Well, thank you everyone for joining us, this afternoon, to speak with AbCellera CEO, Carl Hansen. Thank you so much for joining us.
Thanks, Andrea. Great to be here.
Maybe to start, AbCellera, a company that's positioned at the intersection of biotech and tech, which is hot right now. Maybe for some of the people in the crowd who may be less familiar with your company, can you describe your business thesis here and how that really helps fit you into the drug discovery and development process?
Great. Sure, happy to. AbCellera was founded a little over 10 years ago, the business thesis was essentially that when you looked at drug development across the industry, something strange was happening, and that's that productivity has largely been going down. If you really do the accounting, drug development is and has been, and is getting worse in terms of total economic return. That stands in opposition to a lot of other sectors where we're seeing great productivity gains. Our thesis was that if you started to look at the structure of drug development, and particularly focused on antibody therapies, that there was a lack of investment and a lack of access to technology. Because technology and innovation tend to be the thing that helps to accelerate and improve productivity.
We set up AbCellera now a decade ago with a really big, ambitious vision, which was to rethink and rebuild the part of drug discovery that happens after you have a thesis. After the target biology has been done, and you've got a specification for a target, and it takes it all the way up to the beginning of clinical testing. Because that's the area of drug development, the creation of drugs, where it looks as though technology could have the biggest lever, or the biggest return, and where there are a lot of problems, where solving them once helps you learn and get better in solving the next ones.
AbCellera really was built on the idea of solving that technology problem, using that technology problem with partners to build a large portfolio of positions in antibody therapies that would be developed in the coming years.
Core to your technology is this research engine, this discovery engine.
Right.
You've talked about the investments that you've put in here to build this up, from the front, the middle, the back end of this engine. Maybe speak through some of those components and those technologies that you, that you have that position you to do the antibody drug discovery?
Sure. you know, building off of what I just said, you know, we define technology really as... We take a functional definition of technology, okay. The objective is not to, you know, create a protein and not to do a screen, not to come up with candidates, but to be able to put together all of the pieces that let you go from that idea right through to a drug that goes into patients for testing. To do that with greater speed, to succeed where others have failed, and to do that at a greater scale than has previously been possible.
To achieve that requires that you bring together, you know, technologies as we think about them, various disciplines, computation, engineering, molecular biology, but it's every bit as much about building the teams and the know-how and physical infrastructure and putting that all together. When we say AbCellera's antibody discovery and development engine, that is shorthand for roughly 600,000 sq ft of lab space that either exists or is under construction, for proprietary technologies, for technologies that are at the state-of-the-art, for some technologies that are standard or best practice in the industry, all bound together with people and software and data science to make this thing much more than the sum of its parts. Integration is the theme.
You know, we've been at that 10 years, and we're, I'd say, roughly, you know, two and a half years, two years from the first full implementation of that, and then, of course, it will, you know, get better and stronger from there.
Perfect. Then as you think about, you know, there's many components that make up this engine-
Yeah.
One of the questions that we get asked frequently is, How are you differentiated from others in the field who may be trying to do similar things?
Sure.
Maybe help us understand that differentiation and the moat that you've created around your technologies.
Yeah, I get that question a lot. There are multiple moats. You know, we have within our engine, technologies that we've invented, and they're state-of-the-art, and they're patented technologies. We have built up increasing know-how in data. One of the core, you know, ideas or strategies is that by specializing in doing things, you do it more. You work with people that are sophisticated in the industry, and you build up expertise and knowledge, so that is a moat in itself. The teams, of course, are difficult to assemble, the infrastructure. If I had to, you know, pull it to one thing, I would say it's about complex assembly of technology, people, infrastructure, workflows that allow you to achieve a functional goal, which is bringing an idea to a drug.
To do that faster, to do that at greater scale, and do that with a greater chance of success. As soon as you start to talk about this, people naturally want to, you know, they dig down in the details. "What about your single-cell screening technology? What about your repertoire sequencing, or what about, you know, your transgenic animal?" What I find is that always flips the conversation into a comparison of two things that are not actually in the same category. It's a category error. It's like if you said, "Hey, how does your iPhone compare to someone's touch screen?" It's like a touch screen is a great technology.
It's part of an iPhone, but an iPhone is much more than that, the functional output and what makes it difficult to reproduce is that it's been engineered and brought together beautifully. Complex coordination is when we talk about the company, and for me, that is the moat. We've said before, it's literally the case that if you handed someone, "Here's all the IP, here's the workflows, here's the money, go build this," I think you're waiting, you know, many years for someone to pull it off, if even they can. Because bringing things together is what's difficult in making this actually work.
Interesting. I'm gonna ask you about AI.
Okay.
There's a lot of investor interest-
A lot of hype -
... general interest. Yes. I guess maybe what is your perspective on AI in drug discovery? Is it all hype? Is there something real behind that, and how is your own engine using AI?
Okay, so I'll bite on that. First I'd say AI is not hype, and we're seeing, you know, ChatGPT, large language models, previously AlphaFold. We're seeing results come out of artificial intelligence-based methods that are astounding, and in many cases, have even surprised the people that were developing them. There's a lesson there, which is there is the potential with artificial intelligence and machine learning to get step changes in your ability to predict and to compute and to automate. All that is true. What I find in drug development is that the layperson sees these things happening. You see ChatGPT, and the lesson that they pull out of it is that AI has arrived. AI is now all-powerful. What are you doing about it?
How is this going to disrupt your industry, your field, your business? "Carl, what does ChatGPT mean for your business?" I'm like: Nothing. ChatGPT doesn't mean anything because the real lesson from that is that what it took to make that a powerful technology was the ability to read the entire corpus of written language on the internet, an unbelievable amount of data. That's the first piece, so access to data. The second piece is the ability to quickly generate models and spit out the models and test them and iterate. If you're building a large language model, you have hundreds or thousands of people that are taking the output of the model and saying, "No, that doesn't work. We're gonna tweak it," and you iterate. Right? You have to have experiment and iteration.
In the drug development world, that doesn't exist. Where is the data, like the high-quality data that's well-organized, on which to do this learning? That's something that our business is focused on enabling the creation of, we invest a lot of time in software engineers, in data science, to make sure we're capturing that and keeping it. It doesn't exist out in the real world. If it does exist, and you build a model, now you need the experimental might to test that and iterate. If I was to, like, use artificial intelligence to say, "Here is an antibody drug," how would you know if I'm right?
You would have to make it, you'd have to test it, you'd have to bring it into animals, you'd have to do the development work, and that is what it's going to take to get the same kind of step change improvements in drug development that we've seen in other areas. You know, we've been building our business for 10 years in order to be able to generate data, high-quality data, faster, at greater scale than just about anyone else that I'm aware of. That data connects drug discovery programs from the beginning to the end, there are hundreds of different types of data, hundreds of thousands or millions of data points per experiment, and we've done a huge lift in building modern software systems to pull all that together and organize it.
We're doing that because it's necessary to execute the business. Without modern software, you won't be able to do this kind of drug development at scale. The consequence of it is that you will also, through running your business and helping people solve difficult problems, build up a data asset that is exactly what you need to start building these advanced models. We are very bullish on AI, but we're very cautious about promising what it can do today because I think a lot of that is the hype and will lead to some disappointment. It's kind of a schizophrenic view on it.
Sure. Maybe in your best estimate then, how close are we to generative AI being able to replicate your entire process?
I'd say today we're not close. I'd put that in the realm of science fiction/science fantasy.
Okay. maybe turning to your business model.
Yep.
There are. well, maybe walk through your business model and the different structures that you have and how the economics are recognized for each of these three different setups.
Okay. Sure. Maybe I'll set it up by making a few comments about the structure of the industry and then how our strategy and technology investment implies or fits with the business model. One of the observations we had is that, you know, we think that there's a technical debt in drug development, okay? If we had better technical capabilities, there are opportunities that could be executed on that are not being executed on today. An example would be, you've got a new company, they've got insight in biology. It's a great founder. Maybe they've got backing. There are no labs. There's no know-how, no expertise.
In the industry today, that group has to go out and try to reassemble all of the machinery just to execute on the thing that is novel and unique to them, and they will not have enough time, that leads to either it doesn't get funded or you're wasting capital and time doing that. You've got, you know, companies, big companies, that are the best or have been traditionally the best in the industry at doing this. Their attention has been where the money gets spent and made, which is the clinical development and the commercial sale, and they haven't made it a priority to reinvest in the technologies internally. What's happened now is that there are targets that are exceedingly difficult or that cannot be prosecuted with existing technologies, okay?
That's the gap, we think there are good reasons why people do this, but it creates an opening. Our strategy has been, we're going to step into that and have a long view, do the hard work, build up the engine. Once you've decided to do that, you realize, "I need to use this a lot to make it make sense." That's the reason small companies don't do it. That investment in technology means that you need to work with others, because no company would either have all the ideas to work on or would have the bandwidth or the capital to actually move those through the clinic. Our business model has been very much focused on partnerships from the beginning.
Roughly, we are investing in technology, and we're looking for ways to develop antibody therapies that can be put into the hands of partners for clinical development and ultimately commercial sale, and making sure that we are always having a piece of that product. Deal structures always emphasize a share in the downstream success of a product, typically through a royalty, sometimes through a co-ownership structure. Apart from that, we're not dogmatic in how we do deals, and there's roughly three categories that we've talked about. The first one is what we call discovery partnerships. This is the first type of deal we've done. It's the largest volume, and it is sort of the bread and butter of the business.
If someone will contact us, they have identified a problem in antibody discovery, we have the means to make their molecule and take it all the way through to a value inflection point, which may be the beginning of IND-enabling studies. We'll sign a contract, they send us the work, we take it up to that point and then hand it off, and after that, we're hands-off. We don't control the program, we don't invest in the program, but as that molecule moves forward, we are eligible to receive milestone payments, and most importantly, a royalty, which is a share of the ultimate success of that drug. Those are discovery partnerships. If you think about it, we normally get paid for the work, so that's an infinite return on investment. We get...
For the marginal cost, that's covered by the, by the client or by the partner, and then we're hands-off and we wait, and over time, you know, we'll get royalties, and then, we'll get milestones and royalties, and those come in at a 100% margin. If you do a lot of those and you run the experiment over time, you end up with this P&L that looks like a Royalty Pharma, but hasn't required the huge balance sheet, because it's not a cost of capital play, it's an exchange of access to innovation and capability for a piece of success. I would say a win-win exchange. That's, that's a discovery partnership.
We have a version of that we call co-development, which is largely the same, and typically done with small companies, except that we will sometimes take less payment upfront for the research, so we'll support some of that payment, and we will come out of the initial work with a 50% ownership position. We have an option to continue to financially support that program through various stages of development. Let's say from a final lead candidate to IND filing, from phase one to phase two, and various steps along the way. That's, you know...
If we don't, we still get the royalty, so it's like a discovery partnership, but it has the extra value of an option, and it's an option that we are very well-positioned to decide if we want to participate on, because we know the partner, we've been intimately involved in the experiments and the molecule, we get a chance to see how the commercial landscape is shaping up. Those are the first two. Those are both working on ideas that have come from the outside into AbCellera. There's another camp or another section of the business, which we call pre-partnered programs. Pre-partnered programs arise from long-range research efforts that we have underway that seek to unlock big areas of therapeutic antibodies that are presently difficult or impossible to develop drugs for.
We have two areas right now. One is in T-cell engagers, where it's really about being able to better control T- cell responses, increase specificity, and help to make that class more successful across more indications. Another area that we're very excited about is in complex membrane proteins, which is working on the technologies to let you find antibodies against ion channels and GPCRs. In both cases, there are common technology problems that, if solved, allow you to work on dozens of high-value targets. In that work, we naturally work on targets, and as we start to succeed, we end up having wholly owned assets that we will take up to final lead candidate or perhaps even to the end of phase I.
For all of these, the objective is to hand these off to partners who are best suited to do the clinical development. That's why we call them pre-partner, because it's work that has started, we've anticipated needs, we see an opportunity, and we're ultimately will hand them back to partners. All three of those are part of one strategy, which is build a diversified portfolio to have different risk/reward, different cash flow profiles, and we work to make sure we're balancing that in light of the bandwidth and the resources we have in the business at the time.
Perfect. Maybe some follow-ups there.
Sure.
On the co-development sector, Maybe help us understand what would be the considerations to step into a more, I guess, maybe more fruitful or constructive partnership with a company? I guess, what would make you decide to take on that additional role?
What would make us decide to negotiate a co-development deal or to participate along financially?
The first.
The first one. Okay. The first thing is that type of deal structure is almost exclusively for smaller companies. That's, as you might expect, a large, let's say, a large, well-established pharmaceutical company. They have a low cost of capital. They want to completely control and own the programs, so it's very difficult to negotiate, at the discovery stage, what would be, you know, a 50/50 ownership position. Smaller companies are in a different position, so, very often their cost of capital is high. They don't have any of the capabilities that the pharma partner has, so we bring, you know, a very large value proposition to them, saving them time and money and making sure that we've de-risked and accelerated their path to getting to that point.
We have, you know, better, I wouldn't say better leverage, but a better negotiation position with smaller companies. What we've found is those engagements have been some of the most productive. We're doing them when we find groups we really think they're onto something, groups that have, you know, deep insight into targets or unique technology or business model that we think is compelling. That type of engagement, you know, the nature of it makes it more collaborative, so we've become an extension of that team. In the best cases, this has led not to just one program, but, you know, two or three and a pipeline behind it that we're quite excited about. It's part of the portfolio. We won't do this on all of them. It's a smaller piece.
I think we've done, you know, six or seven to date out of what would be 75 to 80 programs with downstreams. Each of these, you know, on its own, could be material for the business because you own 50% of it, which is also true for the pre-partnered programs.
All right. You've had some interesting partnerships or deals with VCs.
Yes.
Maybe speak a little bit to that and how that feeds into these, I guess, these models.
Sure. When we think about the partner landscape, I'd say there's sort of a two-dimensional grid. There's companies that are, you know, large and well-capitalized and established, companies that are small out of the gate. There's companies that are highly enabled in antibody therapeutics, take the Lilly's of the Regeneron's of the world that are partners, and there are companies that are just starting to have no capability. Now, in that in that bottom left quadrant, let's say, the small companies with no capability, there are a huge number of opportunities. There are probably somewhere like, you know, 700 or 750 antibody discoveries that are coming from that class of company at any given time.
It is a challenge partnering to see all those opportunities and to sift through them to find the ones that are best from our perspective. We, we do not swing at every pitch. We say no to far many, far more partnerships than we engage in. We want to make sure that if we are working with someone, it's because we believe that team, that idea, that investor, you know, has the potential to bring something that will be successful in the market and ultimately create a drug for patients. What we've worked on is, hey, how can we best do this?
It turns out there's a set of institutions in our industry that have made this their business and are very good at it, and these are the top-tier venture capital investors, that are either sourcing and funding those companies or that are starting them up. What we believe we can offer to these investors and the entrepreneurs is the ability to work on ideas that otherwise would be too difficult to get off the ground, to do that with less capital, and to ultimately provide a better return on investment to the investor. One of my favorite examples is one recently, that we talked about a company called Abdera, which is local in Vancouver. There's a backstory, but, you know...
We had, we'd essentially, you know, formed the alliance and committed to work with them before the financing came in to start the company. Our presence, I believe, was one of the main drivers for the financing coming in, because what otherwise looked like a very long slog to get started, could start on the day that the company was formed, with teams that are in place and technology that's already been proven to be, best in class. We, I think the company was founded in early 2021. We started in March 2021. By the end of last year, the first lead candidate was in hand, early this year, that lead candidate led to a Series B financing, a substantial financing of $140 million, with a pipeline behind it.
The labs were only set up, you know, a short time ago, right? It shows that, you know, it allowed the Abdera team, which has created a terrific technology and great executives, to focus on what was unique and special in their business proposition and not have to reinvent everything and accelerate the entire path in making companies. It's our thesis that there are many opportunities like that, particularly, well, obviously, we work in antibodies, where ideas have not been funded because it's too difficult, and it need not be difficult if you've got a partner that has invested in the capabilities and has a business model that allows you to access that for a fair share of the success of the enterprise.
Perfect. One of the key metrics that I think investors look at when they look at AbCellera, and we talk about this each quarter, are the new program starts. The number of new program starts as you bring them into the discovery phase, as you then watch these programs progress towards the clinic. What is the right way to think about the cadence of these new program starts?
It's, it's a great question. It's, it's one we get a lot, and I think what I'd want people to look at is not the volume of program starts. I think it's very tempting to say, "Okay, Yeah, this many starts this quarter, this many starts this quarter. You know, I can estimate some growth, and I can build a model on that," and that's sort of satisfying, but it misses entirely what is most important about programs. What's most important is: What are you actually working on? Is it a good idea? Is it a reputable team? Have you negotiated good terms? Is the commercial opportunity big or not? You know, what is the chance, the technical chance of success?
When we tell our partnering team to go out and source deals, we don't tell them, you know, "Close a certain number of deals per quarter." We say, "Let's make sure that as a company, we are allocating our resources and time, and technology on what we think is going to be the most important work possible." For that reason, I'd say, you know, 80% of the things that come across, you know, the team's desk, we don't engage on because we're trying to be selective. We're selecting on the basis of the partner, the biology, commercial opportunity, chance of success, competitive landscape, deal terms that we can negotiate, and all of that has to come together.
The other dynamic that's happening is we are expanding our capabilities more and more, and so the engagements that we did two years ago, we handed off at a much earlier stage than we do today. Today, we will take molecules all the way through to the final development candidate that's ready to go into cell line development, and now we are in a position to start doing cell line development. In a couple of years, we'll be having the first molecules that are coming off GMP manufacturing, and we're adding clinical and regulatory capabilities. So the depth of work that is happening per partnership or per program is also going up. The final piece is that I'll just highlight is, I get asked a lot about capacity.
You know, in terms of capacity, if you look at the amount of work being done, and in my view, the quality of work, it's gone up remarkably over the last few years. We're doing much more per program. We also have done a lot of work on the pre-partner program side, and we're simultaneously building out the capabilities to go downstream. The way that we think about it is, you know, that we want diversification, but we don't want diversification at the expense of quality.
Perfect. Maybe with that in mind and in that context...
Yeah.
How do you help investors understand the long-duration cash flows that are associated with your business as they think about the value that AbCellera provides?
Yeah. The first thing I'll point out is that, you know, long cash flows or long timelines to cash flows is not a feature of AbCellera's business model, right? It's a feature of biotech. Like, any biotech company that's developing drugs needs to take drugs through the early preclinical and into clinical development, and on average, historically, that timeline has been, you know, 8-12 years. Is about right. We are setting up our business to participate not in the market of, you know, doing, you know, a fee-for-service work in some research, but to participate in the actual end market. Because we participate in the end market, our timelines are going to be similar.
Like biotech, you get evidence that value is being created and there's traction long before you finally get an approval and hit commercial sales. That's the reason why there are biotech companies that are not commercial, that have, you know, large market caps, and people recognize the value because the science starts to get de-risked as things move through. In our case, this is not going to be one thing. It's going to be a portfolio of things that mature over time. We're keeping our eye on the long game, and we believe, and we have seen in negotiations, that by doing that, we believe we can maximize value.
if we try to, you know, put up numbers in volume of partners and try to, you know, maximize revenue today, it will be at the expense of long-term value. That's how we're running the business. yeah.
Perfect.
Okay.
I guess maybe, in this environment, we're hearing a number of companies talk about cutting back on spend. In this lower-spend environment, how is that impacting your business in terms of the partnerships that might be coming through? Have you seen any impact from that?
I've had that question a few times. It's always hard, you know. You get an incomplete sampling of the space, and so it's hard to get a gauge on exactly what's happening. My sense is, you know, just logically, this is a tough time for biotech. I mean, these are down markets. There are fewer companies that are getting funded. Companies are having to prioritize programs. From that perspective, we're fishing in a smaller pond. I think it doesn't impact us that much for a couple reasons, and one is that we are looking for the highest quality opportunities. I believe that even today, the highest quality opportunities are finding capital, and they're moving forward. That's a good thing for the industry and for patients.
If you're, if you're fishing for, you know, the very, the very top of the sector, I think you're more resilient, although perhaps not immune, but more resilient. The other piece of it is that, you know, as I said, with the Abdera example, but, you know, generally with small companies, our model allows you to continue to advance your business and your science, and to do that in a capital-efficient way. Once we have vetted a program and we have conviction, we are tying our economic stakes to the downstream success of that, not requiring a large cash outlay today. I think that business model, you know, which should, honestly, should be attractive in all environments, becomes particularly attractive in an environment like this.
When you talked about your technology being able to speed up the process from ideation.
Yeah.
-to drug discovery, maybe help provide context for what it is in, maybe in the field right now, how can your technologies improve that speed, and where can it ultimately go with your technology?
Sure. When we talk about speed, we're talking about the path from the specification of the drug through to the beginning of clinical development. Right now, if you're really fast, you're doing that in about three years for the best programs, where the biology and the animal models are amenable to that. I think that's probably gold standard in the industry. The average time in the industry is probably closer to five or more. It might even be six. It's hard to actually get a number on that, but it is considerably long. We look to speed that up in a few ways. One way is through technology, and technology in particular, that can start with a much, much broader search of potential antibody space.
The paradigm in antibody discovery for a long time has been, I'm going to do some kind of early discovery, and I get a small number of candidates, and one of them sort of looks like it might be a drug. I'm gonna spend a lot of time working on that and trying to fix the affinity or the developability. That process is what takes time. We have a workflow that just running it through, I believe, is faster than anything that's out there.
The real speed comes from going 100 times deeper at the start, 1,000 times, and taking more candidates through, because you've invested in automation, you've invested in data science, and then you can run all of those candidates through the filters and just let go of the ones that don't make it, and at the end, you end up with more than enough candidates to bring forward. It's not to say you don't need to do any engineering, but you minimize that, and that can greatly shrink the time. The first piece of getting up speed is depth and being able to take the attrition. The second part, which I think is underrecognized, is integration.
One of the reasons we're so excited about, you know, forward integration to translational science, to CMC, to GMP, to regulatory, particularly for small companies, is that the way the sector works right now, you lose, you know, six months, a year, a year and a half, trying to negotiate with contractors, trying to get in the queue, you know, trying to access technologies, and in the meantime, you know, you're losing your competitive advantage against the rest of the world. If we integrate everything and we know what the process is, we can select molecules upfront that fit well into that process, and we can begin work at risk long before you could ever do that with a partner.
Just through execution and integration and understanding from the beginning what you need at the end, we believe you can take, you know, 12 months, maybe more, off a program. That's not so much a technology innovation as just getting your business tight and really understanding the end-to-end workflow.
Perfect. Maybe some quick questions.
Sure.
in the last couple minutes.
Yeah.
You recently announced a co-investment by the Canadian government.
Yeah.
Maybe just help us understand how significant was this and what does this mean for your liquidity?
Great question. You asked about timelines. You know, an important question around timelines is, you know, are you properly financed to have the business turn the corner and survive? The answer to that is yes. You know, we currently have about $800 million in cash, but we have considerably more liquidity, and a big part of that comes from this contribution agreement or a co-investment agreement with the Canadian government, which is a $700 million project and brings about $225 million from the Canadian government and the provincial government, that is directed towards infrastructure building and building up the capabilities and working on programs to take them right through initiation through to the end of Phase 1.
That, that investment, alongside of a previous investment that we had in the GMP manufacturing, adds roughly, you know, another, let's say, $250 million, $300 million liquidity. AbCellera has about $1 billion in liquidity today, in hand. As our business runs forward, what we're going to see is, you know, the research fees will grow, milestones start to add on, eventually, royalties start to hit. Pre-partnered programs have the potential to bring cash flows forward because you're handing things off when they're de-risked, and they can bring large upfronts. Through all of that, when you layer it together, we believe we have a really solid path, to profitability and to not have to raise money again.
That doesn't mean we never will, but we, you know, particularly in this environment, you want to make sure you've got a plan that puts you in charge or in control of your fate, and we're feeling great about that.
Perfect. Last 10 seconds. What should we be focused on over the next 12 months? We really didn't touch very much on the pre-partnered-
Yeah
... data that could be coming, but very quickly.
What I'm most excited. Things you should be excited about on the pre-partnered side, we're doing work on T-cell engagers and GPCRs. On the GPCR ion channel front, these are some of the hardest problems, and there are, you know, multiple targets that would be first-in-class blockbuster or have potential to be first-in-class blockbuster drugs for large indications. Everyone knows what the targets are. You haven't been able to do them. Internally, we're very excited about the progress that's being made towards that. We had signaled before that we thought the first of those would move into IND-enabling studies. I think we're on track for that. The same is true on the T-cell engager front, where we have moved programs forward.
We've shown that we have built a CD3 panel that can help control the level of T- cell activation, and we're getting, I think, very strong reception from the field, as this class of drug becomes increasingly important. It's attracting a lot of attention. Those two areas, I think there hasn't been much seen because they're difficult problems and they take time to surface, but they're parts of the business we're very excited about.
Perfect. With that, thank you so much, Carl, for joining us.
Thank you.
Thanks, everyone, for joining us.