking, I think. Right. You know, in our industry, I feel like people get so caught up in the, you know, what's happening today, what's going to happen tomorrow with the markets type of mantra that, you know, even though we talk about investing for the long term a lot, at the end of the day, people just get too caught up in the day to day and the short term fluctuations and movements because there's so much noise right?
But when you're at arm or you're at synopsis or you're at Autodesk, you can focus maybe a little bit, maybe in some ways, solving those very complex problems makes that task of focusing almost a little bit easier in terms of being very long term oriented. And and when I think about sort of what's changed in the investing world over the last, let's call it 4 to 5 decades, the long term trend that we see in the data, right, is if you look at how companies accrue value today in terms of their market capitalization in the stock market, right.
We're talking to three companies that are all sort of in the $100 billion market cap club. How did they get there? It's mostly those things that come from R&D. It's IP, it's patents. It's what we collectively call intangible assets. Right? They're not buildings. They're not machinery. 50, 40, 50 years ago, 80 to 90% of market cap in the US was tangible assets, physical assets.
You could actually trace it back to factories, inventory, machinery, real estate, raw materials. Today it's a complete inverse of that. Maybe 10 to 20% of the value of the US equity market is tangibles. The rest is now intangibles. And so, you know, when you think about again now we're going to try to pivot to the topic of AI a little bit.
But Ryan started getting up at it earlier in in mentioning sort of other things that we track that signal to us that we think should signal to other investors why companies like these are successful today, why they're going to be successful for the long term. Things like patent filings. I'll give you one incredible stat that just blows my mind every time I look at it and update it, which is we have a patent data set where we map out around three dozen disruptive technologies, seven of which we consider to be foundational to AI.
And we track the patent filings every year globally for 20,000 public companies across those three dozen disruptive areas of technology, natural language processing, which obviously is foundational to AI, to generative AI, is the number one patent filing technology for Nasdaq 100 companies, 40% of all patents globally last year for natural language processing were filed by Nasdaq 100 companies, those 100 companies.
That's an astounding concentra nation right of innovation. And so when I hear Ravi talk about, you know, they spend 34% of their, revenue on R&D, that is the type of intensity and focus on innovation that predicts success in today's market environment. Right? If you actually look at the top R&D spenders and you start to build portfolios and you simulate their performance over the last 10 to 20 years, that top quartile of R&D spenders as a percent of revenue, they blow away the rest of the rest of the curve.
It's not just that they blow away the companies that don't spend on R&D, it's they blow away the ones that are spending a little bit to sort of keep up. But they're not leading right. They're not breaking new frontiers. So anyway, maybe that's a segue. I hope it's a good segue into, the next and probably more exciting even topic area of AI.
I think we can probably safely categorize 2024, as the year that I sort of broke through the general public in terms of all the different things that it is capable of and the different impacts that it might have in the coming decades. So I want to start with Ken on this. We were talking a few days ago about sort of the nature and flow of data within Autodesk's, software offerings and how that is changing as a result of AI.
Can you talk to us a little bit about, you know, how that is impacting your customers? How is it, at the end of the day, adding value, which is the million dollar question? The ROI question on are on. I still to this day.
Yeah. Thank you for that question. Autodesk. It's it's unique. We think of ourselves. Yeah. Traditionally you know we we we focus on three industries media entertainment architecture engineering construction operations and manufacturing. Collectively internally we call it the design and make industries that we serve. And within these industries we provide software solutions to multiple different personas.
And so you can imagine any product you can think of your smartwatch, your shoes. This building requires different, a whole platoon of different personas to come together and work to create this building the bridges that they build, the roads, the the shoes that you wear, etc., etc.. And what we done when we start looking at these things, what we've done, it's about, a decade ago we created our own cloud platform to make sure that all of the different personas using different software technologies can collaborate seamlessly and put their data open in a cloud.
So we have our own cloud platform. We I think now most of our products are on our own platform. In addition to that, we also moved away from files. We started to analyze the data that are being produced by various software applications. So the combination of our own cloud platform, which is the open, which is an open cloud platform, and the grand realized data really allow AI to produce insights and automations across teams, across department, sometimes even across industries like what I mentioned earlier, we serve the manufacturing industry and the, architecture, engineering construction industry.
These days, the way buildings are being built or bridges are being built very similar to how manufacturers building their products. So because of these two things, a combination of these two things, data can intermingle with each other. Most of our customers, when they think about a product, when they think about designing a product and ultimately bring the product to to the market, they got to worry about their design intent, performance requirements, material selections, cost, supply chain issues, manufacturing, limitations, etc., etc., etc. huge amount of data in each segment that I just mentioned.
Super complex. The product itself could be very simple, but to bring that to market is super complex and this is where I shines the most. And nowadays customers relying on our AI to really, generate actionable insights out of a very complex, huge amount of data and also identify repetitive workflows that can just be automated. And at the most importantly, our customers.
They want to save more time so that they can focus on innovation. They can focus on creativity. They don't want to spend time to think about which supplier should I call, the parts that I want for my product, whether the price is going to be right, whether there's going to be enough of a supply to meet the demand.
All these things take people's ability away. Take take people's, away from innovate. And this is where I can play a huge part within our within our, customer base.
You know, that's that's very interesting, almost sort of enabling even, even a further acceleration in the pace of innovation. That that, to me is a very, very clear ROI. Yeah. Thank you. Ravi, let's go to you. So we're talking a lot about design and simulation today. And specifically, with synopsis in the case of chips.
Talk to us a little bit about how you guys fit into the AI ecosystem. What's changed in the last few years? What is the most exciting thing that you see? This new technology enabling, digital twins, if you have time to cover that, I find fascinating. Would would love to hear about that, too.
Sure. So I agree with you. 2024 has been a seminal year in terms of looking at the AI landscape in so many different industries, and first just looking at our customers in so many different industries. The way compute is changing in those industries is dramatic, because more and more people are seeing the value of adding more intelligence into products.
And that means you need silicon and software. So given that whether you look at automotive, you look at medical, you look at industrial, you look at aerospace. There's a dramatic increase in the amount of computing needed and the nature of computing arms that the tip of the spear on that what we see is to create the chips that are needed.
That's where I have the ability to address some of the most fundamental limitations. But it's not the sole savior by any stretch. The innovation in the architecture of this chips is still the number one thing that's going to drive the right solution in the right industry, and that requires a combination of the right building blocks for compute, for IO, for memory, the right tools to design, verify and manufacture, and then ultimately the right capabilities to scale into the systems they are in, whether it's hundreds of devices or it's a fleet in a data center with a million CPUs.
So I we see is evolving from if you look at where we were a year ago and where we are today, the nature, how AI is coming into the design of chips is first as an assistant and then to help generate things to reduce the amount of work and then to optimize for power, for safety, for certain functionality.
So we're seeing a more refined evolution of AI as not that well, AI is going to build a chip, which is how often people think of how AI is going to change engineering. It's actually first going to be a knowledge assistant, almost a copilot. And synopsis pioneer the introduction of AI into electronic design automation starting in 2000, with the first patents and the work done there all the way through, introducing Copilots and now introducing the ability to generate some things instead of having to do them manually.
And then ultimately, we see doing the optimization based on what the end market needs. And so that's where a genetic AI comes in. But ultimately we are really at the beginning of the beginning, as I say, in how this is going to change. But the fog of war is slowly clearing and it's becoming clearer and clearer what role I can play.
And specifically, how can we make sure that the value is there as it evolves? But that's a journey in the AI world.
Thank you very much. I mean, similar question to you about, sort of how you see where ALM fits into the AI ecosystem, but also hoping that you can expand a little bit about just broadly what it means for the semiconductor industry. Going forward, how's it how's it going to change the industry long term from your view?
Yeah. So from an ARM perspective, AI has been a core driver of the business. And you sort of think about why that is. If you look at kind of the, you know, probably before the ChatGPT moment before 2022, I was constrained. It was, you know, in a few different use cases, mostly in research. And now what you look at posts that both post the world waking up to ChatGPT is really AI has been become pervasive.
And so why, you know, what's the impact that it has on ALM so with AI becoming pervasive, you need those language models to run on compute. So whether it's a large language model or a small language model, whether it's in the data center, whether it's in the palm of your hands, whether it's in your home, you need those language models to run on compute.
And so that's really what's changing in the world of semiconductors, is you're going to need a ton of more compute, and you'll need high performance, energy efficient compute. And that's really where ARM comes in. So it's been a huge growth driver for our business. It's been a great tailwind for us in terms of where I see AI going forward.
You know, if you look at the last I've been in the space for quite some time and you look at, you know, 2022 and up into 2024, it was really I think the conversation was all about models, small language models, large language models that scaling lots of language models. And so that's really been kind of the conversation.
Now, what I see in 2025 and beyond is it's all about reasoning. It's all about being able to use AI and make decisions. So the whole world of AI is what I believe will really take shape. And in 2025, we will see AI making those decisions on our behalf and not necessarily replacing humans, but augmenting a lot of our capabilities.
And so that's really what I see. You know, from in terms of AI just being a concept or just being all about language models to now having practical applicability in pretty much everything that we do. In fact, I was reading recently an article that said, by 2028, 18% of business decisions will be made by AI agents.
And so for me, that's hugely impactful. I think I look at AI and I believe it's a once in a generation opportunity for us, probably for many of us that will never have the opportunity in our lifetimes again. And so I'm super excited about what's to come. And I believe, like you said, we're just in the infancy. We're just getting started.
It's going to create new industries. I think the compute paradigm has completely shifted, and I think semiconductors, you know, in the past, I think semiconductors have been really focused on kind of, some areas of the business around smartphones, around PCs, but now it's much broader. If you look at electric vehicles, if you look at autonomous vehicles, so you look at the complexity of what's needed.
It's all about more compute. And so I think semiconductors are really at the forefront of that innovation. And I think believe AI is really going to drive that.
Yeah. I mean, as a as a financial analyst, I think it's fascinating to to consider an industry like Sammy's, which is now, you know, not a young nascent industry. It's Intel IPO in 1971. First, you know, first IPO on Nasdaq, sort of kicked off the trend of big innovative tech companies listing there. But it's been known as this sort of famous, famously cyclical industry.
And it's because the, the end users, the destination markets for chips always used to be fairly discreet and would run on these cycles, right. Desktop computers and then laptops. And it would be correlated with how frequently, you know, a windows update comes out and how fast the machines deteriorate. And nowadays it's just so pervasive, so ubiquitous, that it feels almost like there is this transition going on from an industry of cyclicality to an industry almost being more like like an enterprise staple.
Maybe if I had to think of a new terminology, right. We think of consumer staples, things that people need every single day and can't live without. Toilet paper, toothpaste. Right. They're never going to stop buying, is it? Is it really possible that, you know, when we're on the other side of this AI advancement wave and we reach AGI or whatever, the closest approximation of AGI is that we're just going to be in a secular semiconductor industry.
That's been transformed, where the demand is just ongoing and eternal to you or to to Robbie or to anyone.
I mean, I can go on my my point of view. I think I think you're right. I think semiconductors used to be cyclical, but that was mainly because it was localized in a in a few areas of the business. But as you mentioned, that's now become pervasive, I think as, as intelligent things become more complex, you're going to need more compute.
And so I think semiconductor, the whole notion of semiconductors is going to change. And you're seeing that right now, and it's going to become, to your point, the staple of how we think about innovation. I mean, we're not hindered from kind of the cyclical headwinds. We'll continue to see that. But I think it's much broader in terms of the sea change that we're seeing of semiconductors than we have in the past.
Yeah, I would agree. And maybe just a interesting perspective. For almost two decades, the world has been on a windows x86 hardware software contract. When mobile came in, that contract slightly changed. Actually, ARM was a pioneer in changing that contract for one reason power efficiency. Then when we look at the next stage now, in order for this secularism to happen in terms of semiconductors, it's another power challenge.
You know, ChatGPT takes ten times more energy than a Google search. So in order to make the reality you describe, the power equation is so fundamental to make that pervasiveness happen. So I think I agree, and I think there's a big challenge ahead where we got to innovate through that.
I love that framing. Thank you. Thank you so much. We're we're just at time. So I think we'll leave it there.
Even though we could go for another probably hour easily.
Well, we can set something up this evening. Yeah.
Go after Jensen. Well, thank you to to to you three can Ravi. I mean this is so enlightening. And honestly, I think it helps to bring kind of the story that my co moderator Mark and I talk about. It's bringing the index and the investing and not just kind of a group of companies, but looking a little bit deeper into kind of the problems that they're trying to solve, the products that they're they're getting off the ground.
It brings that story to life. So I appreciate wholeheartedly, your comments today and your time. And thank you, everybody, for the time today.