Thanks, Tom. Let's look ahead to our 2025 roadmap now and cover what I like to say the key three things that really matter. This may sound a bit like a broken record, but first, it's fundraising, where we've set out a net target to grow our fee-earning AUM to over $40 billion over the course of the year. Our fee revenue and earnings growth are more closely correlated to our active FEEUM over the period, so it's a better proxy for our financial performance. And look, this year, we're going to make sure that that cadence is in-line with our fundraising that tracks for FRE and that it's predictable and it's easy for you, our investors, to understand and that you can bank on us in terms of being able to predict that earnings potential and that earnings growth throughout the year. Taking FEEUM from $35.5 billion to over $40 billion over the course of the year will involve finishing fundraising for our third flagship and second credit strategies. At the same time, we'll be launching two new investment products and continuing to build on our initial success in tapping the private wealth channel where Andrew Cox has done a great job for us. When it comes to investing, in addition to building out cloud and AI training data centers, we're starting to see customers look ahead and around the corner and prepare for the next phase of AI, inference, where location matters and performance across the entire network matters. I'm going to talk a little bit more about this in a few pages. Finally, as I said earlier today, continue to scale our platform DigitalBridge. Tom just walked you through our guidance and I believe we're in a great position to continue to deliver the double-digit earnings growth and expanding margins that are central to our investment thesis. This really is the year where we scale and most importantly get efficiency in our platform. Next slide, please. So, when we look at 2025, it's going to be all about continuing to scale our multi-strat platform. As you can see here, we've mapped out our fundraising cadence by product over the course of the year, so there's no mystery to where we're doing our fundraising. The first half of the year, as I said earlier, will be focused on finalizing capital formation around our third flagship DigitalBridge Partner strategy, as well as our second credit fund. Then, as we get into the second half, you're going to see new strategic capital formation initiatives we've been developing kicking high gear, including our second private wealth offering and strategies built around digital energy and stabilized data center assets. We think these new product offerings are not only natural but strategic and really offer the potential to scale in size over time as we've been actively engaged with TLPs to architect these solutions that really fit the clients' mandate, that's really critical, bringing products to market that LPs want and that are topical and have the secular tailwinds that investors are really craving today. Throughout the year, our co-investment program will continue to form capital around some of our best new ideas that we formulated in our third fund. Whether it's JTower, [OBM] (ph), or Yondr, these are the next great platforms that we're scaling at DigitalBridge, and they will require co-invest capital and we will deliver on forming that capital. So, when you take a step back, between new capital formation and investment realizations, we see this multi-strategy program generating $4 billion or more of net growth, bringing our FEEUM to over $40 billion by the end of next year. Next slide, please. So, when you flip from fundraising to evaluating the investing landscape in 2025, one of the big questions out there is around how technology advances, like DeepSeek are impacting hyperscale CapEx, and more broadly, the investment in the AI infrastructure ecosystem. Interestingly, when we updated our 2025 CapEx targets to the big five hyperscalers over the past couple of weeks, the total is actually 20% higher than it was just six months ago, rising from $250 billion to over $300 billion. So, the velocity of what's happening in terms of CapEx for our customers is not slowing down, it's actually increasing. Serving cloud and growing generative AI workloads is generating very good incremental margins for these companies. So, they're motivated to build the capacity to meet demand and avoid hitting bottlenecks in their business cases. And look, everyone thinks it's all about data centers. That is simply just not true. It's about an ecosystem. You need the delivery mechanism to bring generative AI to devices, to IoT networks, to autonomous vehicles, to mobile phones, to wireless utility meter readings, your refrigerator, all of this is about the delivery and the promise of AI, not about building the biggest data center in the middle of North Dakota or Iowa. This is really about an ecosystem. We learn this in public cloud. So, look, it's worth noting these numbers don't include some of the emerging players that we're starting to see become more active, particularly OpenAI and some of its partners. Let's turn to the next slide to understand some of the drivers of this incremental demand. Look, in the media, you've seen this active debate over the past month or so around who is right and when it comes to the direction of travel on AI investment. And I'm not here to tell you who's right. What I am here to tell you is I've been doing this for three decades and I've been building infrastructure since the early 1990s. And what I can share with you is, on one hand, investors are talking about the need to accelerate investment to scale compute. The Stargate Project got a lot of attention outlining its goal to invest up to $500 billion over the next five years just to support the needs of one customer, OpenAI. On the other hand, you contrast that with the concern around the impact of a model like DeepSeek, which appears to materially lower the investment necessary to develop high-performance large language models. While we've never been able to validate the cost to build DeepSeek, there are valuable lessons to be learned in its protocol and the language that it uses to perform. Our perspective is that actually, both sides are right. You don't have to choose. And actually, this is really just about how the technology works. And that's captured well by Jevons paradox, which many of you become familiar with as the debate has evolved and played out in the public forum. Here, we've overlaid the cost of compute, which goes down on a per unit basis over time, which is natural, stimulating consumption and demand for more compute resources. None of this is really new news for us. DeepSeek is simply the latest development in a trend towards improving efficiency that was honestly already in place. It's a trend that ultimately is only good for stimulating demand, which we believe is one of the reasons you seek hyperscale investment that we've outlined on the prior page, and it's only accelerating. There, building to meet the demand, driven by the natural and necessary efficiencies of technology and breakthroughs that we're seeing across the entire AI ecosystem. Next slide, please. On this slide, you see in practice how lower cost actually drives demand for more better and faster compute over time. On the left side, there's great detail on the generative AI large language model cost curve, which has been decreasing exponentially. Here you can see DeepSeek on the lower right part of the graph, and that's only part of the story. While on the right-hand side, you see successive waves of technology adaptation that in every case have been catalyzed by cheaper, more ubiquitous compute, bandwidth, and connectivity. And look, again, trust me, I've lived and managed digital infrastructure businesses through all of these cycles. We were there in the early '90s building the first mobile network towers. We had a business that built the first fiber networks in 1997, post the Telecom Reform Act of 1997. We were also really critical in the early 2000s, building critical 3G and 4G infrastructure that really provided the backbone of mobile data, which fueled exponential growth and investment. And now, as we look at how we ultimately built public cloud over the last 10 years and now the opportunity to build AI. So look, today I can attest to this next wave through experience. Earlier this week, at the Board meeting of one of our growth stage investments, Articul8, the CEO went out of his way to highlight the uptick in pipeline, specifically tied to the most recent deployment of DeepSeek, with enterprise customers activated by the improving affordability of developing customer AI capabilities. So, there it is on display, Exhibit A. When you have lower cost compute and it becomes more ubiquitous, it's easier for smaller enterprises to make that CapEx decision to go build their own generative AI, and that's exactly what we're doing at Articul8, one of the over 50 investments that we have. So, our purview and our view here is we get to see it all. We see it from the infrastructure side, we see it from the customer side, and of course, now we're seeing it from the generative AI side. The key to this slide is really simple, bottom-line, the natural innovation you see today across the AI ecosystem is driving some of the most rapid adaptation I've ever seen of a new technology in history. Next slide, please. Let's take a look at how some of these developments are playing out in our ecosystem. First, one of the noticeable trends that we're starting to see with our own hyperscaler customers is the focus on the next phase of AI, which is inference. While data centers remain at the core of the opportunity, the giga scale investment that you're seeing in training clusters is increasingly going to be augmented by compute footprints that serve inference, which is the actual use or application of those pre-trained generative AI models in our daily lives. So, here, as I said earlier, location and performance in the rest of the network start to matter a lot more. We saw this play out in public cloud from the time period of 2013 to 2024. As you can see on the left-hand side of the slide, capacity today is dominated by training. But over the next few years, we expect inference to represent the bulk of workloads in data centers as applications and platforms embedded with AI begin to proliferate. That proliferation is really the dissemination of that data and those applications and those models move out to consumers, to devices, to enterprises, and to government agencies alike. But to do that, you have to have the network infrastructure to deliver those workloads. And again, history has a way of repeating itself. Look at the growth and the rapid growth at DataBank and companies like Equinix that do edge computing. There's no mystery to why public cloud workloads have moved closer and closer to the actual use cases. Generative AI will follow that exact same footprint, except it's more pronounced, and there's more consumption, which requires more dark fiber, it requires more small cell infrastructure, and it requires more mobile edge infrastructure, which will come in the form of towers and edge data centers. We're just at the beginning of really building out the generative AI delivery mechanism for infrastructure. This is really exciting to us and this is the thing that we're talking with LPs about today. Not big hyperscale campuses, but actually the associated ecosystem it takes to deliver the promise of generative AI. Next slide, please. A good framework for the profile of inference is to think of it as Cloud 2.0. Inference workloads are really about infusing traditional cloud-based use cases with new intelligence, essentially the same activities that we use for public cloud today, but those become faster, more efficient, and ultimately more useful. Just like the cloud, these use cases rely on infrastructure that's closer to the enterprise and to the consumer. You can see on the right-hand side here, AI agents will increasingly execute and orchestrate these common use cases. Whether it's search, enterprise workflows, e-commerce, social media, these are all latency-sensitive workloads that will benefit from the integration of generative AI agents and accelerate the age of inference. Next slide, please. So, look, before I wrap it up, I want to put these industry-wide trends into perspective and bring them down and distill it at the DigitalBridge shareholder level, to see how they're manifesting across our portfolio. The demand for cloud and AI training and inference workloads is driving rapid growth across our data center platforms globally, which we've gone from under 1 gigawatt of cumulative capacity four years ago to today almost 4 gigawatts of leased capacity across our portfolio that was lit at the end of 2024. By the end of this year, we expect capacity to have grown at a 68% CAGR growth rate over the past five years. That's amazing as it is industry-leading. Just as importantly, we've got a secured power bank that's second to none, that's almost four times that size, over 16 gigawatts, positioning us to continue to scale in the years ahead. I can't tell you how valuable it is to have a land bank and a power bank that we can lease into over 12 gigawatts over the next two to three years. While many of our GP competitors and other real estate developers are trying to secure the power, we have the power in place today and we have the land and we have the building permits. This is really an advantage that was embedded in the fact that we started down this journey over 10 years ago, not 12 months ago, not 24 months ago, we're not new to the sector, we've been here from the get-go. So, we've talked about this, that the most important component today is power. It's key to the equation. So, we've spent a lot of time at the portfolio company level supporting their efforts to bank this future capacity, and here it is on display for you, our investors today. We're excited about having the ability to meet customer demand because our power is already secured. Next slide, please. So, we talked a lot about megawatts, gigawatts, but really what we've never done is we've never explained what that means for you, DigitalBridge shareholders, and we need to do that. We need to unpack the value of a megawatt to you, our shareholders. So, let me try to do this in a way that's easy and tangible and really ties to the fee streams that we generate managing an increasingly large pool of capital. The carry participation DigitalBridge shareholders benefit from is embedded in the value of our data center businesses. So, the example you see here looks quite detailed and we're sorry for the detail, but it's important that you understand it, it's actually a pretty simple analysis using market-based assumptions that highlights the potential for substantial value creation via carried interest. Starting at the top with CapEx around $10 million per megawatts, as you work your way down, it's reasonable to see how we can generate a 2 times MOIC on our equity investment, which translates, in this example, to around $290,000 of carry per megawatt for every $5 million of equity deployed. This is actually a pretty simple algorithm. What's particularly compelling is that $290,000 per megawatt turns into $290 million when you're building a gigawatt scale, which I just showed you on the previous slide, we are doing that. So, in this scenario, 1 gigawatt equates to $1.55 of value on a per share basis. This is the kind of value creation we're talking about when we highlight the growing embedded value of carry for DigitalBridge shareholders. It's why we raise co-investment to fuel growth at our existing portfolio companies and why we continue to apply our buy-and-build strategy to new platforms. So, look, I hope this helps put it all together for you and explains our investment program in a perspective that is really tied to numbers and tied to the share price. You're certainly welcome to apply your own assumptions. We're always happy to have that debate with all of our shareholders. And look, at the end of the day, it's a theoretical framework, but we find it useful to bridge the translation between megawatts and what it means to you, our shareholders, at DigitalBridge. Next slide, please. So, as always, what I try to do here in our fourth quarter earnings at the beginning of each year is I try to lay out what my agenda is for the coming year. So, let's cover the 2025 CEO checklist. One, we talked about fundraising. The goal is to hit net FEEUM of over $40 billion by the end of this year. That will involve finalizing capital formation around the third flagship fund and our second credit fund, and then successfully launching our two new strategies built around digital energy and stabilized investment grade data centers. The third piece of that is launching our second private wealth offering, which is already in flight. I'm really excited about this. We had forecasted that we could raise $600 million in our private wealth channel last year. We massively outperformed that, raising over $1 billion of capital. I'm really excited about what we can do here as we scale that part of our business. Two, on the investment side, we expect to deploy another approximately $20 billion into AI infrastructure to support cloud and AI buildouts. From training clusters to the early stages of inference focused on deployments at the edge, we plan to be the leading investor this year, as we were in the previous year in the AI infrastructure ecosystem. Again, it's not just about big hyperscale data center campuses, it's about bringing that connectivity and ultimately making sure that those workloads get to the right place, and we have the right portfolio companies and we have the right investment strategies to enable that. The third key objective is around scale. I've been using this word a lot today, and it's really important. Continuing to generate double-digit earnings growth and expanding our margins is at the core of what the DigitalBridge investment thesis is go forward. We will make our business more efficient, we will make our business more profitable, and we're going to deliver better earnings growth for you, our shareholders. All of these initiatives position us to support the accelerating growth that we're seeing across the digital infrastructure ecosystem in AI, cloud, and mobility. So, I'll wrap it up today. I deeply appreciate your ongoing interest in DigitalBridge. And with that, I'm happy to open up the call to Q&A. Operator?