FULL TRANSCRIPT
Slava Rubin (00:00)
All right. Thanks everybody for joining for our next in our pre IPO briefing series. My name is Slava. I'm with Vincent, your platform to getting access information for all things, alternative investments. We also have with us Jan Erik from Sacra. Thank you Jan Erik for joining.
Jan-Erik Asplund (00:17)
Thanks for your time.
Slava Rubin (00:18)
Absolutely. So we've covered a lot of great companies. We've got lots of good feedback. And today we want to cover Glean. Many of you have heard of OpenAI. Many of you have heard of Anthropic. Obviously AI is super trendy. Not everybody has heard of Glean, but Glean is a darling and it has a different perspective. It's really focused more on the workplace AI. So we're really excited for today's conversation. And this is brought to you by our partner, Upmarket.
And as always, we'll go to the next slide, which is our compliance department. Nothing in this presentation should be construed as an offer to sell securities or a solicitation of an offer to buy securities. All investments involve risk and the possibility of loss, including loss of principal and neither past performance nor forward looking information is a guarantee of future results. With that said, we're going to have Jan-Erik give an overview of the company and then I'll dive in with questions.
As all of you have any thoughts that come to mind or any questions, feel free to follow up in the Q &A area. All right, Jan-Erik, take it away.
Jan-Erik Asplund (01:18)
Awesome. Yeah, Glean, very interesting company, enterprise search company with a lot of AI around it. And the best way to sort of think about what it is, is Google for your workspace. And it was actually founded by a former senior engineer at Google, Arvin Jain. it's a space that has been tried many times.
by different companies to crack this like enterprise search over all your files, all your apps, all your docs and Google themselves tried it like 22 years ago and it's been kind of a graveyard startups. so Glean is sort of the latest entrant and so far, one of the fastest growing most successful attempts at it. So yeah, but we can go into more what they do.
sweet. Yeah, they really started early on, well before LLMs were a thing, about three years before ChatGPT with basically a search engine powered by early forms of AI. But the product really took off post-ChatGPT, post-LLMs becoming something that you could build into your product. Today, the product looks a lot like ChatGPT in some respects. There's an AI chatbot.
but there's also a bunch of other components to it. There's AI built apps inside the platform that do different things. They have their own APIs for other companies to build integrations with and various other tools like that. And then there's a whole sort of raft of enterprise specific aspects to the product. One of the big ones is governance. So if you're a multinational corporation, you have a ton of
sort of permissions and viewability restrictions on different types of data, different files, different apps. And so a huge part of what's going on in Glean on the backend is kind of this permissioning system, making sure that random employee, you know, number three can only see exactly the stuff that they're supposed to be seeing. And yeah, there's more, you know, sort of more to the product, but we can go into it as we get there in the conversation.
And yeah, one of the sort of, as you said, sort of a darling of the AI space, one of the standout kind of enterprise companies that have been raising aggressively and getting valued aggressively. So right now, last primary around 4.6 billion, secondary price on the market is roughly around five. And yeah, laundry list of great investors, Sequoia, Altimeter, Kleiner Perkins. yeah, they've been...
successful on the fundraising front, for sure.
Slava Rubin (03:42)
All right, already worth about five billion on the secondary market. So they're obviously doing something right. You mentioned they already have about 110 million of ARR. Let me just start with the basics. I know you gave a little bit of an overview. What does Google for the workplace mean? Like, can you just give me a use case or two, like really simplified? You know, how are their clients, what are the top one or two use cases of how people are actually incorporating Glean into their productivity?
Jan-Erik Asplund (04:10)
Yeah, so two of the big ones that have emerged as like a sort of the landing use cases, how Glean gets into an organization. One is sort of engineers who need to find troubleshoot issues with code. so they'll get an error code from a tool like Sentry, and then they can plug it into Glean and they can quickly search through their entire.
their entire code-based documentation, but also external libraries, and they can quickly troubleshoot issues by just having basically something that can search across all of their internal info about their code base and find where this issue might be. So that was one big one early on. The other big one early on was onboarding. So when you are new employee and you join a company, you don't know anything, and often finding that information, you know.
you don't even know what tool to search in or who to ask. And so what Glean does, one cool thing is, yes, it has search through apps. So if you need to find the time off policy, it can find you that in your HR platform. But also there is a concept of, you know, kind of ownership and employee management in Glean. So for example, if you need to find the person directly responsible for, you know,
your time off, who you should be asking about your time off. There's a concept of that inside the Glean platform that knows your organization, the organization chart and who's responsible for what so it can find you that person to talk to. So those are kind of the two, HR and engineering were two of the big early ones.
Slava Rubin (05:41)
Nice. And in like a holy grail, Glean is crushing it five, 10 years from now. What does Glean as like the productivity tool for the corporation look like? What do you envision? What do they envision?
Jan-Erik Asplund (05:53)
Yeah, I think what they envision is if you're sort of using Glean to the total extent that they would imagine you do, this becomes your sort of layer into all of your work, the sort of interface layer into everything that you do. whereas today, like you might be going in between Slack, email, Confluence, Salesforce on the enterprise side, you would not be
directly interfacing with those tools nearly as much. You would be asking Glean, know, remind me of our Q4 target, you know, who's the point person on the Vincent account, you know, and you would potentially, you know, then you would say, okay, write me a sales email, you know, to send to Vincent so I can get to be the sponsor on their next podcast. And it would do that learning from all the previous emails that you've sent that it has access to.
And so you can imagine a world where, like Google, for example, started as a interface layer into the web where you would search, you know, cat video, find one. And today it's more of like a suite of products. You could spend your whole day in G Suite doing work. I think Glean is sort of imagining a similar outcome where you spend a lot of your day in Glean versus in these like destination apps that they integrate with.
Slava Rubin (07:05)
I mean, that's a huge vision for sure. So how do they make the money? Where's that 110 million error coming from and what are the revenue opportunities?
Jan-Erik Asplund (07:12)
Yeah, so right now, it's pretty standard B2B SaaS fare. They have a per seat pricing model subscription that comes out to roughly $40 a user. We think probably for some of the larger enterprise deployments, it's slightly lower than that. So yeah, I think a big part of the game is expanding seats. And they tend to land in these engineering HR teams.
The goal is to grow from there. One thing we've observed is the sales cycles are pretty long. So they're really trying to move up market into these much larger companies. the sales cycles up there are longer. So a lot of the early revenue has been these sort of mid-range companies, 90-day sales cycles. But what they're going towards is kind of the longer, up to six months type sales cycle.
unit economics, pretty standard B2B SaaS, but probably with a larger services component. Because again, big companies, big enterprises require more of a services component generally for implementation. But also Glean is a slightly more, it's not a plug and play operation just yet. So there's a fair amount of configuration that goes on with customers to map out their universe of docs and files and apps.
that requires some sort of onboarding at this point.
Slava Rubin (08:39)
So does that services customization, does that eat into the margin and how pretty of a darling this could be?
Jan-Erik Asplund (08:48)
Yeah, to an extent. I I think if you look at companies like
Slava Rubin (08:51)
Salesforce. I consider Slack pretty plug and play, but I consider Salesforce much more customization required. Is that fair?
Jan-Erik Asplund (09:00)
Definitely. I would say, the core SaaS business could be like the 80 % margins range, whereas the services stuff is probably looking at these comps like probably in a 30 % range. I mean, I don't think it's a huge impact. Like this isn't like a Palantir where it's like a consulting business masquerading as a software business. This is a software business with a consulting component.
I mean, I would sort of clock the gross margin around 60, 70 % with upside as they actually automate a lot of this stuff.
Slava Rubin (09:36)
Do you have any sense for that 110 million in 2024, how much of that was straight software revenue versus how much of it was consulting revenue?
Jan-Erik Asplund (09:47)
Yeah, I would guess probably like 80, 20, roughly in that region. Yeah.
Slava Rubin (09:52)
80 software-based.
Great. obviously people know OpenAI, Anthropic, and others, Gemini, Cloud, et cetera. Who do we consider the competitors for Glean? Who are the heads-up competitors? I imagine the OpenAI and all of them could become the competitors, but who do we consider today the competitors head-to-head for Glean?
Jan-Erik Asplund (10:15)
Yeah, definitely. So there's kind of a, like I mentioned, this was a graveyard of startup ideas. There's been companies trying to do enterprise search for decades and it's been a, you know, it's been basically a complete failure until we had LLMs that could synthesize and make sense of this. So there's a bunch of AI native startups that are doing roughly what Glean is doing. You have Hebia, which was sort of initially positioned more one-to-one similar with Glean, but now is more focused on finance. You have
Sana AI, which is also in a sort of similar area as Glean. And then you have Writer, which Writer is unique because they sort of have a proprietary LLM model. They're a little bit less focused on like having all the workflow products that Glean has. So it's less kind of you know, it's less focused on helping people do everything at your organization and search over everything. But these are kind of the main figures in the space.
And then you also do have sort of these like Zapier's air tables notions that do have similar functionality to some degree now that they're incorporating more AI technology. just like automating the movement of data across your organization is something that Glean is not alone in. There's plenty of other companies doing it, but no one quite at the enterprise scale and sort of form factor of Glean.
Slava Rubin (11:34)
Got it, so would you consider Zapier, I'm a fan of that company, do you consider Glena Replacement or are collaborating?
Jan-Erik Asplund (11:40)
I definitely think you could, would imagine that companies would have both, because they do serve different, Zapier is more for connecting kind of these long tail applications to each other or getting stuff into a Google sheet or Google doc, more of like the edge cases. But they are kind of like with the sort of LLM craze, they're moving more into giving you full kind of automation across different aspects of your business.
Realistically, no enterprise right now is thinking of it as a Glean versus Zapier, Bake Off or versus Airtable, but they're sort of developing similar capabilities.
Slava Rubin (12:21)
Gotcha. The open AIs and such have raised billions and billions of dollars because of the cost of having this proprietary foundation, LLMs. know, Glean has raised under a billion. Are they creating native LLMs on their own that are homegrown or are they leveraging what's already out there and nuance, nuancing on top?
Jan-Erik Asplund (12:41)
Yeah, from what I understand, there's nothing proprietary about their models and they've been heavily reliant and were an early partner with OpenAI. So a lot of like GPT-4 was a big moment because that sort of made a lot of this stuff possible. Whereas GPT-3 and 3.5 were not quite sophisticated enough as models. yeah, that's definitely part of the sort of less capital intensive approach is that they are using the best models for the job.
Slava Rubin (13:07)
Since they're leveraging OpenAI, is there any reason to believe that three years from now OpenAI just doesn't come out with their own version of Glean?
Jan-Erik Asplund (13:15)
Yeah, I'm sure that is in the most sort of bullish roadmaps that Sam Altman has created. And they actually, you might remember when OpenAI warned their investors not to invest in certain other AI companies. One of them was Anthropic, one of them was Perplexity, one of them was Glean. And so there's definitely reason to think that they are interested in the space and they have integrated with
Google Drive, OneDrive, you can create little workspaces in ChatGPT and invite your coworkers into them to work together. There's glimmers of sort of indications that they want to play in this enterprise segment, but would it be as easy as, you know, just sort of wanting to do it? I mean, with Perplexity, they sort of just integrated search into ChatGPT. And if you ask me, it's roughly as good as Perplexity. But Glean,
You know, they were around for, they've been around for five years. They were around for three years before they launched anything with LLMs. And a lot of that time was spent on the sort of plumbing of the, of the, of the, know, building an enterprise search tool. And that means building.
It's a very like technologically complicated task to build these like native, native feeling integrations with Salesforce, with Confluence, with every B2B SaaS app that enterprises use. And it's not something you can just pick up off the shelf. And so that I think is where they sort of position a lot of their advantage. And their lead is that they spent three years building this kind of plumbing that is, it's hard to replicate.
Slava Rubin (14:48)
Just a reminder for anybody who has any questions, feel free to go to the Q &A and I'll look to get to it. You mentioned these more native competitors. What about the more basic larger competitors? We talked about OpenAI. What about the Googles, the Salesforce, the Microsoft, the Metas? How should we think about those companies competing or collaborating with Glean as we think about their prospects three to five years out?
Jan-Erik Asplund (15:13)
Yeah, so Arvind has called out Microsoft Co-pilot as the most direct competitor that they have today, which makes sense because so many enterprises are all in on Microsoft. Microsoft does have access to, if you are all in on Microsoft, they have access to all your email, all your files, your docs. so having the latest and greatest GPT models,
integrated into Co-Pilot, which has access to all your files, makes Microsoft probably the most meaningful competitor in that sense. You do have Google. Geminize models are really good, especially at taking in giant amounts of data and context and making sense of it. Google, you have Cloud Search. They're integrating AI into G Suite now.
You know, I think the odds there that they're going to make a sort of sustained play are perhaps low. I mean, just based on their sort of history with releasing products. Salesforce has a little bit of this too, but you know, it's more sales marketing centric with their AI agents play. So of the sort of giants, think Microsoft is definitely the biggest competitor. I think the main thing with Glean
is Glean is agnostic to your stack. So it doesn't matter if you use Microsoft, maybe use Google for other things. You might have a hundred other SaaS apps. So where Microsoft might have an advantage for maybe smaller companies that are more kind of full stack Microsoft, I think where Glean is sort of different is the more broad full cloud coverage.
Slava Rubin (16:49)
Let's talk about the future. So what are the growth opportunities? I mean, I know you gave the pie in the sky opportunity as to kind of being a new layer as to how you access enterprise, but you what does this look like three to five years from now? What can this become?
Jan-Erik Asplund (17:04)
Yeah. So a lot of the, a lot of the expansion sort of vision for them right now is about bringing on these companies with, with really big seats and, selling into really large global enterprises. And a big part of that too is expanding sort of right now there's a lot of, you know, larger startups, tech companies are sort of their core customers, which is not uncommon for other startups. So the big sort of verticals they've identified
are basically financial services, retail and manufacturing. So they already have a pilot going with a Citigroup. So that's one big one is sort of expanding into these more traditional larger enterprises. then, yeah, geographic expansion is a big one. So they're looking hard at Asia Pacific as a big expansion region. then, yeah, think, know, advancing the capabilities of the platform are key. So some of the stuff I talked about before,
you know, they're really riding on the advantages of, you know, building on other people's models that continue to get better. you know, chat GPT has gotten a lot better since Glean launched. there's reasoning models now that are better at doing sort of a autonomous tasks. And so, you know, if Glean can go from, you know, I have to go and identify a sales target. I have to go and queue up an email. I have to edit the email, then send it, you know, they could potentially take out some steps there where they have an agent.
you know, looking at who your sales targets are every morning and writing a sequence of, you know, 30, 40, 50 emails that maybe you don't even have to, you know, assess and then you can send them. So yeah, it's bigger, bigger companies, larger deployments, and then, you know, more advanced sort of AI stuff.
Slava Rubin (18:42)
Is this mostly sales led growth or is there any product led growth? I imagine it's mostly sales given the size of the contracts.
Jan-Erik Asplund (18:49)
Yeah, it's not really, you can't really go and just try the product. yeah, there's not really a freemium offering. It's pretty sales led. I mean, think there's a lot of organic inbound interests because of the sort of, know, mind share that they've quickly gathered in the space. yeah, pretty sales
Slava Rubin (19:05)
Is there any sense for how big the sales team is and how much money they're spending on that?
Jan-Erik Asplund (19:08)
That's a question. I don't think I have the exact number, but yeah, I think it's roughly like maybe 20, 30 % sales.
Slava Rubin (19:16)
Got it, got it. We have a question also. Is there any additional you want to cover as to what the future could look like in regards to agents with Glean? Or do you feel like you covered that already?
Jan-Erik Asplund (19:25)
Yeah. I mean, I think it's a space a lot of companies are going after in a B2B context, especially. mean, Anthropic is one of the biggest ones actually here, mostly with code. But yeah, think it's one of the ones that is like a sort of the, we talk about with OpenAI, like what the ultra-bull case was. If they automate, you know, if they...
sort of automate a general intelligence, you that can do everything that a human does, but better. And I think Glean is sort of, you know, betting on a similar kind of extreme upside case, you know, where if you have an agent plugged into your entire business, all of your docs and files, all your conversations and knows your business, you know, that could be a a huge unlock if they could sort of act autonomously to help you with all aspects of your company. So definitely it's a big part of the vision.
Slava Rubin (20:16)
Let's think forward a few years. OpenAI came out with their own product in the enterprise space. Copilot is doing well with Microsoft. Who knows what the other players are doing between Google, Salesforce, et cetera. Is there space for Glean to be a winner alongside those others, or does it need to really dominate and make it seem as if the OpenAI product is not worth using?
Jan-Erik Asplund (20:39)
Yeah, I think.
I think there's a case, the most plausible vision I have, if I think about maybe what a more bumpy road looks like, we do get an open AI enterprise product. And actually they've already demoed a sales product at a recent conference in Japan that would give you sort of what I was describing, a list of salespeople and then it could help you generate outreach to them. And it would also...
tell you who was sort of maybe most primed to buy based on various sort of product led signals. So we know they're going to do this. I think the key thing for Glean is there will probably be companies that are not comfortable funneling all their enterprise, corporate proprietary information into OpenAI for various reasons. mean, one is just that OpenAI makes money from training models on better and better data. Whereas Glean makes money from a SaaS subscription and they have
been sort of proven and positioned for the enterprise. So I think that will be sort of Glean's main advantage on a positioning front is just that they are, know, an enterprise search company. They are not looking to, you know, use your data to train their models on, to make them better for everyone, I think. And then, yeah, with Microsoft, it's just about being sort of, you know, tech forward to an extent too, like,
a lot of companies would probably use that Microsoft Co-Pilot agent. But a lot of companies probably, think Glean is betting that a lot of companies will not want to.
Slava Rubin (22:01)
Got it. Give me your baseball bear case on what this could look like in regards to Gleam's future and the potential projections.
Jan-Erik Asplund (22:08)
Yeah, definitely. So I think sort of the base case, the way we sort of have come down on it is that they've developed a a lead built on pretty deep actual technical work on integrations and getting LLMs to work across all these different docs and files and apps. So our sort of projection there is basically by 2029, like 2.7 billion.
in ARR and that just in tempest is, know, models getting better, capabilities getting better. You know, this is no longer a search engine primarily for your data. It's a place where people do do work. The bold case is that, you know, agents really take off and we're seeing, I mean, we're seeing very rapid, you know, impressive achievements by OpenAI and Claude.
And as these kind of funnel into Glean and get productized into Glean, which will take time, there's a case where this becomes sort of an indispensable tool for every part of the organization, not just engineering, HR, but it becomes a core tool for sales and elsewhere, which we sort of peg it at 4.3 billion. I think the bear case, you kind of laid out some of it.
would be that open AI eats up the sort of tech forward, you know, early adopters of this kind of stuff. You have Microsoft, you know, doing its own co-pilot, making it better. And then the sort of third vector there is like SaaS tools themselves developing better AI. I mean, what B2B SaaS company is not working on AI LLMs at this moment in some way. And so I think a lot of the original glean value proposition was based in this time.
when none of them had AI. So Glean was sort of the AI synthesis layer for all this data and all these files. But there's a world where every B2B SaaS app that people use in the workplace has a robust sort AI interface and connectors to other apps where maybe the value proposition of Glean is less significant.
Slava Rubin (24:07)
It's interesting, even in your bear case, assuming 1.4 billion ARR with an ADX multiple, you still have an 11 and a half billion enterprise value in 2029, which is not something to sneeze at. We'll get to price in a second. I just wanted to share that here. So with the bear case, 11 and a half billion, base case, 40 billion, and the bullish case, 106 billion. So when you come up with those amounts, prices, valuations,
How do you think about the right private and public comps to compare to when you think about how to price this type of company?
Jan-Erik Asplund (24:45)
Yeah, it's tricky because enterprise search is not something that has a huge public presence. So we looked at a lot of these companies that I mentioned before, the enterprise AI search, pumps like Sana, Rider, Hebia. There's another company called Moveworks that just hit a hundred million ARR. And they are all roughly in the same range. mean, Hebia, Rider, Glean are all around 35, 40X. So, know,
In the market, none of these prices are outlandish. mean, you have other companies in AI raising at 100, 150x, like Perplexity recently did.
Slava Rubin (25:22)
revenue, right?
Jan-Erik Asplund (25:23)
The X axis is revenue, yes. And the number in the bubble is the multiple. So they're growing very fast at high revenue scale at 100. So yeah, I don't see these prices as out of bounds, especially with those sort of 100X plus players that we're seeing.
Slava Rubin (25:40)
Gotcha. So today, the last price they raised twice in 2024, most recently at 4.6 billion. Before that, I think it was 2022, they were closer to a billion, and then they went up to two and a half and then four and a half. So all of a sudden, they're growing rapidly in terms of valuation. They're trading in the secondary market around five billion, which is another 10 % premium above
their last valuation, what do you think of the current entry price?
Jan-Erik Asplund (26:10)
Yeah, so the current price, roughly $37 a share of $5 billion. Yeah, think, well, like I said, it's not crazy compared to some of the other AI multiples that we've seen in some of the other AI companies.
But that said, like those companies have, think, shown or have a much clearer sort of less bumpy theoretical vision for getting to larger scale. Whereas I think Glean has to sort of navigate this movement into non-traditional kind of AI adopter industries or places like finance where there's a lot of sort of red tape.
and a little more of that risk around, if our data is leaked or used to train models or if people get access to it who shouldn't have access to it internally. So those will be sort of bigger lifts on the sales front, which I think might account in part for the lower multiple. But again, I think it's a market that is really hot. have a lot of people excited. There's a lot of demand for the stock. So I don't necessarily see
that $37 price per share, $5 billion valuation as being out of bounds.
Slava Rubin (27:13)
Let's say things became really competitive. What's the one thing that you think Glean focuses on as its key differentiator? What product or feature or capability or what do you think about there?
Jan-Erik Asplund (27:26)
Yeah, I think probably the integrations because I think covering, you know, now they have a roughly 300, a little over 300 integrations with external apps. I think that has to be sort of the key focus because it's been so far the one thing that has been the least replicable about their strategy because obviously anyone can throw an AI interface onto a data set or onto a collection of documents.
So there might be innovations at the interface layer that they're not expecting, but I think the integrations part will really be key. And then obviously the sort of permissioning and all the stuff that goes into making it enterprise ready, because it is not hard to imagine a company sort of doing glean for startups, SMBs, smaller mid-market companies, because again, it would be much simpler without as much sort of compliance risk.
I would say probably the sort of extent being enterprise ready in all respects in terms of compliance and integrations would be the key differentiator for Glean.
Slava Rubin (28:29)
If we were to enter today, obviously we need to think about what are the upside potentials or the risks in the coming years. How do you think about that as it relates to a three to five year investment?
Jan-Erik Asplund (28:38)
Yeah, definitely. I one is sort of the phenomenon of experimental ARR, which folks have talked about. You know, a lot of these AI companies are touting their recurring revenue or annualized revenue. And a lot of that is the result of kind of enterprises that want to do AI have received, you know, a sort of mandate to do AI and have signed up for products and
what remains to be seen is kind of what that year one, year two retention looks like on those. And so I think that would be a key thing with Glean is do customers sort of stick around and keep these again because it's you know, it's unclear what the sort of adoption looks like. And I think one of the key things to look at there is the sort of DAU, MAU ratios that we're seeing on the business model front. like it has,
impressive. They've reported this 40 % ratio that people, 40 % of people use it every day versus monthly. And that's very good. think it's maybe also reflection of the types of customers that they have who are more tech forward. So whether it can sort of expand that and project that out into non-traditional tech industries will be key. But yeah, think, yeah, it's all about usage and seeing that customers are actually
seeing the customers are actually using it and getting value.
Slava Rubin (30:01)
Great, okay, and then what do you think the IPO prospects are? Meaning is this the sort of thing that if I invested today, I should think about IPO-ing next year or am I holding for five plus years? What are your thoughts?
Jan-Erik Asplund (30:12)
Yeah, I would take just about five years would be my guess. So I've been tired of this question every time we have a company because it's always some company like Elon Musk's companies or Sam Altman's where they're like, whenever we're go public. This is not like that. So Glean, Arvind was previously the CEO of Rubrik, these data backups company, which he took public after nine years. So we know that they're not sort of...
morally, temperamentally against the idea of IPOing. He did say fairly recently in a presentation that Glean is far away from going public and they did just hit a hundred million. So they're not, I would say not quite there, but I would say in the two, three years to come it's possible.
Slava Rubin (30:53)
Got it. All right. So my final question is to put you on the spot. Are you a a buyer today at five billion and B what do you think the price will be in about three years.
Jan-Erik Asplund (31:04)
Nice. I think so. Yeah, I think I'm excited about Glean. I feel like they've carved out a really strong name and they have a really strong team. And it's not like they're sort of know, attracted to the AI type company that had been seriously working on this for a long time. Price wise, think, let me just, if I just look at my projections. Yeah, I think let's say they're at
uh, 1 billion, um, 1 billion ARR 20, uh, 20, 28, 1.8 billion. Um, I used to be better at mental math. 1.8 billion is like, uh, 27 billion total market. So that's like the current price times five.
Yeah, I could sort of see like a 125, 155 price per share if things sort of go well and we don't enter AI winter. That would be unexpected for me. Definitely upset my priors. But yeah, I could see that.
Slava Rubin (32:04)
That's based on what enterprise value.
Jan-Erik Asplund (32:05)
I think 27 billionish.
Slava Rubin (32:07)
you can see it reached $27 billion in three years.
Jan-Erik Asplund (32:10)
I think so. Let me just make sure I'm doing my math right here.
Slava Rubin (32:12)
Nice. So while you're doing your math, many of you have asked where you could get these types of shares, whether it's Glean or others. So we're excited to be partnered with Upmarket for this briefing. So they're our partner for everyday investors to be able to get into deals like this, whether it's pre IPO or other alternative investments like hedge funds and others, private equity or even real estate.
So thank you Upmarket for partnering with us. If any of you have any questions, we'll share the link here as well as the link for Vincent. And we'll be sure to have this video available afterwards. So while I was putting you on the spot there, mean, my personal perspective is that this has a little bit less volatility. What I mean by that is I think the downside is a little bit less and I think the upside is a little bit less.
I'm not a huge fan that they don't have as much proprietary software and it's much more about stitching things together. I do think stitching things together is awesome, but it's hard for me to see it being like some breakout, massive, proprietary, huge, $100 billion plus company. But I could be wrong. I do think they have an incredible team, obviously, with the rubric.
CEO and having really great pedigree from other companies. I don't think it's cheap, but I do think it's the sort of company that grinds forward because this capability is just the future. It's obvious. It's just a matter of how competitive will the market be in terms of how much market share they could eat up. So those are my quick thoughts. Jan-Erik, what are you going to say on valuation?
Jan-Erik Asplund (33:44)
No, think I could, yeah, I mean, I was just gonna say, I think I could see, know, the 20, 20, 25 billion in three years, I think is plausible, given sort of the, you know, momentum, given what we're seeing on the AI advancements improvement front, but a lot of it hinges on selling it into the kinds of companies that don't adopt this technology necessarily and building really great interfaces into it for sort of the everyday person to be able to use and get value out of in a way that
OpenAI, Anthropic haven't really done. So it's a big product lift.
Slava Rubin (34:15)
They go after financial services, retail manufacturing, is the three verticals you mentioned. Retail manufacturing sometimes are not on the cutting edge of technology if they're able to just eat up a ton of market share there. I I could see 25 billion. I mean, that's not something to sneeze at. It's not a huge valuation like OpenAI has, but if you come in at five and it's worth 25, a 5X is nice in a few years for sure.
Jan-Erik Asplund (34:39)
Yeah, definitely.
Slava Rubin (34:40)
All right, well, this has been a great conversation. Jan-Erik, thank you very much. Everybody who added their questions, thank you for joining and participating. And we look forward to having you join for our next one. Have a good one.
