All right, great. Good morning, everyone, and thank you for joining RXO's investor webcast covering artificial intelligence and machine learning, and how RXO is using both to differentiate ourselves in the freight industry. I'm Jared Weisfeld, RXO's Chief Strategy Officer, and I'm joined by three RXO technologists. With me today are Yoav Amiel, RXO's Chief Information Officer, Tudor Bodea, Director of Data Science, and Alyssa Myers, Vice President of Technology, Freight Brokerage. Yoav has been in the field of artificial intelligence for more than 30 years, working at the world's largest technology companies. Tudor has a PhD in Civil Engineering and Statistics and has written a book on pricing analytics. Alyssa has been a technology product manager for almost 15 years and has a background rooted in supply chain management. During this call, the company will make certain forward-looking statements within the meaning of federal securities laws, which, by their nature, involve a number of risks, uncertainties, and other factors that could cause actual results to differ materially from those in the forward-looking statements. A discussion of the factors that can cause actual results to differ materially is contained in the company's SEC filings, as well as in its earnings release. You should refer to a copy of the company's earnings release in the investor relations section of the company's website for additional important information regarding forward-looking statements and disclosures and reconciliations of non-GAAP financial measures that the company uses while discussing its results. All right, with that, let's get started. I'll start off with some introductory remarks to help set the stage. I'll hand it over to Yoav, Tudor, and Alyssa for their presentation, and then I'll moderate a Q&A session. I anticipate today's session to last approximately 45 minutes, and a transcript of today's event and supporting materials will be available on RXO's website. Over the past 10 years, we have invested hundreds of millions of dollars into our cutting-edge technology, including artificial intelligence. These investments have facilitated a strong 42% return on invested capital, best-in-class gross margins, and significant volume outperformance that resulted in RXO growing 3x faster than the brokerage industry from 2013- 2021. To give you an idea of the scale at which our technology operates, every weekday, as we are matching freight, the RXO pricing suite of products is pinged hundreds of thousands of times, which allows us to benefit from best-in-class dynamic pricing. We developed our technology from the ground up with three specific cohorts in mind: our customers, our carriers, and our people. The technology is critical to enabling our people to drive outsized results for RXO. For us, it's all about the intersection of technology and human capital. AI and ML will not be at the expense of our people. We are leveraging technology to drive productivity and efficiency gains, which will help us achieve $500 million of EBITDA in 2027. While we have a decade of experience using AI and ML to drive real business results, we are still at the very beginning of our journey. With that, I'm going to hand it over to Yoav and the team to discuss our use of AI and ML in more detail, and then I'll rejoin after the presentation to moderate a Q&A session. Yoav? Thank you, Jared. Hi, everyone. I'm Yoav Amiel, RXO's Chief Information Officer. Now, AI and machine learning is my passion, and for more than 30 years now, I've been involved in this domain, building innovative and scalable solutions for several big companies in the high-tech industry. Now, before we dive into the details behind machine learning and AI, it is important to highlight the technology foundations behind RXO's systems. We are the original disruptors in the freight industry and built our transportation platform, RXO Connect, from the ground up. At that time, we embedded machine learning into our tech stack, and the technology we are using has gotten significantly more sophisticated over the last 10 years. RXO Connect is a scalable and integrated ecosystem that supports all modes of transportation, as well as all sides of the transactions, including thousands of employees, independent carriers, and shippers. Now, the fact that we built it from scratch allows us to focus on innovation and minimizing the needs for maintenance and trying to glue together existing systems. We are releasing new functionalities and capabilities every two weeks. Now, when looking into machine learning and AI, data is a key component. We have more than 10 years of data collection, serving as a critical input for our proprietary algorithms and engines. The fact that we have our own data sets provides us rich and actionable data with high density and granularity. Important to know that the internal data set includes attributes which are not available elsewhere, and third-party data sources are not nearly as high quality. Those in the industry that don't have their own years-long data sets are at a disadvantage. During today's session, we will focus on AI and machine learning, highlighting the components, the processes, and the success criteria for building strong and efficient engines. We will review a couple of use cases, and we'll touch on the future state as well, including the opportunities that Generative AI brings to the table. Before we dive into the different capabilities, let's have a brief review around the history of AI and how it evolved. What is artificial intelligence? AI evolved over the years, but on a high level, it is the development of computer systems able to perform tasks that normally require human intelligence. In short, it is the science of getting computers to perform human tasks. Known AI areas include visual perception, which is the ability to interpret visual information like pictures or videos and give it a meaning. Speech recognition, today, you could find it embedded in smartphones and home devices, decision making, and even translation between different languages. Machine learning is a subset of AI. It focuses on development of algorithms and models that enable computers to learn and make predictions or decisions. As a result, computers no longer need to be explicitly programmed for each specific task. For instance, in traditional programming, you write if, then statements. If this, then do that. When using machine learning, you feed a computer with examples, also referred to as the training dataset, and let the system come up with the answer, usually around classification, recognition, or recommendation. Deep learning is a subfield of machine learning. It focuses on learning through multiple and continuous layers of representations of the data. In many ways, it tries to mimic the way a human brain works. For example, neural networks are the backbone of many deep learning algorithms. Generative AI, a term which you are probably very familiar with right now, is a category of AI. It is all about generating new content based on patterns and examples from existing data. While the awareness started from OpenAI and ChatGPT, Generative AI is not limited to chatbots. It could be around text, images, videos, music, even programming code. The beauty or opportunity around GenAI is the fact that it opens the door for use cases we did not have in the past, mainly around content creation, interactions, and optimization. I'll hand it over to Tudor to speak about the key ingredients for successful AI and machine learning algorithms. Thank you, Yoav. Hi, everyone, I'm Tudor Bodea, Director of Data Science at RXO. I have been with the company for five years now, and fortunate enough to work on most of what we are going to cover today. Prior to RXO, I led the dynamic pricing disruption in the hotel industry. Yoav, just as an FYI, I was in grad school when deep learning emerged as an AI and ML subfield, and have vivid memories still about how difficult and novel at the time it was. Now, maybe that aside, while there are many ingredients that contribute to successful AI and ML models, in general, they revolve around the following core areas. First and foremost, what everyone wants, but only a few can get, and that is data, and lots of it. In today's world, the successful AI and ML models require big, comprehensive, covering multiple domains that is, and high-quality data, and the data usually abounds around us. Think of load, facility, geospatial and market intel data, to name just a few. Think of those, maybe Chicago to Atlanta loads, that every broker likes to talk about when asked about industry staple O/D moves. All of this data, while it exists, needs to be captured in a cost-efficient way and monetized appropriately. As with everything else, the time impacts how AI and ML models perform. To account, among other things, for economic cycles of various lengths, past data collected and curated over longer time periods is preferred, and, as one would expect, usually leads to superior AI and ML models. On top of the long history longitudinal data, the shorter the time to market for an initiative powered by AI and ML models, the faster the revenue realization, which, in a commercial setting like ours, is really an absolute must. Following the time ingredient, the algorithms, now fully integrated within sophisticated cloud-based AI and ML platforms, bring the AI innovation closer to the user, and typically create efficiencies so that the same tasks are done faster, better, and safer. Along these lines, as an example, and Yoav maybe hinted to this earlier today, just think of the personal assistant algorithms that now can find loads for a carrier following a simple set of voice commands. That, in itself, is really outstanding. Now, to turn data into meaningful and profitable insights, the training of the algorithms needs to happen on specialized datasets. Over their entire life cycle, these datasets need to consist of accurate, reliable, and consistent data. On my side, years ago, I learned of the importance of data consistency when I could not recreate the gross margins from the buy and the sell rates that were provided to me. Let me tell you, right there, right then, that was not funny at all. Last but not least, strong AI and ML model governance is required if an organization were to capture untapped business opportunities and really compete successfully in high-velocity environments, where experiencing major tech or AI/ ML incidents, or maybe in plain English, IT disasters, is not really an option. Building on these generic success ingredients covered on the previous slide, I would like to highlight maybe what positions RXO for success in the space of AI and ML. These are core differentiators and are likely unique to RXO. On the data side, we own proprietary data curated by engineers and equally important, by our operators. Augmented with third-party provider data, our data sources' breadth and depth are difficult to match, and the number one reason why the power of our algorithms cannot be replicated easily. As an example, as Jared mentioned moments ago, on any given workday, we make hundreds of thousands of pins to our pricing infrastructure, all of which, upon completion, help us expand the footprint of our data sources. For us, time acts as a catalyst for success. The decade that we have spent gathering and understanding the data has accelerated our ability to learn and really improve our systems. We have 10 years of data and feedback loops that show how customers and carriers both behave, and how our pricing reacts to their moves across nearly every kind of freight market cycle. There is really no doubt in our minds that the 10+ year head start in this area creates a competitive advantage for us over any new entrants in our industry. In regards to the algorithms themselves, we always build them so that they are aligned with the business objectives and create value for their target audiences. Our approach to leveraging this technology is to equip our business partners, the shippers, the carriers, and internally, the employees, with powerful solutions that allow them to maximize value creation. Our algorithms are developed drawing upon the vast experience of our user base and evolve continuously based on well-established, and more importantly, in-time perspective feedback loops. These feedback loops tie directly into training. Our algorithms are RXO built and trained on state-of-the-art AI and ML platforms, using unique and industry-relevant data sets that benefit from the business know-how of our operators, which most of you know are industry veterans. Finally, from a governance standpoint, we rely on best-in-class processes, tools, and people to monitor the performance and the quality of the outputs of our algorithms. We have purposely spent time building control mechanisms that are capable of changing on the fly, the behavior of our algorithms, so that they continue to really be responsive to the ever-changing market conditions. Perhaps really unique in this space and, a differentiator, too, our operators are an integral part of governance as they really contribute the feedback and ultimately are the beneficiaries of the system changes. To conclude, our tailored approach to each of the key ingredients for a successful AI and ML model creates a competitive advantage for RXO. We focus on value creation first, which is core to what we do, then leverage our data, our long history in the industry, our experts, and our technology to create AI and ML solutions that not only make an impact, but they also drive commercial results. Maybe, for those in the audience who are visual like me, we are summarizing the sources of our AI and ML competitive advantage in the following figure. Starting at the top, our proprietary, insight-rich data sets, together with the industry know-how of our operators, help build and train AI and ML models that are uniquely positioned to create value and outperform in the marketplace. Shippers, carriers, and employees offer specific and timely feedback, which together with the best-in-class governance, assures that our AI and ML models evolve and continue to deliver value and commercial performance in the field. Now I will hand it over to Alyssa, who will discuss a few relevant AI and ML use cases. Great. Thank you, Tudor. I'm Alyssa Myers, I'm responsible for the technology that our brokers team uses. I've been with RXO for nine years and helped design many of our proprietary systems, which are really now used company-wide. What I'd like to do today is walk you through a few use cases of how RXO is actively leveraging AI in our technology solutions, and the first use case I'll cover is pricing. We realized early on that pricing automation would really shorten our sales cycles, which would allow us to capitalize on opportunities and achieve faster scale. We also realized it's not just about getting the price, it's about getting a very accurate price. In my role here at RXO, I give a lot of demos of our technology products, including RXO Connect, and one of my favorite moments in these demos is when my audience sees the power of our pricing automation. Their interest is immediately piqued. I get questions like: "Do you guarantee that rate? How do you know it will be profitable?" I get to point back to our track record of profitable growth as evidence of the power of our pricing technology. How have we been able to evolve our pricing intelligence so effectively? Well, first, we started by combining our data with leading, you know, price, supply, and demand indicators. One of the key pieces of value that our own data brought to the table is that it's been collected over years. We were able to deduce patterns based on past market cycles and then use these patterns in combination with public market data to predict a price. Next, we leveraged our experts. We began surfacing these prices to our internal employees, and they would provide feedback on maybe whether the prices need to be higher, maybe they needed to be lower, and then they'd give us reasons why. We incorporated and continue to incorporate their feedback on an ongoing basis. Finally, we began to surface our pricing directly to carriers and shippers and use their interactions with those prices as an additional feedback loop. These feedback loops from our employees, our shippers, our carriers, allow our pricing to pick up on shifts in price sentiment faster than a market indicator would, so that our algorithms can respond very quickly. Our early focus on data collection and really understanding the importance of pricing accurately at scale has enabled us to realize strong, profitable growth. We grew volume over 60% in the last 3 years, and we've consistently achieved healthy profitability. In addition, our ability to price in real time has been a key automation driver for us. The fact that we can win freight from a shipper or cover a load with a carrier automatically is highly driven by the fact that there's no dependency on a person to actually provide that price. Moving on to our next use case, let's talk about freight matching. At the heart of our business model is matching loads we receive from our shippers with the best carriers who can service the freight. The opportunities we saw with freight matching were twofold. First, we had a productivity opportunity. If we could predict the best carriers for a load, we could surface this information in our tools to allow our reps to become more productive. Second, by surfacing loads directly to carriers that we know they'll be interested in, we can create a deeper, stickier relationship with them. Understanding these two opportunity areas, we took a broader approach to developing our AI solution for freight matching. We realized that our solution needed to account for carriers working with us in our online tools like RXO Connect, but it also needed to work for our employees, who were proactively reaching out to carriers. Additionally, we also wanted the solution to be capable of matching carriers to both spot and contract freight. The result is a single AI process that is capable of servicing multiple channels used by both our operators and our carriers. Carriers can seamlessly interact with a list of very personalized load recommendations in RXO Connect, but then our operators are also given information about the best carrier to reach out to on any given load, so they don't even have to go search. They also have the option to browse carrier matches with contract opportunities, which is really key for carrier relationship building. This matching technology is continuously updated to take into account feedback based upon what reps and carriers did or even did not interact with. It provides both productivity gains to our operators, and it creates a lot of stickiness with our carriers. In Q1 of this year, we reported a carrier seven day retention rate of 79%. This is very direct evidence that this personalization for carriers is creating a very compelling experience that they want to continue to come back to. On top of this, getting a head start on this matching technology allowed us to scale our workforce productivity. If we look at 2018 through 2021, our productivity actually increased by 50%. This has enabled us to grow volume faster than our headcount. As we look toward the future of AI, Generative AI has been, and will continue to be, a key area of focus. GenAI is a tool that will allow us to address the more complex challenges and possibilities that our industry faces. I'll walk you through how GenAI is opening these new doors, and then we'll touch on several use cases that are top of mind for us at RXO. As Yoav touched on earlier in the presentation, GenAI is really a deeper form of AI. While traditional AI is focused on taking known inputs and outputs to create automated intelligence, GenAI builds upon automated intelligence to deduce things like responses and predictions and forecasts based on other known patterns. In essence, GenAI really steps beyond pure automation and has the capacity to explore very creative solutions. As we see it, GenAI really enables three key areas. The first is creativity. GenAI engines can combine variables that our own human biases may cause us not to even consider, and this can inspire very creative approaches to solving problems or uncovering opportunities. The second is interactions. It has the capacity to draw parallels and create connections between patterns and data, providing new insights. Finally, there's optimization. GenAI is able to analyze infinite sets of possibilities for us to leverage to identify new opportunities or even potential areas of risk. Applying these three areas of opportunity to the transportation industry can have a huge impact on the way we operate. One application that ties back to creativity relates to sales optimization. Imagine using a GenAI solution that looks across many data sources to build a customized sales strategy for a seller, allowing them to spend less time on research and more time having meaningful conversations with more leads. Looking next at interactions. Digital assistants, these are going to play a key role in how we interact with stakeholders. These assistants can creatively craft communications, maybe regarding things like detention or collections, allowing our workforce to actually be more productive. Finally, if we look at optimization, areas like network optimization are going to evolve dramatically. Gen AI's ability to run multiple scenarios on supply chains can help highlight risk factors and even make recommendations for risk mitigation. In summary, as the technology evolves, our possibilities are really continuing to evolve. We're proud to continue to embrace new use cases that will drive our business growth, just as we've done in the past. We believe that the key to being successful in this new generation of technology is to not only embrace the technology, but we have to stay close to the needs of our shippers, our carriers, and our employees. It's these relationships that provide us with new inputs and ideas to leverage the power of technology to create tremendous value. I want to thank each of you for joining today's webcast. I really hope you've enjoyed the presentation. Now I'll hand it back over to Jared to moderate Q&A. Great. Thanks, Alyssa. All right, we're now going to transition to the Q&A portion of the event. What I've done is I've put together the most frequently asked questions across the investment community regarding RXO's investments in technology and artificial intelligence. Now we can get answers directly from the RXO technology team. Let's get right into it. What are the biggest challenges with adopting these tech initiatives that we just talked about? Are our customers and other stakeholders actually ready for this? I'm happy to chime in on this one. The tools and the technology are absolutely accessible, and they're highly available. As we all know, there's a lot of buzz around this topic. I think one of the potential biggest pitfalls when it comes to adoption is, you know, falling too susceptible to the buzz. I think companies have to be very careful that they're not just doing AI for the sake of doing AI. They really need to stay focused on how we use the technology to truly create value. You know, if that's the approach that's taken, and that's kind of the barometer that we have in mind, customers and stakeholders will absolutely adopt solutions that make their lives easier and that create value. The other piece I want to hit on, though, is the governance component, because there could be an AI solution out there that, you know, creates a lot of value, but maybe the content isn't quite right. Having that governance in place and those eyes on what that those algorithms and that AI is actually producing, is absolutely critical. In summary, customers and stakeholders are ready for compelling experiences that really bring them value. You know, we need to leverage this technology with that in mind. Perfect. Thank you, Ayssa. Maybe now let's move to where we finished the presentation. We talked about potential future use cases from an AI perspective. How can RXO leverage AI to expand into new markets longer term and drive new revenue streams? Can we get some examples? I can take this one, Jared. You know, we've been leveraging AI and machine learning for our growth for many years now, building and optimizing our proprietary engines like matching, pricing, recommendation, capacity aggregation, and more. Now, while these engines helped us with automation, productivity, optimization, and volume growth, when we look at our, you know, future growth plan, we want to increase our customer reach and drive more volume overall to the company. Today, we have more than 10,000 customers already, and we see an opportunity to expand it into the SMB market. Now, if you take into account the Generative AI capabilities, we can further automate the interaction engine and help this type of a self-service capability specifically for that segment. These capabilities will support transaction with high digital percentage, leveraging our robust existing technology while minimizing the need for support or interactions. That's a great call-out on SMB. Maybe just to remind everyone, when we look towards our 2027 guide, which calls for $500 million in EBITDA, we've got multiple growth initiatives to get us there. We're going to continue to expand share within our existing customer base. We're going to go after new customers within the large enterprise in terms of Fortune 100, Fortune 500. To Yoav's point, we're also going to go ahead and go after SMB, which today is still relatively small for us. That's a huge opportunity for RXO longer term. Maybe to the next question, will artificial intelligence lower the barriers to entry in the freight industry longer term? Surprisingly, I would say that this trend could even have the opposite impact on barriers of entry and increase that gap, something that we already seeing today. We all know that, you know, AI and machine learning can bring efficiencies and automation into the day-to-day operations, but you need to do it the right way. You need to have accurate results and be profitable to the organization. We explained throughout the presentation, there are two key ingredients for successful AI and machine learning. One is data, and the other one is scalable infrastructure. First, if you look at our infrastructure, we invested hundreds of millions of dollars into our Connect platform, allowing us to scale and leverage the power of AI. Second, if you look at data, new companies without this dense and granular internal data set, will find it difficult to leverage the real power of AI. Yeah, maybe, Jared, perhaps just to complement Yoav's answer. Now, AI is a key enabler, nevertheless, but it still needs to be fed with the appropriate data if it were to perform well. The industry know-how that allows one to really make sense of her data, and through AI, maybe to learn meaningful insights of commercial value, that will still remain highly valuable and come with a premium that, you know, to Yoav's earlier point, can only widen the gap. Now, you know, maybe building on the example that you referred to and which I tried to emphasize. We make hundreds of thousands of calls to our pricing infrastructure every day, right? The data, the know-how, the infrastructure, and all of the processes that make that a reality, it's something that's very difficult to replicate, no matter how maybe AI savvy an organization is. Maybe, you know, in this context, in my mind, protecting and retaining the intellectual property becomes incredibly important, as that is really the source for a competitive advantage. That, you know, pure and simple, you cannot be, or you cannot replicate. It's that simple for us. Thank you, Tudor. We talked earlier about hey, how AI can open up new addressable markets, drive new revenue streams, but what about from the cost side? Can AI, I'm thinking, you know, improve matching, pricing, routing technologies, can that materially bring down the needed headcount in order to operate? Sure. You know, when we first began focusing on AI solutions, what we really had in mind was, how do we use this technology to enable future scale? Our goal wasn't necessarily to reduce headcount, if that makes sense. You know, we wanted to make sure that we could grow the business faster than headcount. I think you cited a couple great examples, you know, matching, pricing, both of which we talked about earlier in today's presentation. I would tie back, you know, to the question, can AI materially bring down the needed headcount to operate? We look at the productivity gains that we were able to really recognize between 2018 and 2021, when our productivity increased by 50%. I think that's a very direct testament to the fact that, you know, the answer to this question is yes, it absolutely can. We're not even, you know, close to being done. We have a fantastically long runway ahead of us, especially as this technology really continues to get smarter and smarter by the day. The other thing I would tie that into, though, too, is we talk a lot about automation. You've probably heard us use the metric 96% of our loads are either created or covered digitally. That automation is a key productivity driver. When you think about what really enables that automation, a lot of it is our pricing. I think I mentioned earlier, the fact that we can get a load from a shipper or cover a load with a carrier, without having that person there to provide the price, has really been a key productivity enabler for us. Thanks, Ayssa. Yoav mentioned this earlier, and most on the call have certainly heard the buzzwords of Generative AI, especially algorithms such as ChatGPT, which I'm sure everyone's used at least once. How will RXO implement Generative AI going forward? Can we get some use cases here as well? Well, the good news is that it's not the future, it is already happening right now. At RXO, we already have several Generative AI capabilities in different maturity stages. You know, our initial focus is in the areas of content explorations and interactions, tackling internal-facing capabilities first. Maybe one example is our sales enablement tool, allowing our sales group to gather intel on customer in seconds instead of hours before going to a meeting. Another, you know, potential use case is around the SMB growth opportunity that we just mentioned earlier. Tapping into that self-service capability of Generative AI, making customer interactions intuitive and conversational, and help grow our volume overall more efficiently. Maybe, Jared, since this is dear to my heart too, you know, as this technology is novel still, we can provide maybe the audience with a general view into some of our learnings today. I do not know how many did get a chance to experiment with the technology, but certainly a bit of learnings from someone who has tried it may help. Here, from an execution standpoint, the AI and ML platforms that support the gen AI technologies make it relatively simple to initiate the work on fit use cases. That said, you know, use cases that maybe go above and beyond the obvious require business and tech know-how that it's still rare and at times difficult to acquire, right? That may be on our side, it really showcases the fact that we do need to specialize in this area. Then, you know, last but not least, as a technology, GenAI is evolving rapidly with, you know, newly released workflows needing to be really revisited and reworked as soon as they are released, simply because the base and the underlying large language models evolve so rapidly that and update so frequently that if you don't do it, then you are going to be left behind. With that, maybe, Jared, back to you. Thanks, Tudor. That was helpful. Can AI speed up the development and adoption of autonomous vehicles? If so, how do we think about the potential impact to RXO? AI is already the engine behind autonomous vehicles, and, you know, the existing engines utilize AI for image and video recognition, lidar, which is the pulsed laser interpretation, and decision-making that is happening on the car while it's on the road. Moving forward, AI will continue to see the development in the AV domain, and the computer power expands, and the AI methods are becoming more and more robust. Now, if you look at RXO, AV is a good thing. It will increase the available capacity and reduce our operational costs overall. We are AV agnostic, as our platform is API based, and such integration with AV providers will be quick and straightforward. while RXO is agnostic to the implementation of AV and will be in a position to work with them, just as any other carrier, it's important to note that we are very long way off before AV become a significant percentage in the available capacity. Thanks, Yoav. Are we using third parties or building our AI capabilities in-house? Do we leverage cloud service providers? Jared, I can offer a view into this. We have an in-house AI and ML data science practice that helps build RXO proprietary capabilities in various areas. I think that Alyssa mentioned pricing matching, but even in ancillary revenue management, we try to apply it now. We build these capabilities ourselves to fully address the first, the needs of the business, then retain the IP, and then control and accelerate the delivery of the innovation. While we certainly build and likely will continue to build core capabilities in-house, we do try to be practical and integrate creatively with specialized third-party vendors, where and when required. To remain competitive, as I mentioned, and be able to scale as our business scales, we do deploy and execute our workloads on cloud-based AI and ML platforms. Just as a sign of, we do have a strong business relationship with one of the key cloud-based and cloud-based AI ML technology providers. Maybe, Jared, to summarize, we build the core AI and ML capabilities in-house, but we do so creatively by leveraging and integrating when required, with third-party providers and with cloud-based technologies. Perfect. Thanks, Tudor. Maybe just in the interest of time, this will be the last one. My guess is, Yoav, this is probably best for you. How will RXO sustain our competitive advantage and avoid potential disruption from new adopters or new entrants into the industry? Well, as we called out multiple times, we started our tech journey more than a decade ago, adopting AI and machine learning from the beginning and embedding it into our tech stack. We have that first mover advantage. In technology, as you know, we all know, you can never rest, and at RXO, we're always progressing. As I mentioned earlier, we already have multiple GenAI use cases in different maturity stages. The hype around GenAI just surfaces to the broader audience, I would say, the power of AI, that we've been using for many years now. Now, the fact that we built our platform from the ground up allow us to focus on innovation and minimize maintenance efforts. I would say that we have a unique combination of operational know-how, scalable technology, robust internal data sets, and years of fine-tuning the algorithms through these feedback loops that we receive. If a competitor will try to build something similar, it will take them years to get to where we are today, and while they are trying to catch up, we will continue to innovate and broaden the gap even further. Perfect. Thanks, Yoav. I want to thank you all for joining today's webcast. The longer term opportunities are enormous across AI, ML, Generative AI, and more. We're going to continue to effectively leverage our technology to penetrate new markets, expand our total addressable markets, increase the efficiency of our people, and automate processes. Importantly, we are still at the very beginning of our journey. A transcript of today's event and supporting materials will be available on RXO's website. Have a great day, everyone. Thank you. Thank you.