425 1 ea0297409-425_churchill11.htm FORM 425

Filed by Churchill Capital Corp XI pursuant to Rule 425

under the Securities Act of 1933, as amended,

and deemed filed pursuant to Rule 14a-12

under the Securities Exchange Act of 1934, as amended

Subject Company: Churchill Capital Corp XI (File No. 001-43020)

Set forth below is a transcript of Peggy Johnson’s podcast interview with TechCrunch, in which the proposed business combination between Churchill Capital Corp XI (“Churchill”) and Agility Robotics, Inc. (“Agility”) is discussed.

Connie: Hi, I’m Connie Loyses.

Alex: And this is Alex Gove.

Connie: And this is Strictly VC Download.

Connie: Hi, welcome back to Strictly VC Download. Humanoid robots are moving off the factory floor and onto Wall Street. Agility Robotics, the Oregon-based startup behind DIGIT, a bipedal robot already at work in Amazon warehouses and on Toyota assembly lines, is set to go public through a SPAC merger that values the company at roughly $2.5 billion. The deal, backed by Foxconn, Amazon, Nvidia, and SoftBank, would make Agility the first standalone humanoid robotics company to list on public markets. At the helm is Peggy Johnson, a veteran technology executive who previously led the augmented reality company Magic Leap and who spent years at Microsoft. She’s betting that a perfect storm of retiring workers, reassuring ambitions, and pent-up investor appetite for the robotics sector will carry Agility to the next level and beyond. We really enjoyed talking to Peggy for this week’s podcast. We hope you will enjoy the conversation, and we will see you back here next week.

Connie: Peggy, really a pleasure to be meeting you and talking with you now. And it turns out we’ve got a great news hook to lead us into this interesting story of yours. Agility is merging with Michael Klein’s Churchill Capital Corp. It’s a SPAC. You are set to see $620 million in gross proceeds, which is the largest capital raise in humanoid robotics history. I have to ask, why go public now, and why through a SPAC?

Peggy: Sure. Yeah, we get that question often, and it’s a good question. We really want to take advantage of the market momentum and be the first mover in the category. There are no pure-play humanoid robotics companies who’ve gone public. And from our perspective, we built a product, we’re the very first to deploy commercially, and we have a number of interested customers in our pipeline, and it’ll help us accelerate our ability to meet those engagements.

Connie: It is interesting, considering the frothy-ish valuations that we’re seeing in the humanoid market. I am wondering, what do you think of a company like Figure AI, valued at $39 billion? Does that make sense to you?

Peggy: It’s really hard to even guess at what the right valuation is. I’ve always tried to stay very pragmatic and very focused on the here and now. And the here and now in humanoid robots is the entry point of simple material handling. So we were valued at $2.5 billion pre the announcement that we had last week. We think that’s spot on. Over time, the robot learns more skills, the robot moves into new vertical markets, and clearly valuation increases over time. So we feel good about our valuation. We think it’s the right valuation for us and for the entry point of humanoids. You know, eventually, many years from now, we’ll enter into the home, but that will bring a whole step-level higher of valuation. But right now, what humanoids are capable of from a commercial perspective is this material handling. So we’re happy to be right where we are.

Connie: SPACs obviously have a very complicated reputation. A lot of companies that went public through SPACs in 2021 are trading well below their offering price or gone entirely. Why should investors believe that Agility won’t meet the same fate?

Peggy: Sure. And investors certainly have the prerogative to question any of these. We feel very comfortable in our progression, our starting with these simpler tasks, adding more skills. Obviously, AI is helping us to add skills very quickly to the robot. We feel comfortable because of the demand we’re seeing from our customers. You could say we’re in the deep tech category. We have real demand today. So it’s not, you know, sometime in the future we’ll deliver this. It’s right now. We have customers who need what we’re building. So we feel very confident in the pipeline and our ability to meet that demand. So if we just keep our head down, keep delivering customer by customer, robot by robot, we hopefully won’t experience the same volatility that, you know, some companies have on this journey.

Alex: We don’t yet have your revenue figures. They’ll come later in the S4. But your filings show operating expenses of around $111 million last year and roughly $100 million in cash burn. When can retail investors realistically expect this company to be profitable? What does that path look like? And also, how do you distinguish between pilot revenue and real repeatable revenue?

Peggy: So we’re not offering any forward-looking guidance right now. But what I will say is that we do have over 30 interested companies in our pipeline, many with global operations, all of them struggling to fill in labor shortages that they have. It’s a tough market out there. We’ve got a retiring workforce. We’ve got younger people who don’t want these jobs that are more manual in nature. So what’s happening with these companies, as they look at their trajectory, it’s almost existential for them. They have to find a solution. We have that solution. So it’s really about execution for us. And again, going public right now will allow us to tap into the capital markets and really tap into the pent-up demand that we’re seeing from investors to invest in this space. We have a solid story. We’ve got great tailwinds. We’ve got demand, and we’ve got a product that can fill that demand. So we’re feeling, again, confident about our position in the industry to deliver what is needed for these companies to stay on track. If they can’t find this kind of labor anywhere, you know, that’s a hit to their production, to their manufacturing output. It’s a real struggle today, and it’s getting worse.

Alex: So would you say that these companies are sticking their toe in the water and trying out pilots? Do you have any customers that are past the pilot stage?

Peggy: Yes. Now, I’ll give you a little background. When we first introduced our robot to companies, they said, well, sounds good, because I’ve got a labor shortage and I need to figure out how, over time, humanoids can help me fill that. And it took a while. There were different levels and gates that we had to go through. So we said, let’s put a program in place that, from start to finish, we can get you to deployment, to actual deployment, in as little as four to six months. We call it our customer acceleration program. That starts off with a proof of concept. We say, what is it you’re trying to solve? And they say, well, you know, we’ve got these two big pieces of automation. They’re not connected. A human still takes something off of a foot wall and puts it over onto a conveyor belt. It’d be great if we could have a robot do that. So we come in fairly quickly. We can assess if our current capabilities can fit their needs. If they do, we start the program. There’s a nominal fee to that program, but it’s not the larger revenue that comes with deployment, clearly. But there is a fee. It covers costs and gets us through those months with them. We actually don’t accept anybody into the program who doesn’t have plans to actually deploy at the end of that period. And by that, I mean if we meet their KPIs, their metrics that they’re looking for, which are generally throughput, accuracy, uptime, just like any automation. If we can meet those metrics at the end of it, we say, are you ready to deploy? Do you have the ability to ingest this type of automation? Do you have the capability of taking on new automation into these facilities? If the answer is yes, we put them on that list. So everybody on our list right now is already vetted, and they have deployment plans behind these first proof of concepts. And so what we have in the pipeline is $300 million revenue coming in for deployed multi-year robots. And it is well beyond the, let me try out this use case or that use case. They know what they’re going to do with the robot. They have the facility, and they’re ready to ingest them.

Alex: So you say that’s in the pipeline. Is that booked revenue?

Peggy: We do have over $300 million in booked revenue, yes.

Alex: And how many robots does that $300 million represent, deployed robots?

Peggy: So it’s on our RAS program, which is a multi-year program. It’s about 1,000 robots, roughly. OK.

Alex: Let’s talk about the robot itself. From what I understand, the DIGIT has three distinctive reverse knees, what people sometimes call bird legs. A lot of your competitors are building robots that look more like humans. Why this particular design choice?

Peggy: Well, it’s interesting. Our founders have no interest in biomimicry, which is just looking at a human and saying, OK, if I could just rebuild this human with parts, I’ll have the ability to do any kind of job. That’s actually not the case. So what they did with our version 4, which is the one that has the so-called bird legs, the backwards legs, is they said, what is the job at hand? It was material handling. And they found by having the robot’s knees go in the opposite direction, much like a bird, they were able to get from all the way down on the ground, pick something up, and not have the knees bump into anything and throw the balance off, and then take that heavy thing and lift it overhead onto a shelf, you know, like human height shelf above their head. And that was a range of capability that they were looking to fulfill. And turns out, for that particular model, the knees go backwards, if you will. But we feel the same way about end effectors or the hands. We have not seen yet a reason to have five-fingered hands. If there is a reason someday, we can put them on our hand. Our end effectors are interchangeable. There’s an isoflange at the end of the arm, so we can take off one end effector and put a different one on. But it’s really, what does the job call for?

Alex: But you’re not against opposable thumbs, I hope.

Peggy:Not at all. Not at all. Whatever is needed. In fact, right now, if you look closely at our version 4 end effectors, it looks like two thumbs and two fingers. Turns out that’s the best way to lift heavy plastic totes that may have up to 50 pounds in them. And by the way, the parts might be rolling around in there. If you ever carried something, you know, where there’s something heavy in a tote and it’s rolling around, you have to keep stabilizing, right, as that weight is moving around. That’s what the robot has to do as well. If you have the right tool at the end of the arm, it’s actually easier to do without the complication of five fingers. We’re also looking at producing our own end effectors over time, whatever the job might call for. But also, you know, the industries in robotics has had end effectors for something like, I don’t know, 60 years. So there’s a large and robust market for hands, if you will, that we can swap out and put on our robot. Should a job require anything in particular.

Alex: It’s interesting. You were talking about stability and lifting weights. You’ve said that wheels become unstable when you’re lifting things overhead. But some competitors of yours are experimenting with wheel bases for stability and cost. Why legs at all?

Peggy: Yes, great question. Actually, there’s jobs for both. What we focused on was going into human spaces and doing the dirty, repetitive tasks that humans really don’t like. And human spaces are built for humans. They tend to be narrow aisles. There tends to be things overhead that are only as high as your normal-sized human can reach. You know, even in our homes, counters are a certain height. Everything’s built for humans in human spaces. And that was our focus. Wheeled robots are great for a number of things, for moving from one end of a facility to the other. If the facility’s flat and doesn’t have any bumps or steps or anything, that’s a great way to get from one end to the other. However, if you start to go down narrow aisles and you have to lift something overhead, one of those 50-pound bins, and put it on a shelf, they can get tippy. In order not to be tippy, the base has to get wider and wider. So as the base gets wider and wider, you start to get constrained in certain human spaces. So again, there’s jobs for each. And oftentimes, we interact with other types of automation and robots in these facilities, and we expect to be interacting with wheeled robots as well. Maybe one of them shoots across the floor and hands something to us, and then we take it down a narrow side of the building. But you could just think of cars and trucks and campers and SUVs. They all sort of operate for different reasons and different use cases. Very, very similar. There is not going to be, by the way, one humanoid takes all. It’s a huge market, first of all. And there’s going to be a number of players in this space. Some may be more tuned to jobs than others.

Alex: You mentioned $300 million in revenue and 1,000 robots. Can we assume that you’re in the part of the development cycle where you’re still learning about your product and you’re probably losing money in these sales of robots to customers?

Peggy: No, we’re actually not. First of all, I will say we are building right now our fifth generation robot. And we’ve been doing this for years. So we know how to manufacture efficiently. We know what the shortfalls are along the way. Five generations later, we’ve learned a thing or two. We’ve already cost-reduced the product over the years, and we’re continuing to cost down the product with further engineering innovation. What’s great is we’re vertically integrated, and that’s very helpful. We can anywhere up and down the hardware, the software stack, we can make changes very quickly because of that.

Alex: So should we assume that the bill of materials for this robot is less than $300,000 per robot?

Peggy: We’re not sharing that right now. I will tell you, though, that we’re on a good path over time to bring the cost down because of what we’ve learned already. And the other thing we’ve learned is how to operate in commercial environments. So there’s a whole set of things that allow you even inside the doors of these facilities that you have to check off in order to operate. And because we’ve been around, you know, customers and deploying with customers for several years, we’re able to, even in those areas, take cost out. You have to meet certain bars to enter these facilities, whether it’s compliance or regulatory or plugging into their IT systems or their warehouse management systems. You can’t just walk in and start working. You have to actually be a piece of the whole puzzle inside. And so we’ve learned that as well and have been able to take cost out in that area, too.

Connie: Peggy, is everything made in Oregon?

Peggy: Yes, right now it is. We have our RoboFab facility up there. It’s a 70,000-square-foot building that has our lines for V4. Our version 4 is where we’re making that today. We’re starting to modify some of the equipment there to take on our production of version 5. So it’s nice not to have to put engineers on planes to go to a far-away manufacturing facility. They actually work right next to the building. It’s within the same premises. So that takes out quite a bit of time that we might have had to do if our factory was elsewhere.

Connie: Yeah, absolutely. No worries about supply chain issues. And all the parts are made by Agility or are some of them?

Peggy: Well, overall, about 75% or so of the parts are manufactured here, and the other ones, we work with partners to bring them in. That was very intentional. When the founders first set things up, they were looking for speed of getting the parts to pull together into a robot. And we’ve kept that up. So we’ll continue to work hard on keeping the supply chain, again, manageable in our reach. If there’s ever any sort of constraints, it’s good to have the parts right here in the US.

Connie: Yeah, that’s a great story, especially right now. And how many robots are you producing yearly? Or are you set to produce in 2026? And maybe if you can give us a forecast for 2027.

Peggy: So largely, those robots have been to fulfill our customer acceleration program in 2026. But what’s important to know is that’s our version 4. It operates inside of a safety cell. All humanoid robots, by the way, have to operate away from humans. You can’t be in close proximity of humans because they’re large pieces of hardware that move and can fall and that sort of thing. Our scale robot is our version 5, and that comes out early next year. And that is the one that all of our customers are focused on because that means while you can do some tasks inside of a work cell or a safety cell, you can imagine the total addressable market opens up quite wide when you can walk outside and do different tasks in different parts of the facility. Very unlike any other automation. So if you see a conveyor belt, I mean, it’s fixed. It’s stuck to the ground. It’s not moving. But a robot that is multi-purpose, that can do a number of things, can work on something in the morning in one part of the facility, walk to another side of the facility, work on a different job, work on a third job. So it allows for much more flexibility. That’s the robot our customers want, and that is the one that they will be deploying in volume.

Connie: I did want to ask about the payback for customers. So if people are right now paying $300,000 roughly per robot, that would seem to cover two factory workers, roughly, because it’s going 20 hours a day. I’d seen you say somewhere else, and it’s got to charge four hours a day. So that’s like two shifts back to back. When you’re talking to people, how do they think about it? How do you explain it to them?

Peggy: Actually, that, yeah, they’re not paying $300,000 per robot. That’s actually, we’re on a RAS contract for the most part. We do offer capex for customers where they can outright purchase the robot. But on our robots as a service, they pay a monthly fee. So when you’re looking at a job in your facility that you can’t find a human for, or if you find a human, they attrit very quickly, generally within six months. So you have to go out and look for another person to come in. Our model has been to offer a reduction to fully burdened human rate. And our current cost, we can accommodate for that. So we can come in on a robot as a service and offer for the robot to deliver ROI on day one because, first of all, they haven’t been able to find the human, and we bring the cost below their fully burdened human rate. The next question, people say, well, you’re taking jobs. We’re actually filling holes. There’s not enough people. There’s something like over a million jobs in the US today in these areas that are unfilled. They’re just very, very hard to hire for and very tough to keep the person in the position.

Connie: So you basically can lease these robots to a client. And what is the kind of assumption of how long they’re going to last?

Peggy: With our current version, we’re about 20,000 hours is the projected lifetime. Clearly, when it’s a robot as a service, it’s something that we have the ability to continue to operate the robot. And if it goes beyond that with maintenance, there’s a few parts that generally need replacing. There’s rubber on the bottom of the feet. The batteries at some point need replacing. But our contract allows for the customer to get a working robot. So right now, the estimate is 20,000 hours, about five years lifetime, but could be longer.

Alex: Yeah. What is the uptime in general of these robots?

Peggy: It’s been reasonable within the customer’s expectations. So it’s in the 90s, in the upper 90s. We haven’t really shared exactly, but it’s definitely capable of doing the job that they need. I will address one thing. People sometimes see videos of the robot. They say, well, it seems to be moving slower than a human. It’s moving very intentionally, but what a factory owner is looking for is throughput. So how many times does it make the cycle and deliver something over and over and over and over again? And that is really the most important thing. It’s not, well, how fast does it walk compared to a human? They actually don’t care about that stat. It’s, can it do the job that if I had a human, they would have done in this period of time? And we’ve been able to meet all of their metrics: accuracy, throughput, and uptime.

Connie: Peggy, Agility describes itself as LLM agnostic, like so many companies. You’re using Claude, Gemini, others depending on the task. Can you walk us through how the AI actually controls digit? There’s a task layer, a skill layer, a control layer.

Peggy: Yeah.

Connie: How does a large language model translate into a robot bending down to pick up a tote?

Peggy: Yeah, so I’ll start up there. That is what we think of as the semantic layer. And all the other ones that you spoke of is more the physical AI, the physical layers. The semantic layer is where the LLMs come in. They help us teach digit new skills. So, for instance, we can give digit a command. We’re working with one of the LLMs, and it’ll break that command down. The other day, we asked, we threw a bunch of different types of trash on the floor. Some recycle, some needed to go on the landfill, that sort of thing. And then we put the bins behind it. And the command we gave it was, digit, clean up this mess. Just like that, digit, clean up this mess. So we generally have a screen where you can see how digit is thinking. And it’s relying on the LLM, obviously. And the command goes out. So digit has to execute that command. It uses computer vision. It looks at the space that it’s in. It sees trash. It assesses the type of trash, walks over, picks up the trash, and puts it in the appropriate bin. And so that’s the semantic layer or the execution layer of giving digit a command. By the way, just interesting side note, one of the pieces of trash on the floor was bubble wrap. And the digit picked it up and put it in the landfill. And the engineer said, oh, that’s incorrect. It should be recyclable. It’s plastic. It should be recyclable. Well, it turns out bubble wrap is not recyclable. So digit was smarter than our engineers on that day, apparently. But digit put it in the right area. So then the layers below, the coordination, the skills, the actual physics of the robot, all of that is inclusive of what we call the physical layer. And that is the area that Agility feels we own. We have honed that over time. We started initially with reinforcement learning, and it helped us as digit was coming to life, basically the physics of the humanoid. And then we use it, and we use a number of tools that are out there in the industry to teach it new skills, to help with its coordination, to make its route more efficient, that sort of thing. But aside and apart from all of that, at the very, very bottom level, you have to have a robot that’s capable of physically standing. That’s hard. That’s not something that you can turn to AI and say, make me a robot, because there’s not enough data out there yet in humanoids, and particularly humanoids operating in real environments. So the LLMs had the entire internet to train on. When you think about, OK, the physical AI of humanoids doesn’t quite exist. In fact, I would say we may have the largest data lake of actual operating robotics data in real-world environments, just because we’ve been in space for so long. And that’s been a real advantage for us as we built our own physical AI. But the robot has to physically be capable of standing. And the mechatronics of that is very complex. In fact, I think Elon Musk said something about humanoids are even more complex than autonomous vehicles, given how they have to operate in these very constrained environments. Roads at least have some sort of discipline to them. Most of the areas that humanoids will be operating in don’t. So you have to assess all that onboard and make the right decisions. It’s a complex physical problem, first and foremost. Then the physical AI can help hone the skills over time.

Connie: Speaking of reinforcement loops, I’ve seen you work a little bit with NVIDIA, and it just released a new system for self-improving robotics, which seems really exciting. But it does seem like those loops are a lot slower than they are for something like coding. And so in addition to being in the real world and learning and learning, are you using simulation software, or how else are you trying to speed that along?

Peggy: Yeah, we use motion capture. We use simulation software, and we tele-op as well. When the robot’s working on the floor, it’s autonomous. On the commercial sites, it’s autonomous. It’s following the workflow. But all of those three areas are very helpful for different parts of the stack. And we use all three of those to help train a new use case, maybe a new skill. We want digit to be able to do something that requires some amount of dexterity. And they’re used in different ways, but all three are powerful tools. But we use them in combination. We don’t say 100% rely only on tele-operations data or 100% on motion capture. It’s a mix of all of them.

Alex: You mentioned the experience that your company has had in building robots. You’ve been in market longer than anyone else. Digit has logged over 65,000 hours of real-world operation. Have there been mishaps? A robot falling, a near miss with a worker? What have been the biggest safety learnings from actual deployment?

Peggy: Well, it’s interesting. From the very start, our founders were focused on safety. These are big mechanical pieces of machinery. And I think even when you go into factories today, there’s a lot of automation, and some parts are moving, and there’s a line in the floor, and you don’t cross the line. And it’s just taken for granted that one side of the line is all of the moving automation. On the other side of the line, humans can walk. And I think early on, and we were fortunate that our founders had that sense of the level of safety that was going to be critical for these to operate in the real world. And I can’t say there was never a fall. Robots do fall. I don’t know of any incidents where agility workers were hurt at all. But again, they take the right and proper safety moves in our factory so that that doesn’t happen. When we’re testing out new skills, the robot’s on a gantry. So there’s a rope, if you will, or a sort of a metal cable behind the robot that should the robot have a mishap, it won’t fall on somebody, but it can be brought down slowly to the ground with that gantry in place. And what we’ve figured out how to solve, but it’s definitely highly complex, is how do you allow for one of these big, powerful pieces of automation to move among humans? We worked on it for quite some time, and our engineers came up with a solid solution that will allow us to come out of the work cell and open up those new markets. But it means having a lot of compute on board. You have to be able to assess instantaneously that what’s coming towards you is a human and take the appropriate action as the human gets closer and closer. A humanoid is dynamically stable. And as the humanoid gets within a certain radius, we have to take action to bring ourselves down to the ground and not drop the payload we’re carrying and allow the human to pass by. And then the robot can go on its way after the human passes by. Very complex. It’s a mix of AI, lots of sensors, lots of cameras, lots of software. Not easy.

Alex: Yeah, and also, I think you’d want to do this without disrupting the routine of the plant, right? You need to sort of adapt to the situation of the workspace.

Peggy: Yeah, and the robot has some knowledge of that, obviously. First of all, it’s done an assessment of the surroundings, and it’s assessed everything it sees. It has to tell the difference between a cement pole and a human and a picture of a human. All of those things have to be assessed properly and understood, again, on board the robot. If a human approaches quickly, you don’t have time to bounce up to the cloud and come back. Everything has to be assessed right then and there. Not easy to do, but will allow for robots eventually to come into our homes. We actually think the home environment is a ways off because at least in these facilities, there’s some discipline, right? There’s straight aisle ways. There’s stationary equipment that doesn’t get up and move around that digital and robots have an awareness of. Homes are chaotic environments. There’s dogs and babies and somebody sets something down in the middle of the living room that wasn’t there a few minutes before. All of that has to be assessed very quickly, and the humans have to be kept safe through all of that. So we think they will eventually come into the homes. We think it’s quite a ways off, though.

Connie: Like 10 years or longer?

Peggy: Maybe 10 plus. Yeah, probably 10 plus. I think a good example is our autonomous vehicles. The capability has been around for a while, but then there’s so many other elements of it that you have to take into account before actually launching a system, like what Waymo did. And it’s step-by-step. It’s sequential. Each step, you can learn from the previous step, and all of that adds up to eventually a safe autonomous vehicle system.

Connie: Sure. You know, Peggy, again, I mentioned Figure, although it’s kept much of its actual performance data private. Apptronics Apollo is deployed at Mercedes-Benz. Boston Dynamics Electric Atlas has its entire 2026 production committed to Hyundai and Google DeepMind. Tesla Optimus, I don’t even know what’s really happening there, but it doesn’t have any external customers yet. There’s Chinese competitors that seem to have really gained a lot of ground: Unitree, Agipot. Who keeps you up at night? I mean, is it Figure AI, Apptronic, Tesla, or these Chinese manufacturers? Who’s sort of like most directly in your rearview mirror?

Peggy: Well, first, I’ll say I’m actually glad they’re all out there. Agility was around for a long time, and there were very few other companies in the space. I would say with maybe the exception of Boston Dynamics. They’ve been a long-time very capable partner with two other different quadruped and a big-arm type of robot before their current focus now on humanoids. So it’s good to have others out there. It helps lots of things. Obviously, sensors, the price of sensors have come down. Some of that was due to the autonomous industry. We use much the same type of sensors, cameras, LiDAR, that kind of stuff. So that’s helpful. It’s also helpful for the education system. We’re starting to see more young graduates coming out of mechanical engineering school. All of that has been very, very helpful. We, though, are the only humanoid that’s actually deployed. And so really, from a commercial standpoint, we feel like we have the lead. We’ve had to figure out how to literally walk into these facilities and take on real work and get paid to do it. Our biggest competitor right now is just us, how quickly we can execute, how quickly we can continue to add new skills. I would say we have a lot of admiration for Boston Dynamics. I think they have a great team, and they know how to commercialize. That’s a step I think a lot of robotics companies aren’t as focused on. Demos are great. Obviously, they show off great capability, dexterity, manipulation, all of that. But how does that transfer? How do you take that tech and transfer into a real commercial offering? Boston’s done that with their products. So that’s the bar that all of these companies will have to meet in order to be deployed.

Connie: You know, I worry that people are overpromising what’s around the corner.

Peggy: Yeah, again, I would probably view that through a safety lens. It’s great to see it in a lab. Obviously, more and more capability in these devices will help accelerate the whole industry. I don’t like to see it when it’s around people and things go awry. You have to take safety seriously, the critical point in order to scale is to meet the safety bar. And that’s a hard lesson. And you have to build it in from ground zero. It’s not like you can build your robot and it does a lot of cool things in the lab and you say, well, now I’m going to make it safe and bring it into these facilities. That’s a redesign. You have to have all of the safety-certified electrical system, all of the parts, and the software to support all of that. So it’s an undertaking to check that box. So I would like to see the industry as a whole be more focused on safety. And maybe because we’re out there deploying in the real world, we know what a high bar that is. And that’s how the others will get out there as well. So it really does need to take safety seriously.

Alex: Circling back to the beginning of our conversation, you’re going public before any of your standalone humanoid competitors. That gives you an advantage because you get all of this great cash, but it also exposes you to public market scrutiny. Are you ready for that scrutiny?

Peggy: We are ready. My management team, many of them have worked in public companies, so I’m very familiar with all of the many things that are part of being a public company, the guidance, the type of, as you said, scrutiny that you get that we’ll be subject to. But we’re comfortable in those environments. Nothing will be easy. We don’t take anything for granted, but we’re familiar and comfortable and ready to go. Great, Peggy.

Connie: Well, thank you so much for making time for us today. We really appreciated learning so much more about the company. I do think the market’s going to be really interested to learn what you’re doing and see it in action. Hopefully, we can talk again in the months ahead too for an update.

Peggy: I’d really enjoy that. Thank you. Thanks for all your time today.

Alex: The Strictly VC Download podcast is hosted by TechCrunch editor-in-chief Connie Loizos and me, Alex Gove of Strictly VC. Strictly VC Download is produced by Maggie Nye, with editing assistance by Teresa Lanconsolo and Kel. Thanks for listening. We’ll see you back here next week.

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Additional Information About the Proposed Transaction and Where to Find It

The proposed transaction will be submitted to shareholders of Churchill for their consideration. Churchill intends to file a registration statement on Form S-4 (the “Registration Statement”) with the U.S. Securities and Exchange Commission (the “SEC”), which will include preliminary and definitive proxy statements to be distributed to Churchill’s shareholders in connection with Churchill’s solicitation of proxies for the vote by Churchill’s shareholders in connection with the proposed transaction and other matters to be described in the Registration Statement, as well as the prospectus relating to the offer of the securities to be issued to Agility stockholders in connection with the completion of the proposed transaction. After the Registration Statement has been filed and declared effective, a definitive proxy statement/prospectus/consent solicitation statement and other relevant documents will be mailed to Agility stockholders and Churchill shareholders as of the record date established for voting on the proposed transaction. Before making any voting or investment decision, Churchill and Agility shareholders and other interested persons are advised to read, once available, the preliminary proxy statement/prospectus and any amendments thereto and, once available, the definitive proxy statement/prospectus, as well as other documents filed with the SEC by Churchill in connection with the proposed transaction, as these documents will contain important information about Churchill, Agility and the proposed transaction. Shareholders may obtain a copy of the preliminary or definitive proxy statement/prospectus, once available, as well as other documents filed by Churchill with the SEC, without charge, at the SEC’s website located at www.sec.gov or by directing a written request to Churchill Capital Corp XI, 640 Fifth Avenue, 14th Floor, New York, NY 10019.

Forward-Looking Statements

This communication includes “forward-looking statements” within the meaning of the federal securities laws. Forward-looking statements may be identified by the use of words such as “estimate,” “plan,” “project,” “forecast,” “intend,” “will,” “expect,” “anticipate,” “believe,” “seek,” “target,” “continue,” “could,” “may,” “might,” “possible,” “potential,” “predict,” “should,” “would” or similar expressions that predict or indicate future events or trends or that are not statements of historical matters, but the absence of these words does not mean that a statement is not forward-looking. We have based these forward-looking statements on current expectations and projections about future events.

These statements include: statements relating to, without limitation: our ability to consummate the Merger and PIPE Investment and the satisfaction or waiver of the closing conditions set forth in the Merger Agreement and Subscription Agreement; the occurrence of any other event, change or other circumstances that could give rise to the termination of the Merger Agreement or Subscription Agreements; projections of market opportunity and market share; estimates of customer adoption rates and usage patterns; projections regarding Agility’s future development plans; the timing and success of Agility’s future development plans; the ability of Agility to implement its strategic initiatives and continue to innovate its existing products and services; the potential for share price appreciation; the expected timing of announcement and close of the potential transaction; Agility’s economic opportunity and total addressable market; the expected amount of gross transaction proceeds and the planned pre-money valuation of Agility; expectations regarding Agility’s ability to attract, retain and expand its customer base; Agility’s deployment of proceeds from capital raising transactions; Agility’s expectations concerning relationships with strategic partners, suppliers, regulatory bodies and other third parties; Agility’s ability to maintain, protect and enhance its intellectual property; future ventures or investments in companies, products, services or technologies; development of favorable regulations affecting Agility’s markets; the potential benefits of the proposed transaction and expectations related to its terms and timing; and the potential for the combined company to increase in value.

These forward-looking statements are provided for illustrative purposes only and are not intended to serve as, and must not be relied on as, a guarantee, an assurance, a prediction or a definitive statement of fact or probability. Actual events and circumstances are difficult or impossible to predict and will differ from assumptions, many of which are beyond the control of Agility and Churchill.

These forward-looking statements are subject to known and unknown risks, uncertainties and assumptions that may cause Churchill’s actual results, levels of activity, performance or achievements to be materially different from any future results, levels of activity, performance or achievements expressed or implied by such statements. Such risks and uncertainties include: that Agility is pursuing an emerging technology, faces significant technical challenges and may not achieve commercialization or market acceptance; Agility’s historical net losses and limited operating history; Agility’s expectations regarding future financial performance, capital requirements and unit economics; Agility’s use and reporting of business and operational metrics; Agility’s competitive landscape; Agility’s dependence on members of its senior management and its ability to attract and retain qualified personnel; the potential need for additional future financing; Agility’s ability to manage growth and expand its operations; potential future acquisitions or investments in companies, products, services or technologies; Agility’s reliance on strategic partners and other third parties; Agility’s ability to maintain, protect and defend its intellectual property rights; risks associated with privacy, data protection or cybersecurity incidents and related regulations; the use, rate of adoption and regulation of artificial intelligence and machine learning; uncertainty or changes with respect to laws and regulations; uncertainty or changes with respect to taxes, trade conditions and the macroeconomic environment; the combined company’s ability to maintain internal control over financial reporting and operate a public company; the risk that the proposed transaction may not be completed in a timely manner or at all, which may adversely affect the price of Churchill’s securities; the failure by the parties to satisfy the conditions to consummation of the proposed transaction, including the approval of Churchill’s shareholders; the possibility that required regulatory approvals for the proposed transaction are delayed or are not obtained, which could adversely affect the combined company or the expected benefits of the proposed transaction; the risk that shareholders of Churchill could elect to have their shares redeemed, leaving the combined company with insufficient cash to execute its business plans; the level of redemptions of Churchill’s public shareholders; the ability of Agility to grow and manage growth, maintain relationships with customers and retain its management and key employees; costs related to the proposed transaction; the occurrence of any event, change or other circumstance that could give rise to the termination of the business combination agreement; the outcome of any legal proceedings or government investigations that may be commenced against Agility or Churchill; failure to realize the anticipated benefits of the proposed transaction; Agility’s estimates of expenses and profitability; the evolution of the markets in which Agility competes; the ability of Churchill or the combined company to issue equity or equity-linked securities in connection with the proposed transaction or in the future; and other factors described in Churchill’s filings with the SEC. Additional information concerning these and other factors that may impact such forward-looking statements can be found in filings and potential filings by Agility, Churchill or the combined company resulting from the proposed transaction with the SEC, including under the heading “Risk Factors.” If any of these risks materialize or assumptions prove incorrect, actual results could differ materially from the results implied by these forward-looking statements. In addition, these statements reflect the expectations, plans and forecasts of Agility’s and Churchill’s management as of the date of this communication; subsequent events and developments may cause their assessments to change. While Agility and Churchill may elect to update these forward-looking statements at some point in the future, they specifically disclaim any obligation to do so. Accordingly, undue reliance should not be placed upon these statements.

In addition, statements that “we believe” and similar statements reflect Churchill’s beliefs and opinions on the relevant subject. These statements are based upon information available to us as of the date of this communication, and while we believe such information forms a reasonable basis for such statements, such information may be limited or incomplete, and Churchill’s statements should not be read to indicate that we have conducted an exhaustive inquiry into, or review of, all potentially available relevant information. These statements are inherently uncertain and investors are cautioned not to unduly rely upon these statements.

An investment in Churchill is not an investment in any of Churchill’s founders’ or sponsors’ past investments, companies or affiliated funds.

The historical results of those investments are not indicative of future performance of Churchill, which may differ materially from the performance of Churchill’s founders’ or sponsors’ past investments.

Participants in the Solicitation

Churchill, Agility and certain of their respective directors, executive officers and other members of management and employees may, under SEC rules, be deemed to be participants in the solicitation of proxies from Churchill’s shareholders in connection with the proposed transaction. Information regarding the persons who may, under SEC rules, be deemed participants in the solicitation of Churchill’s shareholders in connection with the proposed transaction will be set forth in proxy statement/prospectus statement when it is filed by Churchill with the SEC. You can find more information about Churchill’s directors and executive officers in Churchill’s final prospectus related to its initial public offering filed with the SEC on December 16, 2025. Additional information regarding the participants in the proxy solicitation and a description of their direct and indirect interests will be included in the proxy statement/prospectus statement when it becomes available. Shareholders, potential investors and other interested persons should read the proxy statement/prospectus statement carefully when it becomes available before making any voting or investment decisions. You may obtain free copies of these documents from the sources described above.

No Offer or Solicitation

This communication does not constitute an offer to sell or the solicitation of an offer to buy any securities, or a solicitation of any vote or approval, nor shall there be any sale of securities in any jurisdiction in which such offer, solicitation or sale would be unlawful prior to registration or qualification under the securities laws of any such jurisdiction. This communication is not, and under no circumstances is to be construed as, a prospectus, an advertisement or a public offering of the securities described herein in the United States or any other jurisdiction. No offer of securities shall be made except by means of a prospectus meeting the requirements of Section 10 of the Securities Act of 1933, as amended, or exemptions therefrom. INVESTMENT IN ANY SECURITIES DESCRIBED HEREIN HAS NOT BEEN APPROVED BY THE SEC OR ANY OTHER REGULATORY AUTHORITY NOR HAS ANY AUTHORITY PASSED UPON OR ENDORSED THE MERITS OF THE OFFERING OR THE ACCURACY OR ADEQUACY OF THE INFORMATION CONTAINED HEREIN. ANY REPRESENTATION TO THE CONTRARY IS A CRIMINAL OFFENSE.