Thanks, Enders.
Powerful mic.
Yeah, good mic right there. So I guess maybe just to start off, yeah, there's been a lot of changes with Box within the past few years, coming out the pandemic, a lot of changes on the product side and go-to-market. Maybe just kinda walk us through what's different with Box today versus three to four years ago?
Yeah. So, maybe I'll point to two major elements of our strategy evolution on the product and go-to-market side, and then maybe Dylan can talk about some of the bottom line, kind of operational efforts as well. But basically, you know, kind of the pandemic, you know, is an interesting point in time. But if you just sort of, you know, go back arbitrarily, you know, again, sort of three, four, or five years, Box was largely a platform that helped companies sort of store and share and collaborate on their most important files in a very secure way. But we were really squarely just solving that, you know, the kind of collaboration aspects of content management.
I think, you know, certainly accelerated by the pandemic, but as a part of our strategy, kind of the whole time, we have been pushing to really expand the breadth of the platform to really power the full set of use cases that companies have around their data and their unstructured data. So, that gets into workflow automation, really advanced data security and compliance, things like threat detection, data loss prevention, ransomware prevention. Then we got into the e-signature market because we felt like there was a lot of workflows happening around Box that we weren't necessarily powering, but we were just integrated into, that were these more kind of transactional workflows, where you wanna get a contract or a document signed. So we got into the e-signature market.
We've seen a growing range of use cases around how companies wanna be able to collaborate in distributed work environments. We got into this product area called Box Canvas, which is sort of virtual whiteboarding and remote collaboration. And now, obviously, with AI, this is really being even kind of, you know, further accelerated, where we can start to tap into all of this unstructured data that we have, and help customers and companies make sense of all the information inside of their enterprise in new ways, and I'm sure we'll get into that in a little bit. But basically, sort of job number one: expand the platform, get into multiple sort of key adjacencies to kinda core content and collaboration.
Each of which have faster-growing characteristics from a market standpoint than the kinda core content management market. So, we wanted to enter into faster-growing markets, consolidate more value, more data, more workflows that our customers were doing around their content, all in our platform. And then we complemented that with a go-to-market motion that enabled us to get the full value of the Box platform into the hands of our customers. So, and this was really our multi-product selling and kinda suite strategy that we've been very focused on. So instead of selling one kinda core product, i.e. Box, and then having some add-on products like data governance or data security, we wanted to sell the full suite of capabilities to our customers.
That really reinforced, you know, Box as a platform to our customers, and it certainly accelerated our growth rate in the past couple of years, where we could, you know, both, you know, improve our total average contract value, retention rates, net retention rates, and then, you know, even gross margin because of the kind of premium nature of these SKUs. That kind of, you know, really brings us to today, which is we're gonna keep doubling down on basically that kind of core strategy: add more value to customers, get into a key adjacent markets that deal with unstructured data, and then bundle those products together and deliver them to our customers in an integrated fashion.
So that's kind of been driving the top line, and obviously, we've made significant, you know, bottom-line improvements as well.
Yeah, and just to build on that, would say, so four years ago, Suites weren't in the market at all. Currently, Suites customers represents close to half, 48%, of our total revenue. And that's driven both all the things Aaron talked about on the product side, as well as that shift into Suites has driven, you know, higher pricing, about 20% on a per-user basis. Since we launched Suites, much stickier, you know, full churn, down from 5% annualized to 3% annualized over that time period.
And then on top of, you know, the customer economics and the benefits what Aaron talked about, we've also been very focused and over this time period to put in a bunch of really important initiatives to improve bottom-line performance. So going back, again, about four years ago, we were break break even. This year, we expect to deliver 25.5% op margins. And a couple of the key areas that we've been executing against are that public cloud migration strategy that Aaron touched on to run our operations more efficiently in the public cloud. We're at the very tail end of that now.
Also stood up our first engineering center of excellence at a lower-cost location in Poland a couple of years ago. That's been scaling really nicely, and then just you know, a lot of efforts around cost discipline across the board to drive that.
Okay, so, I think that's a great way to start off. A lot to dig into, here. I guess maybe just to, you know, start, just wanna talk about the last quarter a bit and what you're seeing out there from a macro perspective. You know, I think billings was a little bit softer, and then a guide down on revenue and billings for the rest of the year. I guess, what was different than what you expected from a seat growth, expansion, contract perspective on the deals that came in in the quarter, in the pipeline, that was different than maybe what you were originally expecting in 2Q?
Yeah, so I'd sort of characterize maybe the first half of this year, and then, you know, we can kind of point a little bit to the second half, and some of the trends that we're seeing. So the first half of the year, you know, with Box, we started to see the very slightest initial impact of macro headwinds in Q3 of last year, and then more pronounced in Q4. And so that has sort of caused us to sort of forecast , you know, a certain degree of growth and dynamics into this year. And what we saw in the first half were obviously some variants , you know, to that initial view.
You know, mostly kind of on the margin, but enough that, you know, we wanted to adjust our guidance. And basically, the dynamic being that, you know, in this kind of environment, you know, we've had healthy logo growth, we have very healthy deal volume, but one of the dynamics is with a sort of a seat-based model, where, you know, we can take on one department or five departments or the whole company. It's really the discretion of the customer of kind of how much they want to deploy Box and for what use cases.
You know, there are certain situations where we'll go into a deal maybe in our pipeline that looks like it's a $100,000 deal, just to kind of, you know, kind of, arbitrary number. And, you know, through that sales cycle, either due to the customer's budget or ultimately the use cases they take on, that deal might, you know, come in at $75,000 or something in that process. And so we've been trying to kinda calibrate our full process of how we kind of look at pipeline and how we see our forecast, you know, given some of the dynamics that we're seeing in the customer base.
But, you know, overall, I think every company on the planet is dealing with some degree of macro, you know, headwind, and, and they're, you know, making IT decisions based on, okay, what are the most mission-critical things that they can invest in? Fortunately, we remain mission-critical in a key number of areas, hence the 3% full churn rate. But, but certainly, again, you know, we might talk to a customer that was going to expand Box into marketing, sales, and R&D, and due to budget pressures, they just choose one of those departments or one of those use cases, and then they'll expand more over time.
So that's sort of the dynamic that I'd say, you know, maybe in the first half of this year, we have seen some degree of stabilization in terms of, you know, sort of not seeing the same kind of quarter-on-quarter degradation that I think would characterize maybe the first half of the year or, you know, starting in Q4 of last year. So that's certainly a positive sign, and then some of our internal KPIs, I think, point to, you know, some pockets where we're seeing, you know, more maybe slight inflection from the original kind of bottoming out.
You know, we'll point to, you know, some of the, you know, kind of billings dynamic that we expect to see in Q4, which is, you know, certainly stronger than Q2 and Q3 of this year.
Okay. So I guess a couple things there. You mentioned stabilization happened in 2Q and into August. I guess, what exactly is it that has stabilized there? And then secondarily, as we think about the outlook, I guess scrubbed is the pipeline at this point. Was there, like, another cut taken to better understand those dynamics? Or like, how do we think about conservatism that's being accounted for now?
Yeah, so we'd say that, you know, not like a fundamentally different approach to how we're thinking about, you know, kind of guidance philosophy. That said, yeah, absolutely, as we've learned more about this environment, seen more of these deals play out, have taken a more conservative view of, you know, what happens in some of those deals, especially if there are, you know, kind of a range of options from a deal sizing standpoint. So we've taken a more conservative kind of deal-by-deal view of things in light of the environment. And just overall, you know, as Aaron mentioned, we are seeing the biggest impact on just the rate of seat growth.
Roles are really the most pronounced pressure that amount of pressure through and into our expectations for the back half as well, just to be prudent, as we're not expecting for there to be a, you know, any sort of recovery anytime soon in the way we're approaching it.
Okay. So when you think about the 4Q, I think it does imply an acceleration in billings from 2Q and 3Q. I guess, what gives you the confidence in that happening? And like, what are you seeing either in the pipeline or, you know, just in trends in general that gives you that confidence?
Yeah, so actually on the billing side for Q4, would say, you know, from a, you know, what's baked into our expectations and guidance, you know, nothing too different from, you know, the Q3 numbers. Which the dynamic is more around, in Q3, there were just some other, you know, optical pressures around payment durations in particular, where we had very strong payment durations in Q3 of last year, including one large customer multi-year prepayment. So delivered even on a constant currency basis, 20% billings growth, which is kind of related to also just a particularly tough comparison.
Aaron mentioned we started to see, you know, kind of later in Q3, some signs of the macro impacting our business, more pronounced in Q4. So it's really a combination of just a cleaner comp, and is absolutely kind of validated a lot of the bottoms up, you know, kind of pipe- scrubbed pipeline, analysis that we do.
Okay, all right. T hat's helpful. I do want to take questions from the audience, so if there's anything out there, yeah, please raise your hand, and we'll make sure to get to you there. I do want to talk about Suites, 'cause that has been a really strong point. But you know, maybe it was a little bit softer in this past quarter. So I guess, how are you feeling about Suites at this point, and is there maybe a saturation point as we think about the penetration in the customer base at this point?
Yeah, I mean, we're extremely bullish on Suites. So, you know, I think we're, you know, certainly not satisfied with the quarter-on-quarter maybe Suites kind of TAV or revenue base growth. But again, kind of most of that is just correlated, you know, to the general seat growth dynamics that we've seen across the business. So, you know, we believe, you know, somewhat temporary as a phenomenon.
But in terms of upside that we see both in seat growth within our Suites customers, as the kind of maybe macro, let's say, levels off, and we see some more normalized comps and customer growth, and the just you know amount of unpenetrated part of our customer base where we know we can continue to sell Suites into. You know, it's hard to get an exact figure, but there's probably another kind of 20%-30% of the customer base that Suites is relevant for. You know kind of you know plus or minus even from there. So we see substantial upside in our Suite sales motion from the kind of current 48% of our revenue base that has Suites.
I think, you know, job number one right now is we're gonna make sure that we get our current kind of, you know, top-tier plan, Enterprise Plus, in the hands of all of our customers. At the same time, you know, we expect in you know, probably the next fiscal year, that we will have an additional, you know, kind of plan that has even more added value within the platform. That's gonna become a you know, kind of a key investment area for us.
So we have a lot of innovation in the pipeline that we know, you know, sort of, you know, represents even more value to our customers, and we, you know, wanna make sure that that value is, you know, appropriately priced into the product plans.
Okay. So I guess with the new higher price tier potentially coming in the next fiscal year, what would that look like, and how would it be different from, I guess, how you're thinking about things today?
Yeah. It's a lot of specifics for where we're at right now in announcing it. But I would just say, if you look at, you know, what we've, you know, commented on around our platform, we're going really deep in data security and compliance, going really deep in workflow automation, going very deep in AI, and kind of broad content management, data management use cases. So in each of those areas, in our product roadmap, we sort of see the next, let's say, concentric circle of more value we can offer our customers. And we think that there's an appropriate level of, you know, sort of substance there, where we think it makes sense to offer a new edition of the product tier.
But it won't be, like, a major surprise relative to what we do today. It's gonna be even more advanced ways to work with your content and get the most out of your business information. And, you know, certainly with how much AI has, I think, taken the world by storm, you'll imagine kinda components of our AI strategy will also make their way into, you know, higher tier editions as well.
Okay. So I do wanna ask about the AI angle.
Yeah.
But before I do that, I think one of the questions we get, you know, especially coming off of last earnings, is, you know, Suites has done really well, and I think there was a view that maybe it changes the monetization angle, you know, more modules, maybe it'd be a little bit more insulated from, like, a seat-based solution or that being an impact. I guess, how should we be thinking about the impact that Suites actually has from, like, a monetization angle? Because it does seems like it is more tied to seats than I think maybe people were expecting.
Yeah, I mean, I think, w ell, we are still a seat-based business model in the sense of you buy Box primarily for a certain number of employees or contractors or partners within your organization. So kind of the big multiplier effect is the number of seats that you have, and then really kinda price per seat is what's the Suite element drives. You know, we still have the dynamic where, you know, consistent seat growth and seat retention is a very important part of our business model. But we've seen a huge benefit from the upward kind of a motion from a price per seat standpoint that Suites is driving, and that has helped, you know, quite a bit.
But within our Suites customer base, if you look at the actual economics that we see there, you know, higher net retention rate than the average, higher price per seat than the average, higher gross margin than the average. So, you know, all things get better the more customers have Suites, and we're gonna continue to push on that.
Yeah, and then to clarify, there's the customers moving into Suites, net penetration is not necessarily directly tied to seat growth, but tends to be pretty correlated for a couple of reasons. The first of which is, in a lot of cases, when customers are just looking to go bigger with Box, they hit that point where they recognize the value, and they have the budget for it. That will often mean, "Okay, now I can, you know, bring in all these capabilities and, you know, open up Box to a new set of users," as one example. So that's one way that it's not even because the seat growth, but a lot of the same budget dynamics.
You know, if you're just in a really challenging environment, you might, for the same reason, say, "Okay, I'm only gonna expand by 1,000 instead of 3,000 seats. I, you know, may hold off a little bit to move into Enterprise Plus," as one example. And the second is just once you roll out Suites, that brings in so many additional capabilities that in a lot of cases, those customers say, "Okay, now these customers where before maybe Box, you know, didn't see as mission-critical or, you know, not as valuable to what they're doing day to day, now that we have XYZ capabilities, it just makes a ton of sense." And that's why it often leads to seat expansion or is correlated with it, even though they are two, you know, kind of different levers.
Okay, all right. T hat's helpful. Maybe we can switch gears a little bit, unless there's any questions in the audience, and we'll pivot towards the AI discourse. You know, I know you launched Box AI, the collaboration out there with Microsoft Copilot for enterprise content. I guess, what has been the early customer feedback that you've heard so far with those solutions out in beta? And I guess, are there any verticals, end markets, use cases that are resonating a bit more at this point than others?
Yeah, and just to be super precise, for anybody new to the story, so there's Box AI, which is our sort of our AI offering, where we embed things like OpenAI and others into our platform. And then we also announced, totally separately, an integration with Copilot, where we can feed data into Copilot for Microsoft's AI experience. So I'll focus primarily on the Box AI front. So you know, we believe we're in an incredible position to leverage large language models, you know, probably more so than the vast majority of enterprise software.
The reason for that is that what large language models are extremely good at, relative to other forms of technology, is they're very, very good at understanding large amounts of unstructured data in the form of words. And where do you find lots of words? You find them in documents, you know, more so than literally any other data type just on the planet. If you take a contract, a movie script, a memo, an earnings transcript, an invoice, all of this, you know, data is represented by, again, you know, sort of strings of words that large language models are better than any other, again, technology that we've seen at understanding those words, and then letting you kind of interact with them in a natural language way.
So, for us, this is a profound shift in how we can work with our unstructured information. We just released a report with IDC, where they found that, among, you know, kind of general enterprises, about 90% of our data is actually unstructured.
Wow!
So, you know, we spend a lot of time thinking about, Enterprise spend a lot of time thinking about their structured data. You know, what's in the Oracle database? What's in the SAP ERP system? And, you know, rightly so, incredibly important data inside of the systems, and, and, you know, kind of relational, you know, database systems. But actually, 90% of their data is in the form of documents, and contracts, and, you know, marketing assets. And all of this information is sort of floating around and being shared by , you know, individuals and employees, and created by employees, and shared with partners, and customers, and colleagues, and we've never been able to programmatically, at scale, understand that data.
It's always requiring a human to kind of open up the file, look at it, understand it, read it, share it, to be able to do anything with it. And now, with AI, we can now do that at a completely different scale, with automation. And so, we announced Box AI, which basically connects the unstructured data in Box with leading large language models, starting with OpenAI. We have announced a partnership with Google, where we'll be also adding some of Google's AI capabilities into Box as well. And then over time, we've built a platform-neutral approach. So we'd like to be able to, you know, offer up any AI model in the future.
There's no particular preference, that we have other than kind of just quality and performance, that we want to offer our customers. And then, we're going to make it really easy to interact with those models, in our platform. So imagine, if you're a lawyer and you're looking at a legal, you know, document or contract, and you want to be able to quickly understand what are the clauses of this contract that might be the highest risk ones, and I need to kind of quickly look at a 50-page contract and understand that.
Or you're looking at an invoice, and you have, you know, tens of thousands of invoices coming in to Box, you know, every month, and you want to be able to automatically extract key data fields from those invoices and then have that invoice be routed to different people as relevant. Or, imagine if, you know, you have a large sales team, and there's lots of product catalogs, and somebody needs to ask a question like: "What's the price of this product that we're selling in this particular region?" You know, how do you go find exactly the right document that has that answer? What if you could just ask that question across a large set of data and instantly get an answer back?
So those are the use cases that Box AI is going to be able to enable. And again, I think if you, you know, take any one of those use cases and compare them to probably a lot of the you know, AI conversations that we're hearing in enterprise software, these are some of the most potent, you know, ways that we can bring AI into an enterprise context. So we're really excited, and our customers are incredibly excited as well. We've started rolling out Box AI in a design partner program to a select set of kind of beta customers. And then, throughout this fall, we'll be expanding that even further, and we'll have some announcements at BoxWorks, our conference in October, that we'll share more about where that's going.
Okay. All right, that's great to hear there. I know we're going to hear a lot more at BoxWorks, but just like conceptually how do you think about, you know, how the product set changes from AI? How do the use cases that that you, you think about that could incorporate AI, you know, longer term, just, y eah, how do you envision the product evolving to, to meet this?
Yeah. So, I think to some extent, you know, we're seeing for lack of a better term, like, kind of random acts of AI in software right now.
Which is sort of if you just, like, throw AI at a thing, then that thing, you know, will get better. And we have a pretty different approach, which is we're so architecture-oriented in terms of our platform, that we think about sort of where do you slot kind of high-leverage capabilities that will have a very wide and positive blast radius, but one where there's a very kind of clear reason for that sort of slot to exist? And so for us, it's both incredibly simple but then, you know, almost infinitely powerful, which is you take one individual piece of data, i.e., a document, contract, marketing asset, et cetera. When you run AI against that, what are the things that that opens up?
We've identified, you know, five or 10 different things. You know, you can summarize information, you can ask questions of information, you can extract data, you can classify documents, you can automate workflows, but it's all on one simple organizing principle, which is take the content, run AI on it, and then create capabilities that leverage that architecture that make Box just generally much smarter. And so instead of saying, like, there'll be 32 ways inside of our product where you'll randomly see, you know, an AI thing pop up, and then users get confused, or you kind of get fatigued about, you know, where to use this stuff, this is, you know, very clearly aimed at how do we help you automate and bring intelligence to just your unstructured data and make it more useful?
So, it's actually gonna show up in key areas of our product. But again, really embedded in how you work with your unstructured data in new ways. So again, security, workflow, asking questions of data, and then, you know, we expect to have a pricing model that tries to balance both, you know, the premium nature of these capabilities that we're offering our customers, so how do we get more value as a result of that? But at the same time, I think our expectation is if we fast-forward 10 years from now, just to choose a really, you know, distant point in the future, we probably shouldn't be talking about AI discretely because software will be intelligent.
So we also want to anticipate a future state where, you know, you wouldn't be at a conference saying, "Okay, I had to pay extra for a mobile app." So we want to kind of expect, you know, sort of figure out where these cost curves are likely going to head, and how do you have AI, you know, also be an embedded component of the platform and offer the most amount of value to our customers? So that's right now the balancing act, which is offer the greatest amount of value to our customers, but also, you know, understand that there's premium use cases customers have, that might even cost us more.
We want to make sure that we're, you know, sort of capturing value as a result of those premium use cases. So you're gonna see kind of a balanced approach to how this gets priced and packaged and in the suite.
Okay. So I guess if I'm kind of understanding it from a monetization angle, you're really focused on this driving price, building new plans and packaging to help kind of support that, and probably less focused on new SKUs. At least that's kind of how you're viewing it today.
I would say, because we don't want to preannounce anything, inadvertently, I would just say, you know, we think about it as how do you have the widest amount of impact, but also capture the greatest amount of value?
Okay.
Then it's worth noting, you know, ensuring that we don't, you know, have any kind of gross margin headwinds as a result as well. So that's kind of important to throw in there. You know, 'cause the underlying GPUs can be expensive, so we do wanna make sure we capture value as customers are using this.
Okay. All right, t hat makes sense there. I guess similarly, how do you view, you know, at this point, where it makes sense to incorporate AI, where it makes sense to build it out yourself, and where it makes sense to partner? 'Cause I guess you mentioned, like, there's a lot of rapid change going on and new vendors popping up and new solutions. So how do you think about that part in the architecture component?
Yeah, I mean, I think two things come to mind. So, I think it was Bill Joy from Sun that had a famous quote, which was, "There will always be more engineers outside of Sun than inside Sun." And that was sort of an argument for open source and Java back in the day, which is this idea that, like, you never wanna be competing in a market where there's way more talent that is heading in a direction that you could otherwise be leveraging.
And so AI is kind of like the quintessential example of that. I generally, you know, other than for very narrow use cases, I'm pretty skeptical of any strategy that requires kind of a proprietary AI or LM approach right now, just because there's just simply too much R&D happening, that you wanna be able to capture all of that, that value and accrue it to your platform. So what's incredible about this moment right now is you have basically every large, every large technology company on the planet and almost every, you know, significant startup, you know, that could exist, training and building some of the world's most advanced AI models. And so for us, that's incredible becaus e...
Just remarkably, somehow, there became kind of almost uniform API approaches to these AI models as well. So it's not like all of these companies are all competing on sort of the having a scarce resource that they kind of keep themselves. They're actually all competing on, on something that's insanely expensive to go and, and build, but then opening up for as many possible use cases as possible. So that's great for us, because then we can basically be in a world where, you know, today, let's say GPT-4 is, is, you know, the kind of the bleeding edge of of AI. Google obviously is sitting around right now trying to make something better than GPT-4, and Facebook is probably sitting around trying to make something better than GPT-4. And so for us, all of that innovation can accrue to our customers.
And, you know, we have to make sure that that's easy to use and easy to understand for our customers in terms of how you configure and manage these AI models. But it's an insane boon for us, and why we very explicitly will not be in the business of, you know, kind of like large training runs, you know, heavy CapEx. This is very much about offering this innovation in as variable of a cost basis to our customers as humanly possible. So that's where we're gonna sit in the market and all of our value is gonna be the layer of how do you integrate these AI models into your data? And that's actually a problem that, in many cases, actually even more complicated.
It's just we benefit from 15+ years of a platform. But that's where data security, compliance, permissions, having an, you know, in, you know, vector database and index around the content really, really matter, and that's where we offer a tremendous amount of value and expertise.
Okay, with everything going on right now, and I'm gonna bring Dylan in on this as well, how do you balance, you know, investing more, trying to put more into, you know, the product, putting more into the go-to-market versus, you know, trying to drive some of that margin? 'Cause, I mean, it does seem like there's a big sea change architecture shift that's happening now with AI. So yeah, how do you view the balance of growth versus profitability at this point?
Yeah, I would say that, you know, the philosophy, you know, hasn't changed. I think one of the key things, if you go back to some of the, you know, multi-year efforts, where we've been on the journey to fundamentally, you know, kind of change our cost structure, you know, a lot of that are areas that we can drive leverage, without having to, you know, really, sacrifice growth. So it's not a, "Hey, do we invest in an engineer or, you know, an account executive to, you know, go out, in sales and talk to customers, you know, or bank it?" But, "Hey, how can we deliver this service to our customers that much more efficiently? They're getting, you know, a better experience, same experience with higher gross margins.
How do we deliver this type of innovation and accelerate it, as we've been doing?" We've had some of the fastest rates of engineering hiring the past couple of years than we had for many, many years, while still driving leverage in that line item of the P&L by really going big in Poland. And then, you know, certainly there are other trade-offs that we do make in there. You know, we are very committed to the long-term target model that we laid out, showing significant improvement in our overall profile. You know, kind of both the revenue growth side of things as well as on the bottom line.
But there, you know, we more make the decisions looking very granularly at what is the return we're getting, especially on the go-to-market side, right? So every segment, every geography, how are new rep performing, how does that compare with global averages? Really tuning our bets, making trade-offs in that sort of way. And we do the same thing on the engineering side, based on everything we're seeing as that evolves. So there's no, like, perfect, you know, there's a lot of nuance in it.
Yeah.
I would say a lot of what we've been doing from a cost structure side has been in order to enable us to both invest in these critical growth areas while still driving.
Yeah, I think the only thing I'd build on that is, and sometimes this is hard, you know, kind of to assess, unless you kind of go under the hood of a company, but like, let's take two types of archetypes of companies. So you have Apple and Amazon. You know, and Amazon is like, let's innovate everywhere, and then hopefully, an AWS emerges. And that, that's a great approach, and AWS did emerge, and that's insane and amazing. But it does mean that you're exploring and wandering in lots and lots of spaces. You know, thousands of engineers working on a project that may or may not work.
On the other side, let's take an Apple, which is like, these are like, we are gonna go super deep. We have very focused efforts. We're not gonna do a ton of speculative R&D. This is not a bottoms-up research, you know, kind of everybody tests out lots of different things and have thousands of people running in different directions. This is very much a, you know, kind of tops down in the sense of, of it's a leadership team coming together saying, "What are the big bets?" And I think we look much more like an Apple in the sense of we make critical bets. We don't do a lot of wandering in lots of exploratory areas.
Part of that is just I think we care so much about the user experience, and we care so much about the architecture patterns of the platform. But it's also because, you know, we have a tight-knit team, where we can kind of right now, at this stage of a company, make those kind of key selective bets. So, you know, I'm sort of, you know, somewhat joking about this, but you know, two years ago, everybody was excited about blockchain. Well, we said, "No, we're not doing that 'cause it makes no sense.
But lots of companies were like, "Ah, 36 engineers on the Blockchain project," and, like, we just don't do that. We decide to be very decisive about where is technology going and where can we have the greatest amount of impact, and we put all of our resources on that, so we don't have the same kind of extraneous work that's happening in the company that I think, you know, some organizations do, and that's just because of the philosophy of our approach to innovation.
Okay. So I think we're running up on time here. I just want to do a quick scan and see if there's any last questions for before we let everyone go here. No? All right. Awesome. Well, appreciate both of you being here.
Appreciate it.
Yeah, appreciate everyone being in the room as well. So-
Great.
All right.
Thank you.
Thanks again, everybody.