Technology Untangled: 2024 Untangled

Speaker A: When you go underneath the technology and the potential it has, it's just mind blowing. That comes with opportunities and challenges because we need to make sure this technology is inclusive, is safe, is responsible, is ethical, but the technology has tremendous potential. I will say I think this is the biggest disruption we're going to see, at least in my generation.

Speaker B: 2024 has been quite a year. From elections and changes of government around the world to war, to lingering economic uncertainty, for the last few years it's felt like the world is in a constant state of flux. But with uncertainty comes opportunity. Opportunities to find new competitive advantages, opportunities to do things better this time around, and opportunities to make the most of the incredible pace of technological changes bubbling under the surface.

Speaker C: So as we wave goodbye to 2024 and enter the middle of the decade, we're going to be examining the changing pace of technology, how leading figures from across the spectrum are maintaining a competitive edge and what lies ahead in the near future. You're listening to Technology Untangled, um, a show which looks at the rapid evolution of technology and unravels the way it's changing our lives. We are hosts Michael Bird and Aubrey Lovell.

Speaker B: 2024 has been a year of opportunities and challenges. There was uncertainty in certain global markets, fear of another tech bubble, and geopolitical unease with elections in Russia, the uk, the US France and a whole host of other major economies. So what's the feeling been like among leaders in the tech field? Antonio Neri is the president and chief executive officer of Hewlett Packard Enterprise.

Speaker A: Obviously we went through a heightened geopolitical environment which I think has gotten worse. An economic downturn because of COVID Uh, we also faced some social injustice, I think. You know, here in the United States, obviously, uh, the election year is always an interesting year, but 2024, from an IT perspective, I think the biggest theme is obviously AI and how much ground has taken in terms of thought and opportunity for any enterprise to transform the businesses. And then the ability to play in that space has been an area where we spend a lot of time, not just with the management team, but with the board. The reality is that when you go underneath the technology and the potential it has, it's just mind blowing. M that comes with opportunities and challenges because we need to make sure this technology is inclusive, is safe, is responsible, is ethical, but the technology has tremendous potential. I will say, at least in my generation, this is the biggest disruption we're going to see.

Speaker C: Now. It won't surprise you to hear that AI is something we're going to be Talking about quite, quite a lot in this episode. Because if 2023 was the genesis of generative AI, 2024 was the year it became part of our uh, collective consciousness. In just one year the AI market grew from $135 billion to $184 billion. That's a 35% rise in just one year. That's according to a report from Statista which we've linked to in the future show notes.

Speaker B: Through the medium of chatbots, AI note takers, sales automation and virtual personal assistants, generative AI became something we weren't just hearing about, we were using ourselves in our everyday lives. It's a funny feeling because it sometimes feels like while AI is exciting and new, it's embedded itself so quickly. That's led to its own challenges in particular around how to enable the greatest number of people globally to, to get fair and equal access to AI.

Speaker C: Yeah. And uh, that in turn has brought together businesses, governments and um, other organizations to try and make sense of something which has changed society in the blink of an eye. One of the people spearheading this international effort is Kathy Lee. She's head of AI Data and the Metaverse at the World Economic Forum.

Speaker D: As you know, during the past couple of years, particularly with uh, Genai, we see there's a potential for the already existing digital inequality in terms of access to uh, digital devices and services will be further exacerbated because particularly with Genai, with the need for the model to be trained on a large set of data and also have access to uh, the high performance compute. A lot of the times I'm not even talking about like smaller and medium sized business for many countries and regions they, they don't even have the access to the compute or uh, the data sets that's needed or access to talent. So we see that as a big gap to be addressed and we're hoping at the forum, uh, leveraging the work that we just launched called uh, inclusive AI for development and growth to really work region by region to see exactly how, what are the top priorities of the region. Let it be data, let it be access to talent, compute innovation. Is it about the industry, is it about the governance? Always some of the underlying uh, type of uh, common frameworks that the forms AI work particularly centered around. AI governance alliance has been building and built that kind of regional national resilience within the local ecosystem.

Speaker C: Is it trying to make sure that actually across the world different regions and different countries, different organizations will have access to this sort of technology?

Speaker D: Exactly. Because AI is such a Ubiquitous technology. But you cannot do that unless you actually truly democratize the benefits, because otherwise, countries, regions, they will develop the AI, uh, systems without the common type of framework. In the forum we started the AI Governance alliance since 2023, really aiming to shape the responsible AI governance.

Speaker B: Uh, so it seems that 2024 has been a year of grappling to stay on top of new technologies in order to maximize future opportunities. And that's not just true for our organizations and governments. It's also true in the sporting world, where the ability to adapt to changing environments, new rules and a new landscape can be the difference between victory and defeat.

Speaker C: Yes. And, um, Formula one is a prime example and it has some analogies to the rapidly evolving AI, ah, landscape. The sport is facing new rules and new engineering challenges which all the teams comply with. But within that common framework, there are opportunities to stand out and, of course, thrive. Major rule changes are coming to the sport in 2026, which will see new fuels, increased use of electric powertrains and more. More. The teams have been held off developing their cars until January 2025, but now it's full steam ahead to eek out every fraction of a second from every component within the new regulations. As George Russell explains. George is a driver with the Mercedes AMG Petronas Formula one team.

Speaker E: When it's a new regulation, it's like digging for gold. You don't know where you need to dig. You might find a little bit here, so you pursue, but you don't know if you dig over there, there could be a lot more to be found. So you don't really know where you're digging. And you've got to trust that hole is, is going to give you the gains. But, uh, it will only be when the car drives for the first time. You can correlate the data, um, back to the wind tunnel, back to the CFD and all of those simulations that we're, we're doing to then make further gains. But yeah, uh, those 14 months of development, you're kind of digging into the unknown.

Speaker D: Yeah.

Speaker C: And I guess there's an element of like, if you develop this thing, it might impact something else. And, uh, yes, that's something, you know, I don't know. If you have new suspension, it might actually be less aerodynamic and.

Speaker E: Yeah, absolutely. It's. Why is using the jigsaw analogy? You could have the nicest piece from the jigsaw puzzle, but if it doesn't fit with the other 1,000 pieces, you're not going to be able to complete the puzzle. So you're looking at trying to make the best puzzle possible. Yeah, and sometimes you have to compromise here and there. And it's not about just having the best front wing. You need the front wing that works the best with the floor, with the shape of the body, work with the rear wing. And it's almost like teamwork. Yeah, we always talk about teamwork, but that's how a car operates. The suspension has to work with the engine, with the aerodynamics, with the driver, with the tires. That all has to be at one.

Speaker C: George mentioned putting together pieces of a puzzle here and that seems to be a theme going into 2025. Because in some, in some ways, developments in computer technology are a lot like a race car. Every part has to work in harmony.

Speaker B: In the last couple of years, compute has been at the core of the generative AI boom. But that's only one piece of the proverbial jigsaw. Alongside that, you need to get your data in and out of the system. After all, even the most powerful high performance computing solutions are only as quick as their data pipeline. That's why going into 2025, networking and connectivity are at the forefront of Antonio Neri's mind.

Speaker A: When you think about AI or cloud or hybrid, you need a network that can scale and that network needs to scale for devices and users, ah, at the edge, all the way to very large deployments of AI, uh, accelerated computing. And in between you have all the workloads that normally run your enterprise which, which are going to have more and more AI embedded into it. And in order to manage all that complexity, you need a networking fabric that can scale. And so I do believe with the acquisition of Juniper Networks and the amazing work we have done with hp, uh, Aruba Networking, we're going to deliver to customers the most modern AI driven networking fabric. And that's why I think, uh, the networking business will become core to everything we do. And our goal is to continue to drive that innovation, both organic and inorganically. Example of, uh, Juniper at the Core foundation or Morpheus in the hybrid cloud control plane and all the things we have done in the last seven years.

Speaker C: It sounds like these acquisitions are very much like building the foundations for the future, particularly when it comes to AI. I guess we don't necessarily know where our AI is going to go because it's moving so rapidly. So is that what we're doing, sort of making sure we've got the tools we need to be able to deliver things to our customers?

Speaker A: Yes, but at the same time with an eye, uh, where we can add the value and monetize the value for shareholders. The reality is that I think 25 has to be the year where enterprises are moving from early experimentation to significant adoption and we start seeing signs of that. But I don't think every enterprise segment of the market is running at the same speed. So HP has a uh, a big responsibility in many ways because we are an enterprise driven company to begin with and we have the opportunity to help customers adopt these technologies. But so far all the capex, all the action, all the attention has been on fewer companies that have achieved some amazing breakthroughs with artificial intelligence. But the models need a lot of development. The technology stack needs to continue to evolve. But I will say the conversation with enterprises helped me realize the value of this technology.

Speaker B: This realization that AI and compute is only a part of the story is going beyond individual enterprises though and becoming an international point of discussion. While organizations like HPE are looking at improving the tech stack, over at the World Economic Forum the conversation is around the usage stack, essentially the best practices and if necessary, necessary standards and regulation which would help global AI adoption run as smoothly as a great network. Here's Kathy lee.

Speaker D: So in 2025 our focus will shift to um, addressing the critical gap between the uh, ideals of AI governance and uh, its implementation in practice. So what we meant by that is this year we are focusing on developing that type of uh, blueprint for intelligence, uh economies that's uh, aimed at the global audience. And we've laid out eight enablers including um, data availability and accessibility, including access to compute talent, innovation etc. That I just mentioned. But we don't want to stop there because this challenge of inclusivity in AI and also by um, example extension AI, uh, sovereignty needs to be tackled at regional level because different regions would have different needs. And I'll also give you a uh, specific example in terms of why there's such a need for regional um, collaboration. The status in terms of how we develop those powerful AI ah, models, you cannot even entertain the idea for every single country to be able to build the data center as they wish and have sustainable and uh, consistent clean energy supply to power those data centers. And most of the regions, despite the aspiration to develop the very uh, local type of language models, many countries wouldn't have enough data sets within its own jurisdiction. So you will need to partner with other regions to build some sort of AI corridors to allow for one country to build a data center, but be able to share that data across jurisdiction with others. Because right now there's a lot of data privacy and also a data localization, um, requirement that's preventing that from happening.

Speaker C: Do you feel like that collaboration is going to happen? And maybe as an extension of that, do you feel like that collaboration is necessary for the growth and the proper usage of AI?

Speaker D: I've always been an optimist, so I think my answer is yes. I do think, uh, we've gone so far and even though we as humans, the objectives are different, the backgrounds are different, we at least have come so far, uh, leveraging all of the collaborations that we could have in place. And with AI, I think what's good is we were able to recognize some of the challenges way earlier compared to the previous waves of digital transformation. But it definitely brings out a lot of nuances because it's also very different from the previous, uh, waves of technology transformation. But again, from my vantage view, we've definitely seen a lot of the collaboration across different sectors, across different jurisdictions. So, yeah, I am very, very hopeful.

Speaker C: You would think that the arrival of exciting new technologies that can aid us in our everyday lives, make us more efficient and improve our, uh, performance would make life simpler. But as Cathy and Antonio have shown, that's not always the case. There are more parts to the puzzle. The same is true in racing, and it provides a useful analogy. Improvements in technology improve outcomes, but it comes at the cost of simplicity. As George Russell explains, in the past,

Speaker E: cars were far less sophisticated. Yeah, they didn't have all of the data points, they didn't have all of these switches that we see on here to change your, your brake balance, your differential settings, your engine braking as you, you turn into the corner. But because they are so sophisticated, there's a lot more opportunity for it to go wrong.

Speaker C: Okay.

Speaker E: So I would argue that it is harder than ever because it is so difficult. You got all of these different toys to play with.

Speaker C: Yeah.

Speaker E: And if this one setting isn't right, that's going to have a big impact, the car's performance. Yeah. So you're, you're having to manage all of these different settings and people sometimes think, ah, uh, these settings just make it easier. No, they don't. They make it easier if the car's in the right window and it's performing correctly. But if it's not performing correctly, you've got to try and use all of these things to your advantage. Whereas in the past there were less things on it to go wrong, if that made sense. So to reach the full potential was much easier.

Speaker C: The mental load there must be phenomenal.

Speaker E: It definitely is when you know you're in a race, you're trying to drive fast, you're managing all of the switches on the steering wheel, you're managing your tires, you're managing the brakes, the temperature of the engine.

Speaker C: Yeah.

Speaker E: Thinking about when you're pitting, thinking about attacking the driver, defending from the driver. There's a lot of things you got to be aware of to get the most out of the package. But with time, with experience, a lot of it becomes instinctive.

Speaker B: It all comes back to putting the pieces of the puzzle together again. If you get it right, whether your tech is baked into a race car or a data center, the benefits can be astonishing. But it's a much trickier jigsaw than it was just a few years ago. We've gone from the equivalent of a few moving parts to thousands, all of which need to fit to get the most out of each other and work seamlessly.

Speaker C: That's something I wanted to ask Antonio about. Now, we've already mentioned networking, which is one of Hewlett Packard Enterprise's big focuses going into 2025. But what else is on the horizon in terms of completing the AI tech puzzle?

Speaker A: Quantum computing is an example. Quantum has tremendous potential, but we have been chatting about quantum for a number of years, and I don't believe yet has reached the, the peak where everybody can use quantum. And the problem is, number one, the scalability of the technology, and number two is the ability to manufacture this scale. So the manufacturing process still too expensive compared to traditional type of computing. But the interesting part is that we have already seen a use case where HPE has a supercomputer connected to a quantum computing. And the quantum computing is acting as an accelerator of the supercomputer, not as a replacement for the supercomputer. So if there are certain runs or algorithms that must be accelerated way beyond what the supercomputer can do, then we invoke the quantum to do that specific little job, but very proprietary in many ways to accelerate that. And so that's what we have seen so far versus 8 quantum replace supercomputing. And that's not yet possible because the economics don't align. It's going to create some interesting challenges, but, uh, as always, right, it's fine to be in this industry and driving the next disruption. Uh, just recently we announced the first industry 100% fanless direct liquid cooling. And we are not thinking just the gpu. We're thinking about the entire system. So I think that's, uh, an opportunity for hpe.

Speaker C: So you talked about liquid cooling or direct liquid cooling. So liquid cooling, a data center, that's not particularly common at the moment, is that correct?

Speaker A: Generally speaking, it uh, has been all air cooled. And that's because the number of transistors that we have on a chip, it was not as big as we're going through accelerated computing where we're going to billions and billions and billions of transistors versus millions in the past. And so the power density of these chips will require a different technique to cool the systems down. It's no longer the ability to just pass air through the heat sink and then, you know, push the air out. That's why we went complete directly with cool without any funds. And there are many benefits. First of all, from a uh, direct liquid cool perspective, 100% of it to 100% air cooled, we actually can demonstrate a 90% reduction in energy. So that's pretty significant. Second is obviously all the carbon footprint which is reduced dramatically. And then the third piece of this obviously is because of the density of the system, we can reduce the space that we occupy in the Data center by 50% because you don't need as much space to correct, to fan the air through it. That alone is significant CAPEX and OPEX benefit for the customers.

Speaker C: It seems like an exciting time in computer technology. Liquid cooling might not sound as exciting as quantum computing, but it's a huge piece of the puzzle. After all, figures from the World Economic Forum indicate that at current growth rates, Europe's data center power consumption is expected to reach 150 terawatt hours by 2030, uh, from approximately 62 terawatt hours at Ah, present. And that's going to need over $250 billion in investment to keep up with demand. And um, we've linked those stats in the show notes.

Speaker B: Anything that cuts costs either in setting up the system or its energy usage is a big deal. But with all this new computing power, what are we going to be doing with it? Well, it's something that Kathie Lee is excited by because we're starting to venture into the realms of AI, which can, well, use AI. And that unlocks a whole new load of possibilities.

Speaker D: I think in terms of the technological advancement. The biggest um, change you have probably seen all over the press coverage is the mentions of agents because the models are only as powerful as the data input that it has. But it's a very general purpose, um, model and for most of users you actually need to know exactly what you're asking for in order to get the answer. That's where the uh, agents concept come in because agents, you can think of them as softwares but I'll also explain it's not softwares. They are meant to be built on specialized uh, type of models trained by specific data sets and be able to perform certain tasks without uh, much of the human intervention. So that's what will make AI models much, much more powerful and useful. We're training them to think independently, we're telling them actually to take a few seconds to think instead of just coming back to us with some suggestions right away. That on one hand unlocks huge excitements and uh, opportunities. But on the other hand that also brings a lot of complex uh, challenges. If the multi agent system, two agents decided to make a decision if something goes wrong, whose responsibility is that? Is that one of the agents? Is that the humans that's behind that, enables the agents. So that's what we think. That's going to continue to evolve uh, in the new year because if agents work, you can imagine the robotics uh, will have a much bigger uh, breakthrough in the near uh, term as well. So again the sky is the limit. There are still a lot of hit and miss. But we've already seen AI showing such promises in drug discovery, in energy transition, in uh, financial services and healthcare. That's another big sector and industry where we can see, you can imagine as we unlock the health data collaboration, how much better patient outcome there could be in leveraging AI to truly help patients with uh, diagnosis, with uh, healthcare delivery, hospital services, et cetera. So yeah, those are all the things that's keeping me uh, optimistic.

Speaker B: It's incredible to hear how quickly the world is changing and how quickly we're all having to work to keep up. Whether it's through business innovation or international consensus, it's clear that collaboration is key to success and in the coming years. Simply put, we can't do it alone.

Speaker C: Yeah. And that's why the World Economic Forum is looking to foster international collaboration, to connect people globally and um, democratize technology once again. Motorsports is a, ah, useful analogy for what's going on in the wider tech space. You have to work in harmony, bring in expertise from different areas and unite under one banner to succeed. In short, and we all know this, it's all about teamwork. Here's George Russell.

Speaker E: It's like you're running a marathon. You can't just go flat out from lap one because your car and your tires, that's not the quickest way to get from A to B. Same way as running a marathon. You've got to pace yourself. But what's different is that we get the Pit Stop. So when you exit Pit Stop, it's like you've suddenly got the same energy back that you had at the start of the marathon. So as a driver, sometimes as you're nearing the end of that, that marathon, you're like, you know, I'm really struggling. I want to. I want to stop now. But they. The strategist may be saying, well, if you pit now, you're going to be stuck behind another slower car. So there's a lot of trust you've got to put in your strategy team. And it's very difficult decisions to be made in a very quick period of time.

Speaker D: Yeah.

Speaker E: So the pressure is definitely on them to do that. And then the Pit Stop team, we've got three people per tyre, so that's 12 people front jack, rear jack, 14, two people holding the car 16. And then sometimes we have people just in the front wing. So, yes, I say, uh, up to 20 people on the Pit Stop. You know, we can win or lose a second in the Pit Stop as well. So they're practicing so hard. You're looking for every last millisecond to

Speaker C: 1% here and there.

Speaker E: Yeah, absolutely. And it's. It all adds up. If you find 10 milliseconds in 100 different places, you know, suddenly you found yourself a lot of. A lot of lap time.

Speaker C: Now, there's a few times in this podcast where we've been able to draw parallels between motorsports and the tech industry. And it's true there are similarities, but there are also differences. Of course, one of those is in searching for those incremental gains, a fraction of an improvement may win races in Formula one, but that's not enough for a hpe, As I quickly discovered when I asked Antonio about finding those nuggets of improvement that George was talking about a moment ago. I'd love to sort of talk about the incremental gains so that, like, 1

Speaker A: or 2% here and there, 1% is not enough. I'm subscribed to the power of getting a 10% improvement every year. Uh, should be totally doable.

Speaker C: Yeah.

Speaker E: Okay.

Speaker A: If you think about just the quality principles, if you have a process that has 10 steps and every step is doing exactly what's supposed to be doing. The reality you're performing only uh, at 90%. So it's not just the improvement of within the step, but it's improvement across all steps. And so this is where, you know, uh, I think we are on the part of our journey that, yes, we can continue to extract more efficiencies in each of the processes, but I think our biggest one opportunity as a company is to streamline the end to end processes.

Speaker D: Yeah.

Speaker A: Because in many ways we still have areas where we're still too complex to do business. We're still a little bit more heavily, uh, dependent on people. And that's why one of the biggest agenda we have is to digitize and automate as much as we can and then streamline those business processes to the minimum amount. Because that drives a better experience, a better quality, lower cost, and then ultimately, from an IT perspective, also simplifies the environment because I don't need too many applications to do what I need to do, but I can do it with less apps in a more resilient and more modern way, I would say. Right. So these are the things we drive, but we have many examples, you know, improving our forecast supply chain processes as a services. Right now, every improvement on first time fix, uh, when somebody calls you, is a huge benefit. We never done. That's the reality. The message here is you're never done.

Speaker C: Constant improvement.

Speaker A: Constant. Now this is what great cultures do every day.

Speaker C: Well, you can't argue with that. But whether you're chasing 10% efficiency gains or 1%, whether you're looking for downforce or looking to bring together global communities to collaborate, 2025 is going to be an, um, exciting year. It's set to be a year where to bring back the puzzle analogy, the pieces of a technological revolution all start to fit together.

Speaker B: But there's a roadmap for all of that. Strategies exist and 2025 is going to be the year of execution. However, that doesn't mean there aren't big questions left to be answered. So for our guests today, what are the known unknowns they will be dealing with in 2025? For Antonio, it's all about the economics of AI.

Speaker A: Well, I think there is a lot of questions every day when you read the news or watch the news, is how this technology is going to be monetized. So I will say, first of all, it's super capital intensive and shareholders want to know that if you're deploying large amount, large sum of capital, that you have a good return on it. If you think that this technology is going to transform trillions of dollars of industries, then how much capital you need to deploy to transform those industries and what is that return? And there is a scenario you're going to invest more than $100 billion here very soon. And we don't want to repeat what happened in the telco space in the late 90s, early 2000, where we laid all this infrastructure and then nobody was, uh, able to use it completely.

Speaker C: I would say it does feel a little bit like the early days of, um, perhaps social media, where there was rapid development but nobody quite figured out how to make money from it until advertising came in. Until advertising came in. And so it actually changed the product.

Speaker E: Yeah.

Speaker C: So, yeah, it feels like quite an interesting time.

Speaker B: For Kathy, the big challenge is driving those ongoing efforts to bring people together and achieve the rarest of things, consensus.

Speaker D: I do think collaboration is extremely important and it is difficult. I'm not saying that it's easy. That's our role. We are a platform at the forum. We aim to really get everyone to collaborate. Public, private, uh, partnership and. But it's not easy because we're humans, we have our own priorities, agendas. It's not always aligned, but I do think when it comes to bigger issues, humans have ability to come together and work on practical solutions, even though it's not always a linear path. But how do you figure out how to incentivize, uh, different parties? And part of it is leveraging, uh, what they care about the most. That is very, very important. But it's also difficult to do. So that's what's keeping me up at night. But I do think we have quite a bit of success in terms of doing that.

Speaker C: For George Russell, the known unknown challenge of 2025 is something a little closer to home, though it'll still involve a lot of international travel.

Speaker E: What keeps driving me is the dream and the desire to become a world champion and never knowing when that opportunity may arise. I had it in 2020 when Lewis unfortunately got, got covered and the call came, they said, you're in the car, and went out and led every lap until I got a puncture.

Speaker C: I was rooting for you. I remember watching that life and I was so, so distraught when that happened. Yeah, yeah. I think the whole country.

Speaker E: No, absolutely, absolutely. It was, it was incredible weekend. And to be honest, I probably wouldn't change it for a thing because moments like that, it molds you as a person. But, you know, I'm 26 years old, I've got a long time left inside of me, so I'm motivated to stay fit, stay strong, keep working hard with my team, because maybe next year we go on a run for three, four, five years, we just never know. And it was only when I looked at Michael Schumacher's career, I didn't realize he didn't win his first championship until his. I think it was his fifth season. So I'm in my third season with Mercedes. It may take five years for us to win together, but, you know, he kept on m believing, and then he won five in a row.

Speaker C: All of our guests today come from different fields. They have different priorities, experiences, and ambitions. But one thing unites them. The desire to push for better. To see a North Star and then aim for it. That's not a goal that reset as 2024 became 2025. It's a, ah, constant driving force. But, uh, as a New Year's resolution, it's a pretty good one to be inspired by.

Speaker B: We'll leave you with this final thought from George Russell. Happy New Year, everyone.

Speaker E: Never be afraid to fail. When I was younger, at least, I never wanted to make a single mistake because I recognized how much hard work and sacrifice my parents were putting in to give me this opportunity to go racing at the weekends. And I felt any mistake I made, I felt like I was letting them down. Yeah, uh, so I never wanted to do that. But there was a day I was thinking about my, my approach and realizing, if I'm never making these mistakes, I don't know how close I am to my, my ceiling. I don't know how close I'm. I'm pushing myself. I wanted to push myself to see how far I, I could go. Mistakes crept in m. But I never regretted them because I learned so much from these mistakes and these failures. So I sort of stepped beyond the line and then I just brought it back now. And, yeah, now, uh, I've got a much greater understanding, um, of where my, my limit is. Yeah, people used to say, oh, uh, look how great he's not making any, any mistakes here or, you know, had a flawless season or whatever, but I knew deep down that I actually had a bit more left in the tank. So don't be afraid to fail.

Speaker B: Um, you've been listening to Technology Untangled. We've been your hosts, Michael Byrd and myself, Aubrey Lovell. And a huge thanks to Antonio Neri, Kathy Lee and George Russell. You can find more information on today's episode in the show notes.

Speaker C: Yeah, and don't forget to check out our sister podcast, Technology now for a weekly hit of technology news and, um, stories that we think you should know about now. This episode was produced by Sam, um, Datapaulin and Alicia Kempson Taylor, with production support from Aubrey Lovell, Harry Moore, Zoe Anderson, Alison Paisley, and Alyssa Mitry.

Speaker B: Our social editorial team is Rebecca Whissinger, Judy Ann Goldman, Katie Guarino. And our social media designers are Alejandra Garcia and Ambar Maldonado. Technology Untangled is a Lower street production for Hewlett Packard Enterprise.

Speaker C: And from everyone at Technology Untangled and, um, Technology Now a happy new year.