Thank you, and good afternoon, everybody. I must say that I am really happy, Howard and Bruce, that you said the markets are getting right, that AI is going to change the world and transform business, because otherwise my presentation might be a little awkward. I am here to talk to you a little first about BBU, of course, and why we think this is the perfect environment for our business. I am really excited to have a few of my partners and colleagues here with me as well, who will speak about various other parts of the business, including Nate Harbacek, who is here with us from OpenAI, Co-Founder of OpenAI Deployment Company and Head of Global Business at OpenAI, to speak with David Bonasia and Katie Zorbas, Managing Partners in our group, about the implications and deployment of AI across enterprise.
Something we have been talking about for a long time, but it is good for you all, for all of us, to hear about it more directly from those working closest with it. At the end, of course, Jaspreet will wrap up how this all comes together for BBU and our performance. To start with, I would like to say that if you think back, markets have always paid up for businesses that could scale fast, often asset-light companies. Today, markets are also paying up for things that cannot be tipped over. They want businesses that can withstand the test of time and still be around in a future where AI will transform many companies in times to come. We have been up here on the stage talking about how if we are in the fourth industrial revolution, that key word is industry, industrial.
How does all this technology help real-world businesses, the industrial economy? We are thrilled that today, it is actually universally agreed by all of the leading tech investors in the world. In fact, many of those tech investors have actually become, from asset light, asset heavy themselves, where they are focused today on things like land, steel, chips, and power to run their businesses. That has created a flow of capital, beyond what we have probably seen in the past, to businesses that have real durable cash flows. These are heavy asset businesses that have real manufacturing footprints, real supply chains, real distributed networks. They have real moats and they provide an essential product or service to their customers, and they can be profitable or cash flow generative through all cycles. These are companies that are not disrupted by AI, but actually propelled by it.
In fact, you would not believe how many times we buy a business that has a billion dollars of revenue, $250 million of EBITDA, and they are still barely using Microsoft Excel. Forget about a CRM or an ERP, and not even close to utilizing AI at scale to propel that business. The opportunities for these companies are bigger than we have ever seen in the past. The thing is, it is no longer some good enough to just own a good business. We can identify what some of these great businesses are. We can acquire them.
But you truly need to be able to operate them better, because not only do you need to apply some of these technological changes like AI that we are talking about, but we are in a world of shifting supply chains, shifting geopolitics, businesses having to navigate and move where they are located, where they actually do their manufacturing. In this heavy asset world, that deep operating capability is key. That is where I think at Brookfield, we have quite an advantage. We have got a history of 150 years of doing this, first on our balance sheet, then later as an asset manager. We have got a culture of operational excellence that prevails throughout the organization.
In industrials in particular, our private equity business has been doing this for decades. Through every cycle, we have invested in industrial companies, some of the best ones in the world that you see on this page, including things like Westinghouse that was spoken about earlier, where we have been able to transform these companies and generate tremendous returns. BBUC, Brookfield Business Corporation, is of course, the largest investor in that private equity strategy, benefiting from all that Brookfield has to offer in this space. That track record is truly exceptional. When it comes to industrial companies alone, we have managed over time to now, actually, this is not marks, this is sales, actually sell 13 industrial companies, generating a realized 5 x multiple on those investments. That is very hard to do across any asset class.
To have done it across industrials over the last 25 years is pretty exceptional. It is not just about what we have owned in the past. It is what we own today in BBUC that is actually going to get far better. These companies that I have got up here on this slide, they are just an example of some of the world's best businesses in whatever sector that they are in. They provide an essential product or service for their customers, one that their customers cannot live without. They make most of their money in actually aftermarket and often service. They have a leading market share with their clients, and they are doing something that is not actually going to change. They will benefit from the tailwinds of AI.
They will not be pushed out as a result of it. The capabilities to transform these businesses are just as important. We can own all of these companies today, but what are we going to do with these businesses going forward? You need to have the capability, which is our in-house operations team, about 35 people, most of who are former C-suite executives from industrial companies, working closely with the management teams across our business, using a repeatable value creation playbook to drive value in these industries. People often ask me, what is it that our ops team really do? I actually think the most important thing that they do that we do now is pattern recognition.
We have done this so many times across so many businesses and so many markets across various cycles that we have kind of learned over time by making some mistakes and correcting, of course, what works and what does not, and what are some of the low-hanging fruit that we can apply into a business almost immediately. When you get better at implementing those changes, sometimes even before we close on an acquisition, we see the results of that transformation much faster and with much more impact. Some of these results are spectacular, and they really speak for themselves. At BRK, one of our key portfolio companies, we have increased margins by 1,100 basis points since we acquired it. At Chemelex, our electric management and heat solutions business, we have managed to grow EBITDA 14% every year in what was previously thought to be a relatively slow business.
Of course, in Clarios, we have generated $4 billion of cumulative cash flow. That is $4 billion in one company since acquisition. I can tell you for Clarios, in particular, that is not even scratching the surface of the cash generation we have coming up in the next few years. All in, across the whole portfolio, we have grown EBITDA on average 11% annually since our acquisition of all of these businesses, and that is far higher than the EBITDA growth in these businesses prior to our investment, which means the way that we are driving this growth is through deep operational turnaround or activity in the companies. All of this is before AI. This is everything we are doing if there was no OpenAI, if there was no technology shift in the world today.
We are thrilled to have partnered with OpenAI as a lead founding partner in The OpenAI Deployment Company to accelerate deployment in enterprise. Our belief is the technology, of course, is already transformative. It is only getting better. You are going to hear more about it from the panel we have coming up. The technology is not really the bottleneck. The bottleneck is deployment of that technology in enterprise at scale. We need to see real productivity gains in businesses. That is how all of this investment in the technology makes sense, and that is going to happen because people are deploying it better. We made this investment in BBUC both because we genuinely believe it is going to be an incredible investment that will generate tremendous profits for us, of course.
More than that, we saw an opportunity to partner with OpenAI across our entire portfolio, and we already have real live use cases of things we are doing on the ground that are already going to hit the financials, and we just have not seen the full impact of it yet. The best part is, with everything I have described and everything we have in the portfolio today, we are truly set up today to continue to compound our business and grow. First is, when you look back to when BBUC was spun out of Brookfield, the businesses we own are very different than the ones that are in the portfolio today. Our businesses today are higher margin, they are larger, they are bigger scale, they have larger market share, and they are overall higher quality than we had 10 years ago.
We have also started to build the next generation of industrial leaders. That is a great part of our business. Jaspreet will talk about how some of our portfolio is early, some of it is evolving, and some of it is mature. What we often talk about is things like Clarios that are already mature and are already leaders. That next generation of leaders is coming. Businesses like Chemelex or businesses like Fosber, Gregg, they are going to be the next Clarios of our portfolio, and we have started that work already. We have been able to buy these companies at 9x EBITDA. When we acquired them, they were already 25% EBITDA margin businesses. That means that they were high-quality businesses to begin with. Our base case operational plan sees us increasing margins by about 500 basis points.
Now, if you take that forward, that means on just those four companies alone that we bought in the last year, it adds about $300 million of EBITDA to those businesses simply with the operational initiatives we already have on flight. At a 10 x multiple, that's a $3 billion additional equity value for the portfolio. At BBUC's share, it's about $4 a share, and that's without us making another acquisition, and that's only taking the last four companies we invested in. As we do this, as we improve these businesses and we make them better, the next most powerful thing we can do is when they get to the right scale, size, and maturity level, is recycle those businesses and sell them. We've significantly increased our rate at which we recycle capital.
If you look back in our first five years, we did about $800 million a year, and today we're averaging about $1.6 billion today. That's almost 2x the recycling that we used to do in the past. We take that capital, and we put it back to work buying other great businesses that we know we can transform. On that note, we talked about a $2 billion recycling program last year that we thought we'd do over many years, and we're already about $1.4 billion through it. We're doing quite well on our way to achieving our target.
Now, that coming full circle gets to the flywheel that we've developed. We buy great businesses, we make them better with our operational improvements and our capabilities, and then we exit them at the right time, and we put the money back to work with the same team that has been doing this for decades, that knows how to do it. That's 30 monetizations that we've done, generating a 19% IRR to date on $9 billion of cumulative proceeds. Those sales were, on average, done at a 12% premium to the NAV that we have on our books. We're selling businesses higher than we've even got them in our books. We put that money back to work again and again and again. That flywheel is really what you're investing in.
What you're getting in BBUC is access to this market-leading industrial and heavy asset services private equity group that knows how to make money and do this again and again, using the access and the opportunities we have across Brookfield. I'll leave you with three things before I hand it over to my colleagues and to Nate. The market is paying up for businesses just like the ones we own, businesses that can't be tipped over and will withstand the test of time. We have the scale, we have the capability, and we have the track record to do this and keep doing it, and to actually make enormous amounts of profits in this environment. This is the right time for us.
Our flywheel of buying great businesses, improving them, selling them, and putting it back to work has been proven, and we have demonstrated those returns for investors to see. With that, I would like to pass it over to Katie, Nate, and David.
Please welcome our panel, moderated by Katie Zorbas, with panelists Nate Harbacek and David Bonasia.
Good afternoon, everyone. I am Katie Zorbas, and I am a Managing Partner in the private equity group. I am pleased to be joined here today by my colleague David Bonasia, Managing Partner out of our New York office and Head of our Business Operations Group within Private Equity for the Americas region, and also leads our AI Value Creation Office across Brookfield. We are very lucky to be joined by Nate Harbacek, who is Head of Global Business at OpenAI and Co-Founder of the OpenAI Deployment Company. We are incredibly proud to have recently announced the closing of this newly formed partnership with Nate and OpenAI, the Deployment Company—
Thank you.
—which is, as Anuj mentioned, a newly formed platform to actually roll out AI at scale into enterprise on a large-scale basis. We were one of the founding sponsors of this platform. We invested alongside a consortium of investors investing over $4 billion into this endeavor, and we are happy to dig into that opportunity today. Nate, why don't we start with the problem that leads to the opportunity? Where you sit in OpenAI today, you see AI adoption play out across a broad range of industries. Capabilities are obviously advancing incredibly quickly. What are you seeing? Where are companies struggling to actually deploy the AI and roll this out into their business?
Thanks, Katie, and thank you for having me. Thanks this room for the capital and the investment in the Deployment Company. I am excited to be here. Your question was about enterprise adoption. It is really interesting. When you look at it, on the frontier, the capability of the models is really increasing exponentially and accelerating. When you look at adoption and impact in the enterprise, in most legacy businesses, that curve lags. You don't see that lag with digital native companies and startups in many cases because they don't have the legacy systems and data infrastructure and policy and procedure gaps that exist in a legacy business that's embracing new technology.
Where we see the problem and where, in part, Deployment Company was formed to help address and solve and bring companies that are kind of behind that frontier curve onto the frontier so they get the benefit of frontier model evolution is to effectively solve three things. The first is to stitch together legacy systems and that data into a really rich context layer that allows the model within their harness to sit within an organization. The second thing that we try to do is teach the technology leaders and the business leaders in an organization how to embrace the technology. It's both use of the products and the harnesses themselves, but then also how to think about context, permissions, compliance, use case definition, and deployment.
If we are doing our job right with a legacy business, you will see them embrace the technology, deploy both our models and their harnesses, and then in partnership with the Deployment Company and their internal teams, start to roll out use cases that bring them up the curve. You see it in digital natives and startups. They don't have the same amount of tech debt that you see in a legacy business. You also see it in industry leaders that are willing to embrace change and innovative. We have really good examples in life sciences, in infrastructure, in energy, and in financial services of organizations that understand that if you give this frontier technology the right context and the right access and just deploy it against the right problems, you can start seeing very rapid and revolutionary change.
Before we get a little bit more into actually the Deployment Company platform, Dave, with your operator hat on, maybe just tell us a bit about how this resonates when you look across Brookfield?
Yeah, absolutely. I would think about it probably in two or three categories. I think there's one overarching element to it as well, and Howard touched on this. We feel this is a pretty revolutionary technology, and it's going to change how global economies work and how people live. What comes with that is it's new. It can be scary for people. In some ways it's intuitive, but it's also actually, as the models get better, they get more complex. There's three things that we've seen in our portfolio that I think really matter. It's about leadership, prioritization, and then the actual operational know-how to get it done. When I think about leadership, even at Brookfield, Bruce is an advocate for deploying AI to create value. Anuj, all the CEO platform leaders are advocates for it.
Going into the portfolio companies, we don't successfully deploy it if it's an IT technology-driven AI for AI's sake. It has to be driven by leadership. I think that's the first piece. The second piece is maybe more around the value creation side. You can't do 50 things across a company. You have to start with two or three prioritized areas, and they have to align with what are the actual priorities of the business. So it has to be meaningful. It should align with how can you actually really create value. Our businesses doing it successfully have a really good prioritization framework. The last thing I'd say, this is messy and complex, right? I think what we're seeing is the technology capability is here, the ability to execute is here.
This is, I think, where Brookfield's strengths play into it, where when you think about getting into a business, getting into the manufacturing plants, in the headquarters, understanding processes, understanding where the data is, the systems, that's what actually separates deployment, which I think really resonated when we were talking to OpenAI about their thesis for DeployCo. It really aligned with it nicely.
Nate, let's jump a little bit into DeployCo itself. Why did OpenAI feel like this was the right model to address these challenges? Maybe elaborate on what Dave mentioned just with respect to what Brookfield actually brings to the table.
Yeah, I'm happy to. OpenAI was founded both as a research and a Deployment Company. It's in the mission for the business. Our job is both to push the frontier from a research perspective, but then also deliver the technology to all of humanity, and that is both at an individual level and at an enterprise level. When we thought about deployment, this really had to be an ecosystem play. We were building an army of entities and people that could deliver the technology to the world. That was not just OpenAI, that was not internal researchers and applied researchers and forward-deployed engineers that existed in the business. It really needed to be a collection of people, some of which sit in DeployCo, but then also an ecosystem that we can enable.
When we thought about this, we wanted to build a purpose-built entity that was focused on elite, forward-leaning enterprise deployment, and it brought along private capital and operators that controlled thousands of businesses that could be a sandbox for us to think about deployment. It was going to be consulting firms, and it was going to be GSIs. If you look at it, the cloud practices inside of a Capgemini or an Accenture or any of the GSI businesses, those are tens, if not multiple tens of thousands of people that understand how to do this for a technology that's a little bit more static and less dynamic than AI. When we thought about the Deployment Company, it was let's build a purpose-built entity who has and trains the leading forward-deployed engineers. They're going to be hands-on-keyboard inside of a business, driving, teaching, and enabling change.
We were also going to teach our partners in the forms of private equity investors and our alliance partners how to do that work. When I think about Brookfield, to me, it's pretty obvious. It was funny, I was talking to Bruce about this when we were sitting beforehand. I met Bruce actually at a sovereign energy company. He and I were halfway around the world, and he was talking about infrastructure and capital and being an operator and transforming in that regard. I was talking about AI transformation. It was very clear that there was a resonance in thought. He introduced me to David, and to me, within about five minutes of talking to somebody, you can really understand have they understood the technology? Are they working in it, or are they thinking about it?
It was very clear to me, even in those first conversations, that Brookfield was already forward-leaning and action-oriented when it came to AI transformation. I think the second reason is doing this work is hard. It's messy. It's much more akin to open heart surgery than it is going in and doing something quickly. We built the technology, and the models still surprise us more often than even you might think. So we had to find partners that had control investments in businesses that would allow us to get in and figure it out together. That was the second thing. The third is just a horizontal slice of the world economy. When you look at Brookfield, there's infrastructure, there's power, there's energy, there's real operating businesses inside of PE, and there are services.
We wanted a chance to work with each of those different vectors of companies with a partner that was going to let us get in there, figure it out, give us access to subject matter experts that would be able to teach our engineers about the business so we could teach them and partner with them on deployment. With Brookfield, we found all three of those things.
Dave, maybe just elaborate on that within when we think about BBUC, what does the partnership with OpenAI enable within our business that would've maybe been more challenging had we gone about this on a more fragmented business-by-business approach?
Yeah. Absolutely. I think the value we saw is we could marry what we were already doing, which is thoughtful, organized approach. We track use cases, we track value, we track how we are actually driving it, and that kind of more hands-on operator approach that we deploy. But coupling that and learning from a company that is pushing the frontier and is also actually thinking about what is important in the context of deployment, not just creating better models. Being able to couple that and understanding, not just what is happening six months from now or 12 months from now, but to understand, have a little bit more of a forward-looking view and a seat at the table with the likes of OpenAI, we think that could accelerate what we are already doing.
I would say we are already seeing some of that, because what may have been a use case that we scrapped a year ago or a year and a half ago is now we can come back to the table because we are getting a look at where the models are actually going, where the product capability is actually going. I think that is something that would have been difficult to replicate without the partnership.
Let us talk about those deployments themselves. Nate, maybe you can just fill us in a little bit on what actually separates a successful deployment or rollout of this AI versus what would be an unsuccessful deployment in your view.
Yeah, I am happy to, and it is something that I spend a lot of time on. Now that we have done the Deployment Company, it is about, to me, finding the right companies and the right lighthouses by industry to really demonstrate adoption and transformation and change. To me, very simply, it starts at the top. Whether it is an investor or a CEO, they have to be fully bought in to doing this because it is going to be hard. Like transformation in many cases, David said it is revolutionary. This is not a small twist the dial here, change a workflow. In many cases, you are re-architecting how a business operates to enable a capability that was previously impossible to consider. It starts at the top is the first thing.
The second thing, when you have that alignment at the top, you want to find both a business owner for the first deployment and use case inside the business and a technology team that is willing to embrace change. They have to feel a little bit like cowboys. I've never told David this, but David to me was the cowboy of Brookfield. I talked to David, he's like: No, we're going to do this. We're going to go adopt it. When you have those two things, it actually matters then less what company you start with. It's much more about problem selection, finding a problem that you feel like you can drive value with using AI and demonstrate the power of the technology in kind of a short to middle term.
If you do those three things right, one use case becomes a business transformation inside of a company, becomes a whole company transformation. When you're working with an investor, all it takes are a couple of those transformations inside of the portfolio, and it spreads like wildfire across the entire ecosystem. David, you should talk about it. Clarios to me is a really good example of getting into the business, understanding a messy couple of problems, really getting the technology in there, and then you see this snowball effect and stone-rolling propagation of use cases across the business.
Yeah, absolutely. I think I'd take a step back first, and I can get into Clarios a little bit. I think when we started approaching deploying AI across Brookfield about two years ago, we also wanted to come at it with an operator's lens to it. I said it before, not just AI or technology for technology's sake. We had to think about it like capital allocators, and that's why we called it the AI Value Creation Office to begin with. So we had to have an operating system in terms of identifying use cases. In private equity, we have about 500 use cases, but then it's not just about use cases. We track a funnel around what's a use case, what's a business case, what's in pilot, what's in production.
In private equity, we have about 150 use cases in pilot or production that we estimate will drive a hundred million or more of EBITDA, and frankly, I think that number could be larger in the future.
Think bigger.
Yeah. What we try to do to make this successful is bringing an operating model to it, not just doing it everywhere all at once. That is kind of the foundational piece. I think going to Clarios is unique because it is a big business. It has forward-thinking leadership around technology and innovation. Both the CEO and then the head of digital and AI in the business, you have top-down leadership to say: Hey, we are a great business today, but we can be better, and we can do things differently. A key thing at Clarios, I mentioned, you need to define what are the priorities of the business. Even putting AI aside, what is important to the business? There are two areas in Clarios we have been focused on the last several years.
One was around on-time delivery, and one was around OEE and productivity in our plants. For example, on on-time delivery, it was sitting in the high 70s, low 80s, and that was because we had a static manual process on a monthly basis where we were. It is typically a typical problem in industrial companies of how do you match demand with where your inventory is, where your operations are. We had this static manual process that with AI, we have now been able to make that more real-time, dynamic, and is kind of always running, and the decision-making is not something they dust the spreadsheet off of at the end of the month. As a result of that, our on-time delivery is now in the mid to high 90s.
That actually resulted in about $50 million of EBITDA improvements, both in reduced freight costs, but also a reduction of service penalties with customers. That is one example. Another example, and this is actually a real-time thing that we are doing with the Deployment Company is we have a network of a lot of manufacturing plants under-invested in prior to our ownership. Portions of our network are at less than optimal productivity levels. If we can get 5 points of OEE, 10 points of OEE, there is a nine-figure EBITDA opportunity. We're working on a real-time. There's actually a component of video and a component of leveraging the traditional generative AI models to think about real-time monitoring of quality issues and maintenance issues in real time to basically assess areas where we could have downtime and a loss of productivity well in advance of our traditional methods.
Again, that's the type of thing where two, three years ago, that type of problem, even with advancements around industrial kind of robotics, was a really hard problem that we now think we can unlock with AI and with the Deployment Company.
Clarios is obviously a really good example of this, and you mentioned some of the kind of tangible, measurable outcomes there. We have a lot of investors in the room here today. Elaborate a little bit on what some of those proof points are, or KPIs that everyone in this room should be thinking about as we see this roll out over the next one, two, three years.
For sure. Yeah, we try to make it tangible. So actually in our program and our operating model around tracking use cases, if something is in pilot or production, it has to have a KPI. We measure revenue opportunity, we measure a cost improvement opportunity, or we measure some type of productivity or efficiency metric. If something doesn't have that, well, then it shouldn't actually get approval to proceed as a use case, and it should fall out. So that's the type of things that we look at. I would say generally my ambition is we have a pretty systematic way to think about value creation now, and Anuj talked a little bit about that capability.
My view is in two to three years from now, and we basically have these seven critical levers that we look at every company against. I believe that AI can enable all of them for us to go faster and potentially have bigger impact. So that's kind of how we're thinking about it. I think within that is how do we also create repeatable frameworks and playbooks. I think this is also the benefit of the Brookfield scale, where we now have visibility to what's happening across AI deployments across all of Brookfield, and we can figure out what worked in one company that may be transferable to another company. Given we do own a lot of comparable adjacent assets, there's a lot of ecosystem effect that I think we should be able to unlock in the next two to three years.
Perfect. We just have a minute or two left, Nate. I want to give you the final thought here. Maybe we can just zoom out and think about the potential of what this could become and what this could look like as it continues to scale in the future.
Yeah, I think when you think about the impact of this technology, you kind of need to think about it in almost three timetables. The first is that decade-long timetable where we will be solving OpenAI, Deployment Company, Brookfield, and our partners, the most ambitious problems that exist in industry today. We will be solving problems that we haven't even thought are possible to address in those next 10 years. If you're working towards that 10-year outcome, you're going to build the business and operate in a certain way. When I think about two to three years from now, my hope is by industry, we've created lighthouses that are really aspirational changes, reduction in incidents on industrial sites, more efficient optimization of energy problems or industrial problems or development problems.
You saw this with Jalapeño, the chip that we did, but it's an analogous problem where you can tape out a chip incredibly quickly because you can do closed-looped optimization on design or optimization decisions in an industrial or a computer manufacturing context. To me, the two or three-year timeline, it's about creating those lighthouses to show the world the power of this technology when it's deployed deep within the enterprise. In the here and now, to me, it's about two things. One, we have to deploy the models and the harnesses more effectively in the organization. Going back to my first point, this is about embedding the models inside the business, giving them appropriate context, and allowing people to learn how to use them at work.
If we're successful in the next couple of years in really deploying this technology across a wide range of both companies and industries, to me, the world is just going to see incredible evolution and productivity gains from AI.
Awesome. I think that's an excellent point to end on. Nate, thank you for being here with us. I know I speak for everyone in the room when I say we're excited to see how this evolves and the value that can be created for BBUC and across Brookfield more broadly. With that, I'm going to welcome Jaspreet Dehl, CFO of the Private Equity Group, to the stage.
Good. Okay.
Thank you. Thanks.
Please welcome Chief Financial Officer, Brookfield Business Corporation, Jaspreet Dehl.
Good afternoon, everyone. Thank you for joining us today. I am going to focus on three things. First, talk about the evolution of BBU over the last 10 years, BBUC. I am probably going to do this a few times. The second thing I am going to talk about is why we believe we are better positioned today than we have ever been before. Finally, I am going to talk about what the future holds for us. Before we talk about the future, I want to revisit some of the objectives that we set for ourselves last year, during Investor Day. We had a few things that we laid out that we were looking to accomplish. On capital recycling, we talked about generating $2 billion of proceeds over a 24-month period. I am happy to report that we are well on our way in accomplishing that.
Over the last 12 months, we have generated $1.4 billion from monetizations and distributions up to BBUC. Earlier this year, we completed our corporate simplification, merging our units and shares into one corporation. As a result of that, our trading liquidity has increased about 75%, which is above the 50% that we expected. Finally, we have an active buyback program. We returned $175 million to shareholders, and we bought these shares back at very accretive levels. These achievements build on a much longer track record, which has allowed us to grow and scale our business. The result is that we have a much larger business, but also a materially better and stronger business. To put some numbers around it, when we were spun out of BAM in 2016, the business was generating about $200 million of EBITDA. That has now grown to $2.4 billion.
Over the same time period, our EBITDA margins have improved by 2,000 basis points, and EFO per share has increased from $1.50 - $5.50. That strong operating performance has translated into significant growth in intrinsic value. NAV per share increased from $17 - $56 today, up about threefold over the last 10 years. This growth in intrinsic value has been driven by a simple, repeatable formula. We look to invest for value, we want to improve the businesses that we buy, and we want to recycle the capital to continue to compound. If we think about those three things, over the last 10 years, we have invested $10 billion of capital across 40 investments, and we have been disciplined in making these investments. The average EBITDA multiple that we have invested is at less than 10 x. We have been buying market-leading operations.
This deployment has been supported by our capital recycling engine, which has continued to build momentum over the last number of years. Over the last 10 years, we have generated $12 billion of proceeds. $3 billion through distributions, which continue to grow every year, and $9 billion through the monetization of assets, generating a 19% IRR and a 2x multiple of capital. We have never been dependent on these monetizations on one particular channel. About 60% of the sales that we have made have been to strategic investors, 30% to financial sponsors, and 10% through public markets. This diversification is really important because it means that you can continue to monetize in any market cycle. This recycling engine has also allowed us to change what our business looks like.
We've sold substantially all of the businesses that we had on our balance sheet when we were spun out from Brookfield. They were generally smaller, lower-margin businesses. On average, those businesses generated $60 million of EBITDA and a 15% margin. We took our capital and redeployed it into larger-scale, market-leading operations. On average, these operations generate $300 million of EBITDA, 5x higher than our original portfolio. The EBITDA margin is double at 30%. We've not simply made BBC larger, but we've made it more profitable and of a better quality. We've also made BBUC simpler through the corporate simplification that we completed earlier this year, which resulted in a higher index inclusion as well as better liquidity. We continue to simplify how investors understand and value our business. Starting the first quarter of 2027, we're going to be transitioning to U.S. GAAP.
As part of that transition, based on investor feedback, we're going to be providing quarterly valuation disclosures in our financial statements. As we look forward, we believe we're very well positioned for the next phase of growth. We've built a larger, higher quality, and simpler business. Much of the growth in the future is going to be driven by the operations that we own today. To put that into context, and Anuj alluded to this earlier, we think about our businesses in three stages. There's the early-stage businesses, typically investments we've just made. We're starting down the value creation path. Mid-stage businesses, which we've owned for a few years, and we continue to build value. Finally, our mature businesses that are well along the value creation and are approaching monetization.
When we think about our business today, about 80% of our NAV is working through the value creation plans that we have, while 20% of the NAV of the business is in more mature businesses that are ready for monetization. While we still have a meaningful amount of operational improvement and growth, we also have a strong pipeline of operations and investments that are ready for monetization. To put some numbers around that, we plan over the next five years to generate over $6 billion through these monetizations. About half of that $6 billion is going to come through the monetization of the mature businesses. The other half is going to come from distributions that we get from our operations, as well as the mid-stage businesses progressing their value creation and being ready to monetize. We're going to have $6 billion.
What do we plan to do with that? We've got a plan to invest $4 billion of it alongside Brookfield's private equity strategies, and our investment criteria is unchanged. We're looking for market-leading businesses that are providers of essential products and services with high barriers to entry and durable cash flows. The most important thing is that we're going to self-fund this $4 billion of investment that we're going to make over the next five years from our existing operations. We're going to have $2 billion left over, and we're going to use that $2 billion to strengthen our corporate liquidity, maybe make some strategic investments that will continue the growth of BBUC, and we're going to continue to return capital to shareholders. We've already demonstrated that flexibility around capital allocation.
Since the start of 2025, we've returned about $335 million to shareholders by buying back about 12 million shares at about a 50% discount to our NAV. That intrinsic value or NAV is underscored by our ability to monetize our assets. Anuj talked about the fact that over the last 10 years, we've been able to monetize assets at about a 12% premium, and last year was no different. Over the last 12 months, we've sold three assets, generated a billion dollars, and we sold these assets at a 20% premium to where our NAV was disclosed. This track record highlights the enormous opportunity that's in front of us. Today, BBUC is trading at $25. Our largest and most profitable business, Clarios, represents about $19 of value. What that means is that for $6 you get the entire rest of the portfolio, a portfolio of 20 market-leading businesses.
Anuj talked about some of our industrial businesses, Chemelex, DexKo, Fosber. In addition, we've got large-scale services businesses like Sagen, La Trobe, Nielsen, and all of that portfolio today is trading at an implied price of $6 per share. From our perspective, that represents a very compelling value. There continues to be a significant gap between trading price and NAV or intrinsic value. We think there's several reasons that this gap should narrow. First, our realized track record has consistently validated the carrying value that underpins the NAV. Second, we expect to generate more than $6 billion over the next five years, which will convert a meaningful amount of that value to cash. Third, it allows us to self-fund our growth and leave $2 billion available for other capital allocation. Finally, our corporate simplification and conversion to U.S. GAAP will make BBUC easier to understand and value.
If I bring that all back together, what do we want to leave you with today? First, BBUC is larger, higher quality, and a simpler business. We still have a significant amount of value creation ahead of us, and capital recycling is going to continue to build momentum. Finally, we remain focused on compounding intrinsic value and narrowing the gap between our share price and the value within the business. Thanks. I'll now bring Anuj back up for Q&A.
Thank you, everyone. We'll now take some questions. We are over time, so I'll just take a couple of questions, and again, anyone who spoke is still here in case some of the questions are directed their way. Start here. Oh, sorry. Bart?
Thanks for the great presentation. Going back to the earlier discussion that Bruce and Howard had about interest rates, there is angst in the market today around the rising rates and their impact on private equity. Just curious from your vantage points, how are you thinking about the impact on deployment, monetization, and could you unpack a bit about the 20% of mature portfolio that you expect to monetize? What companies are in there? Thanks.
Sure. I think they said it well, that actually, we do not think this interest rate environment is so crazy in the long term. We have always built our business and prided ourselves on buying companies that could withstand the test of time. So we have always underwritten higher inflation, higher interest rate environments. The problem before was the rest of the world was when interest rates were zero, it made us sometimes uncompetitive. The great thing today is we actually are finding an ability to buy the kinds of businesses we like at good value, because people cannot bank on low interest rates, or an environment that is easy to make money like they could in the past. We have been busier than normal, as you can see with some of the recent investments we have made, and the pipeline is looking quite healthy as well.
For us, this is actually a good opportunity and a good time to put more money to work. In terms of monetizations, as Jaspreet showed, we did three in the last year at a 20% premium to NAV, even in this kind of a market environment. That mature portfolio, there are a few companies in there that we are at a stage where we are probably ready to start exploring alternatives of the ones that we have owned a bit longer in the portfolio. I would say things like La Trobe, we sold a bit of already. It is quite mature in terms of our journey.
Maybe within a little while, that one might be one, or BRK or some of these other businesses that have been in the portfolio a long time. I think that given our track record in the last 12 months, I am not expecting any surprises there.
Yeah. Sorry.
Thank you. Bill Katz from TD Cowen. Thanks for hosting today. So two-part question. If I looked at your NAV calculation, it is only up 3.7% year-on-year, and I am really happy to hear you are going to move to quarterly. When you think about the flight path from here and OpenAI discussion, et cetera, and DeployCo, what is the ramp look like to accelerate the NAV improvement? Then secondarily, related to that, how do we think about capital allocation? You laid out three different paths. Where are you in terms of buyback given that wide discount to the NAV and the stock price? Thank you.
Yeah. I will start with the last question first. I would say our buyback program is still been quite active. That is just given where we are trading today. We are still putting new money to work. Of course, we, from time to time, look at our leverage levels. Those are the three areas that we are most focused on. In terms of NAV, it is some puts and takes over the last year. There are some businesses that we have got that we are working on, and that we have got some challenges in the portfolio, and there is no secret around that, where we have adjusted NAV downwards. That has been offset by some of the other businesses that have been performing quite well, like Clarios, where it has been lifted up. So on balance, it was a year that NAV did not increase that much.
I do think, though, where we are today, based on where we are in every portfolio company, it is a different journey and it is at a different phase of its cycle. But in many of the companies where there have been challenges through the pandemic and after, we are seeing real green shoots and we are seeing real positive momentum. That is before any application or any opportunity that comes from AI, which is more upside beyond the operating plans we already have in place. So I would believe that it would accelerate further in the coming years. Sure. I will take maybe that last question. I think we are on time.
Great. Gary Ho from Desjardins Capital Markets. Anuj, Nate and David talked about how DeployCo is benefiting some of your existing portfolio companies. Are you adding AI efficiency gains in your underwriting, yet? If not, is that a possibility?
It's a good question, actually. I'd say right now, today, we're not modeling in necessarily an additional boost to what we can do in terms of operational improvements with AI in a portfolio company. I think Dave said it well with maybe in the future, this is an actual one of our work streams that will factor its way into the actual underwriting. Today, it's kind of we believe that we can usually go into these businesses, improve margins, and AI is something that we're hoping to do to supercharge or to either do the same changes we were always going to make faster or to have more impact or to have a totally separate impact. But probably it's going to take us a little bit of time of seeing more outcomes of the application of the technology. We know it's there. We know it's real.
Once we see more outcomes, I think it might become a more regular part of our underwriting. Great. Thank you all so much.