Thanks, Jacky. This quarter in executing the digital playbook I want to cover generative AI and some of the implications for digital infrastructure and ultimately, how it impacts DigitalBridge. This is the number one subject with both public and private investors today, and it’s one of the most active areas of engagement at the portfolio level with our customers, particularly in the data center vertical. On the slide here, we’ve incorporated a number of examples of contrasting traditional AI use cases with the new kind of capabilities that generative AI unlocks. So for media, by example, while traditional AI optimizes your Netflix recommendations with generative AI, we’re seeing the actual production of short films from text prompts. These kind of breakthroughs will only accelerate and get better. So, lots of new exciting new capabilities, but how does this really ultimately translate for DigitalBridge and DigitalBridge shareholders. Well, what we’re seeing today is that to unlock those capabilities, every enterprise software platform is being re-architected to incorporate generative AI. And that new layer of creativity translates into a lot more compute. I’ll walk you through the arithmetic in a second, but it’s pretty compelling. Just like the time and energy it takes a human to turn an observation into a creative new idea, Gen AI doesn’t burn calories, it burns KW. And that’s really important as you think about digital infrastructure. Generative AI is compute-hungry. So, let’s go forward. Next slide, please. Just to give you a sense of the speed adaptation around generative AI, many of you have heard or have seen this chart. But what it does is it gives you incredible context for the opportunity set. ChatGPT, OpenAI’s large language model or LLM, had the fastest adaptation on record of any consumer technology, hitting 100 million active users in two months. That happened earlier this year. So to contextualize that, that was 4 times faster than TikTok. It was 15 times faster than Instagram getting to 100 million users and Uber took over 5.5 years to hit 100 million users. These are really significant global platforms. And ChatGPT blew them away. So, we’re seeing rapid adaptation. But what about scale? How big could this be? It’s certainly the number one question on investors’ minds as they try to frame investment opportunities. And while it’s way too early to tell, what I’ve been trying to do is contextualize that relative to something we do know today, and that’s really comparing it to where public cloud started. So, this is about a $300 billion annual business with large cloud service providers representing about two-thirds of the market. Based on the feedback we’re getting from our CEOs and their conversations with customers around ramping capacity requirements, we believe this opportunity is going to be at least as big as the public cloud market was over a decade ago. And again, to contextualize that, today, public cloud, which really has been building and leasing space to the data center marketplace over the last 10 years, is at about 13 gigawatts. Ultimately, to drive AI and to get networks to where we think they will believe -- where we believe they can go, we believe the opportunity set is close to 38 gigawatts. So, we’re just literally in the first inning of a potential 9-inning baseball game. And it’s always important to frame the time against ultimately, the investment period, the amount of power consumed and the size of the wallet share that DigitalBridge can take. Next slide, please. So, I described Gen AI workloads as compute hungry, but I want to highlight briefly why that is. It’s really a combination of two factors. On the left, you see chip power is exploding. The new specialized AI chips, GPUs that NVIDIA, AMD and Intel are producing, consume 2 to 3x the power of prior generations. So, to put that into context, the latest chips literally consume as much power as a toaster. The second driver is the size of the AI models and this is really hard to get your mind wrapped around, but let’s give it a shot. Large language models like ChatGPT have billions of parameters with the latest models, GPT4 reaching almost $1 trillion. So ultimately, consequently, the result is data center power consumption is set to rise dramatically and increasingly be dominated by these AI workloads. It’s estimated that 80% of the data center power will consume -- be consumed by AI over the next 15 years. So, at the end of the day, access to power is a key differentiator. And having available space where those workloads can be realized is also very important. And this is where DigitalBridge believes that we have a key competitive advantage, having great portfolio companies throughout the world with available space, power and cooling that meets the needs of next-generation networks. Next slide, please. So, we know that Gen AI is power hungry. I think we all get that. But how does that translate into the data center ecosystem. It starts with AI model training, which will happen principally in large public and private clouds. These are principally at data centers owned by Vantage or Switch or Scala, some of the great portfolio companies that are building the next generation of cloud campuses, 100-, 200-megawatt 400-megawatt campuses. The key term here is power density, which manifests itself in two ways. On the left, it means higher megawatts per facility, instead of drawing 50 megawatts today, it will be 200 megawatts in the future, but with the same footprint. This is the power of power density, and this is the power of building the next generation of data centers that fit the customer needs of the future, not of the past. So why? Well, inside, as you can see in the middle, there’s going to be higher power density on a per-rack basis with each rack filled with power-hungry GPUs, drawing 40 or more KWs compared to traditional data center racks, which draw 10 KWs or less. This is a fairly substantial shift in the power density that can be delivered on a server. The other key factor that relevant or cloud trained is access to low-cost power. AI training is not as location or latency sensitive as applications. And you’ve got to get your mind wrapped around that around ultimately training AI models can happen in one place, but ultimately, the delivery of applications, which is either consumer-based, machine-to-machine based or enterprise-based has to happen closer to the end user, which is a low latency environment. So, think about these two workloads in two different capacities. With cloud training, it’s not about uptime and latency. It’s about the ability to access and invest in large concentrated megawatt capacity, i.e., for example, a Switch in Reno where we’ve got low-cost power that is 100% green, and we have it in a large amount of capacity in an 11 million square foot footprint. This is a competitive advantage at the end of the day. Your power density and efficiency are becoming increasingly relevant, so access to digital infrastructure in size and scale at the lowest cost is a key success factor. This was one of the reasons why we bought Switch. Next slide, please. I just described how AI training models happen in the cloud, which is where you’re seeing a lot of activity today. But once the model is cloud trained, it’s ready to be utilized by consumers enterprises and machines, a process called AI inference. As AI models are deployed and AI-powered applications proliferate over the next few years, inference and the growing relevance of the entire network will become clear. Gen AI is edge delivered. This is an important concept for all investors to get their minds wrapped around. For the actual applications, we use our phones, we use our laptops and speed and latency matter. It’s not efficient to send data back and forth to Ashburn, Virginia from Boston or Miami. The trained AI models need to live close to the consumer or the device or the enterprise. By the way, they can’t live on your phone either. If you want battery life longer than 5 minutes. So it is a combination of using ultimately mobile, but either your laptop or your desktop and ultimately, at the end of the day, you need a robust network. And a robust network that includes edge data centers, fiber, cell towers and small cells is going to become increasingly relevant over the next three to five years. We’re seeing it selectively already, to be honest, with inbound interest from things like metro fiber capacity that are in the order of magnitude larger from cloud service providers, which is why you’ve seen some of our fiber revenues pick up in the growth in fiber and enterprise fiber pick up. That is why the whole network matters, not just the data center. Gen AI is edge delivered. And to do that and to create that orchestration requires all pieces of the digital infrastructure ecosystem, and this is where DigitalBridge delivers. Next page. Let’s take a step back and take a look at how we are positioned for this cloud trained edge delivered future. As you can see, we’ve been buying and building data center platforms on a global basis. This portfolio represents about 35% to 40% of our AUM. It operates from the core to the edge of the network and serves well-defined workload profiles across an increasingly hybrid compute landscape. We are well positioned to serve cloud trade demand for public and private cloud operators through Vantage, Scala and Switch, as I mentioned earlier. Then going to the next level, our portfolio companies, DataBank, AtlasEdge and AIMS are set to capitalize on edge delivered Gen AI, the next layer. Across the board, we’ve got one of the newest fleets in the industry, as we like to say. So that means we’re investing, and we’ve got the facilities to serve these new workloads. It’s really important to understand that our customers want state-of-the-art data centers that have significant access to low latency, large amounts of fiber, incredible cooling and most importantly, the power density that we talked about earlier and hopefully on a renewable basis. It’s a high bar. But at the end of the day, what we’ve been doing for the last 2 to 3 years is setting ourselves up to meet these demands and be successful. That’s why you’re seeing the leasing numbers that you’re seeing today at our portfolio companies. We are taking outsized wallet. Next slide, please. So, I’d like to finish by talking about some of the tangible evidence we’re seeing already around the impact of Gen AI and our business. It’s very early inning. Someone recently described it to me is that we’re still in batting practice. I’m not sure I totally buy that, but there is strong anecdotal evidence, this is set to be a very important demand driver for our ecosystem and for DigitalBridge portfolio companies in the coming years. So, let’s start with what we’re seeing on the supply side in our conversations with LPs. As I referenced earlier, this is the number one topic that we talk to our LPs about today. And while they’re just getting up to speed on the implications across their investment portfolios, it’s clear that they’re looking to DigitalBridge as a key thought leader as it relates to the digital infrastructure piece of AI. We’ve held a number of one-on-one meetings and webinars with our key global LPs covering some of the topics we’ve talked about today in greater detail. There is a high level of engagement on this topic. And we believe, particularly as the demand materializes, this is going to represent another tailwind for us in fundraising over the next few years and not just in our flagship product, but in credit and core and in other products that we’re involved in. Secondly, on the demand side, data center lease up is where we’re seeing this first. TD Cowen just reported over 2 gigawatts of industry-wide leasing over the last 90 days in the United States. That’s within a 10 gigawatt market. So, we’re talking about really big numbers and our portfolio companies are participating in that 2 gigawatts and taking outsized market share and a bigger portion of the wallet. So, when you look across the DigitalBridge ecosystem, that pipeline, where you see the first signs that lead to bookings, which translates into revenues as new capacity comes online, pipelines are up 84% across our data center portfolio year-over-year. So just to contextualize that, 2022 was one of the best years we’ve ever seen in data center leasing. So, we’re on pace to eclipse 2022, and we think this year is only accelerating in terms of the demand. On the right-hand side of the page, we’ve highlighted interesting feedback from our portfolio companies and investors alike. The two that jumped out at me are the first one from one of our data center CEOs. He was describing how a 24-megawatt requirement was a large opportunity just a few years ago. And now we’re seeing inbounds for 10x that size. So 100 to 250-megawatt type deals, in a campus setting. I mean these are just amazing numbers and really difficult to comprehend, but this is what’s happening down at the portfolio company level. The next quote that I’m particularly proud of is the CEO of DigitalBridge is the response to a DigitalBridge investor diligence call with one of the new specialized cloud providers that’s serving AI workloads. In response to the question of who are the easiest companies to work with on securing new capacity, well, the first response was DigitalBridge companies. That’s why we win. It’s really that simple. Building great customer-focused companies is what wins in this market. Next slide, please. So, as always, let’s wrap it up with a review of the CEO checklist. One, on fundraising, our number one KPI, we’ve raised $3.4 billion to date, and we’re on track to hit our fundraising targets for the year. I remain fully convicted in hitting those goals. We’ve made tangible progress on simplification with our DataBank recap and the Vantage SDC process is up next, and we will get this done inside this year as I promised. Finally, at the portfolio company level, we continue to support the growing compute and connectivity needs of the most powerful investment-grade logos in the world. Look, whether it’s Gen AI or any other secular demand driver underpinning our business, we’re supplying the picks and the shovels to the next-generation leaders that are building tomorrow’s technology. We expect Gen AI to be the next growth driver of demand for digital infrastructure just like digital PCS was 20 years ago and just like public cloud was 10 years ago. We’re in one of those great generational opportunities. And in many respects, it’s just the latest opportunity for us to show up for customers. That’s really the key is the ability to show up and support our key logos. So, thank you for your support as we continue to execute on the final stage of our transition to a fast-growing alternative asset manager, levered to the powerful tailwinds in digital infrastructure. I look forward to updating you next quarter on our continued progress. And with that, I’ll hand it over to the operator to begin the Q&A section. Thank you.