90% or greater of use cases can now be fully handled by many, many different models, including open source models.
Arvind Jain, the incredible founder of Glean, is one of the technology luminaries of the last decade. He founded Rubrik before, which obviously IPO'd very successfully and is a brilliant public company. Now he's gone on to found Glean, an incredible business today that's raised money from Kleiner Perkins and many other great investors.
For almost all other AI companies that are not doing frontier model training, they should see the model companies as a huge asset. Once you move towards consumption, there's no inherent bundling advantage. You have to do 10 times the work to get the same amount of revenue from your customers.
Ready to go.
Arvind, I'm so excited for this. We have a mutual friend in Mimoon who says many, many wonderful things about you, and I think he's one of the greatest ambassadors of our time, and so I'm really excited for this. So, thank you for joining me.
Now, I think with entrepreneurs, you're either thrilled by winning and it's that chase to win, or you're terrified of losing and it's that fear of losing that inspires you. Which one are you?
Well, that's a good question. I think I would probably say the latter. I'm always worried about what can go wrong, and that keeps me up at night.
I love that. Only the paranoid survive. Has it always been that way?
Even with the success you've had, I'm sorry, it's so interesting. Like, you know, Rubrik was a phenomenal success, public company today, and you're one of the co-founders. It doesn't change with time?
No, because I think, number one, like every time you do a new company or build a new start a new project, it's sort of like starting from scratch in my opinion. Like you have some good lessons from before, but it's a new world. It's a new environment. Like think about Glean, like you know, it's fundamentally different from Rubrik in all ways possible, and especially in the world of AI, you have to be you have to I think you have to think that way because like there is a disruption every single day, and if you start to focus more on sort of keep building on what you've already built, like that's the winning mindset that you know you you you're winning with something, you want to sort of double down on it. I think that's not sufficient in this new AI world.
Can I ask, for those that don't know, can you provide a 60-second summary on what Glean is and how you work?
So, Glean is an enterprise AI company. We started as a search company for businesses. So, help an employee quickly find information that they need, you know, that's sort of buried across one of a hundred or thousand different systems inside their company. So, that was sort of like how we started. It's a Google for your work life, but then over time as AI models got better, so it evolved into an AI platform. So today, the way to think about Glean is that first it's a superset of ChatGPT, Claude, Gemini, all of those combined into one product experience. It's a co-worker for your employees, and it's connected to all of your company's context, how work happens inside your company.
So Mr. Alex Karp from Palantir went on CNBC last week and he said that the largest enterprises in the world were more skeptical than ever of frontier model providers. You work with some of the largest, you have incredible customers. Do you agree with him? Are they more skeptical than ever?
Two things, you know. One, they're terrified of them, like in the sense that I mean, just like every software company is worried about that, hey, will we be in business? Will the models eat it all? Similarly, enterprise leaders are also worried that, you know, is their sort of core IP, their data, their information, as well as their way of learning, their way of doing things, like would it all be, will they be subject to too much of technology dependence on these model providers. So that feeling is there for sure. But I think what he said was that AI is not working in the enterprises, then everybody's afraid to actually say so, because, you know, it's not supposed to be a cool thing to say.
Before we get to AI not working, because I think it's probably one of the most important questions, but it's a whole separate segment. Do you think they're right to be afraid of the frontier model providers eating their lunch or not?
Well, yeah, I mean, depending on the enterprise. Yes, I think the well, look, like you know, if we are talking about fundamentally changing how people work, and if we are saying that majority of the work that we do today is going to be done by an agent which is fully powered by one of these frontier model companies, then in some sense, like you've now transferred a lot of your operations to these technology providers. This is more than technology dependence. This is actually real sort of operational dependence on the companies that are actually running those agents for you. It's actually interesting, like if you think about how work happens over time, like you know, there is, you know, like when you initially do a task for the first time, you maybe you'll document a process, like you know, how, like what are the 10 steps you need to take to actually complete some piece of work, and then over time, you know, people start to sort of optimize and tweak that process. A lot of it never gets documented, and you just you sort of based on doing this work over and over again, you now built all these learnings that you apply in real time to do this work in the future. All of that institutional learning is actually going to accumulate in that agent that is doing that work. So if you don't have any control on running that agent yourself, if you don't own the learning that it actually gains over the years, then you're basically fully dependent on these AI companies to get your work done. So it's absolutely, I think there's a fundamental question in front of enterprises today, like how do they actually use these AI technologies but still retain control and all the compounding learnings that happen with AI? They belong to the enterprises.
Are you seeing enterprise customers move away from frontier model providers towards open source?
That's something that's happening now. So I think we are at a real inflection point with open source. Part of it, you know, like you're waiting on the open source models to actually get better. Like you know, the desire has been there for many, many years. There's no enterprise, you know, that we talk to which is okay with saying that, hey, look, you know, I can get my work done with OpenAI or with Anthropic and I'm good. Everybody wants to make sure that they are in control of their destiny, that they get to use many of these models. And now given that AI has become so expensive, right? I mean, like if you look at people hear stories all the time about companies, you know, coming up with an annual budget for AI and they run past that, like you know, within a month or two. So in this... yeah, so that has really accelerated that desire for open source, because you know, and that coupled with the fact that we now have really good models in open source.
What do they care about? Do they care about cost? Do they care about ownership in terms of their data staying on-prem in models that they actually can have visibility on? What is it?
I think right now the open-source drive is coming from the cost point of view. I mean, there are certain businesses, of course, you know, that have the requirements to actually keep, you know, all the inferencing workload within their own private data centers. When AI just came, companies were a lot more afraid of getting their data outside of, you know, their own control and model companies training with their data. But that sort of is a fear that's no longer there. Like you know, like people believe that the model companies are going to be responsible and not train their models on enterprise data so long as, you know, I've signed up for the right kind of contract. So right now the drive is coming from cost.
In terms of where you sit in the landscape, every one of your investors that I spoke to said that I had to ask this question, which is the obvious question, which is, do you worry that Anthropic will do what they did to Figma, say, or what they've done with legal, or what they're doing with health, and move into your space and cannibalize your business?
First of all, I think we should be careful in terms of what they've actually done for Figma or the legal space or finance space. They are launching these sort of vertical packs, but I think they're quite shallow in my opinion, and I don't actually know of people who are sort of moving their workload entirely from Figma or, for that matter, from any other tool to Anthropic. It's actually sort of net new always, like you know, or like I think it's expanding the market. Like for example, now in design, the designers still use Figma, but the non-designers actually, you know, are using Claude to design. I mean, so that's sort of what we're seeing is that AI is making things simpler. So if people are not experts, you know, on the primary users of that particular tool, they can actually start to do some of that work with Claude.
So you don't worry that they all put emphasis on moving into enterprise and being that context?
Well, they're already doing it. Whether they're doing it or not, we actually face that competition every day with enterprise customers. People will often ask us, well, I mean, Claude can also connect with enterprise systems through MCP. So what's different? Like you know, what can Glean do which, you know, Claude cannot? So we have to go and explain what context really is and why it is actually complicated to actually build it. So we are competing. In fact, actually, I would say that they probably started to compete with us before others. Like they, you know, because you know, if you think about Claude Code as an application or Claude Desktop, the primary use case for that has always been question answering, right? That's the largest application or use case for AI in the world today is in fact information seeking and question answering.
How important do you think being first to market is?
It's actually very advantageous, but it's actually only a thing that helps you, but it's a thing that's not going to carry you. So for us, you know, we actually get a lot of credit for being the first enterprise AI company in the world, the first ones to actually bring RAG into the enterprise, the first ones to build conceptual semantic search. And so that actually gives us that brand and the right to compete in this market even though now, like you know, we're much smaller compared to the giants that OpenAI and Anthropic have become. So it's a huge sort of asset, but neither is it a requirement nor is it a savior.
How do you advise founders who are losing sleep at night, worried that the frontier model providers will come into their space?
Oh right, I would say like absolutely don't worry about that. I mean, like I think as a founder you have to actually solve problems, not worry, number one, right. So you have, yes, you know, like you have to always anticipate what they're going to do. You have to see their current capabilities. But I think like for almost all other AI companies that are not doing frontier model training, they should see the model companies as a huge asset, not a competition in my opinion. Like you know, we actually believe that everything that Anthropic is doing, everything that OpenAI and Google is doing, as well as all the innovation that's happening in open source, that's great news for us. Like we don't worry about that and we don't think of that as competition. In fact, like you know, they've allowed us to deliver a product that we could never, you know, without that help.
Do you not think we're seeing the ultimate commoditization of the model layer? When you speak about Anthropic, OpenAI, and the rise of the model layer and the speed with which new models are coming out, especially bundling from open source Chinese providers, are we not seeing the ultimate commoditization of the model layer?
So one thing is clear, that let's talk about enterprise use cases. 90% or greater of use cases can now be fully handled by many, many different models, including open source models. So there's definitely commoditization from that perspective. In fact, like you know, we at Glean, that's actually one of our core value adds to our customers, you know, which is cost control. We will actually tell them that, hey, look, you know, as people actually complete their tasks on our platform, we actually pick the right model for you, and if you're okay with using open source models, we'll use them, you know, when we think it's appropriate, when it's going to generate a high quality answer.
What percent of customers are not okay with open source models?
This is actually so new, like so we actually only have, you know, I would say that open source truly coming to that, like within three months of frontier capabilities, that has just happened literally like a month back or not even a month, right? You know, I would say GLM 5.2 is the very first time where our own team, for example, feels comfortable that now we can run majority of our workloads on that model. So we are yet to find, you know, what people are going to tell us. And I don't, like, you know, from a point of view of open source and using the model, everybody's going to be fine. The question is going to be, are they okay with the Chinese model or not? That's the only question here. It's not open source versus closed source.
Why would they not be okay with a Chinese model when you look at the ownership that you have, the ability to have it on-prem? You're not sharing anything back to China. Why would you not be?
I think it's just comfort. It's just what if something goes wrong? There's always paranoia and fear. What if there's a back door? Some magic, you know, some back door that we don't even understand, like you know, then that could be a back door. So there are some concerns. There's also if you use these models and if it becomes a known thing, then you know, it could be used against you in some ways, you know, by your competitors and things like that. So like you know, variety of factors. But ultimately, like it again boils down to who's willing to be bold because this is a new thing. Like you know, large enterprises have to make this move, and the early movers will make the move first, and then it'll become a more normal thing.
I'm always doing this show to learn. If 90% of enterprise workflows can be done with open models, have we completely mispriced the frontier model landscape?
It's a very different time. I do feel like, you know, the model business on its own, regardless of, like, forget open source for a minute, there is plenty of competition even within the labs, and more and more companies are coming into that space. So in that fierce competition, even in a three-way race, I think you can actually get, you know, a good amount of pricing pressure. And now, of course, you know, with open source, like you know, it actually is an order of magnitude cheaper prices. So like, I actually heard rumors that OpenAI was going to drastically reduce, you know, their model prices in response to these developments, like you know, competition and open source. So I mean, I think the model business, you know, on its own is actually probably not as lucrative as everybody believes. But these companies now have a lot more things. They're no longer model companies only.
Totally get that. But if they're doing shallow things in those adjacencies, they're not exactly going to generate a trillion dollars of revenue. Like Dario said,
if you think about, like you know, first of all, like you know, these two labs, they're very fundamentally different businesses. OpenAI, of course, has amazing consumer product. And Anthropic, actually, the interesting thing that is happening is people are actually building on top of their platform. And so when you think about Anthropic right now, there are a lot of folks who are actually developing automations and skills, and everybody's sort of, they're creating these MCP servers to their internal systems, getting connected and connected all to Claude. So there's an ecosystem actually that's being developed. So they very much, you should consider them an application-level company, not just a model company.
If you were to make a guess in three years' time, what percent of your workflows do you think are through open source?
Well, we've been telling customers, I believe that the majority of enterprise workloads will actually be on open source models in three years for sure. Yeah.
Another competitive element that you face, forgetting kind of the model providers, is like actually Microsoft. You know, Microsoft have made a phenomenal business on the back of creating a 70% as good product but bundling it into a bundle for enterprises and then selling it with a nice sticker on it. How do you think about the bundling pressure from a Microsoft Copilot as a competitive threat?
Well, for us, they are one of our most significant competitors. And the bundling strategy actually works, and you have to fight, you know, against that. I mean, there's luckily, like you know, there's always been room for best-of-breed, you know, software, and you know, like people and our customers think of us exactly like that. Like if you are trying to actually bring a great search product, if you're trying to, you know, build a horizontal comprehensive AI platform, they know that, you know, we do it better. And so companies are willing to invest on top of that, like you know, as part of the bundled product suite from Microsoft. But the other thing, like you know, is actually maybe making bundling not as effective of a strategy anymore is the fact that AI is moving towards consumption-based models. So once you move towards consumption, there's no inherent bundling advantage, because you know, like, and as a business, I can get six tools and I let the users choose where they want to do their work. Wherever they do their work, I have to pay for it for that particular unit of work. So consumption, you know, can ultimately break that bundling strategy.
And respectfully, I don't know if it does if you're working with enterprise because they will make you compliant as an enterprise bundle. And so you'll go through approvals, processes, sign-off processes internally for the largest enterprises in the world, your VWs or your Fords or your Gs or Tyson chickens. I always use them as like a, you know, I know, random companies. But they'll approve Microsoft as one vendor. If they're suddenly having to approve 15 vendors, forgetting the pricing and the transactions, it creates a vendor management problem that they didn't have before.
That is true. Yeah. But I think I would say that if you go and talk to companies that have been on the other side of the Microsoft onslaught, most of them will talk about pricing as the main killer because I think it's hard to compete with free.
Who's a fiercer competitor, Microsoft or the frontier models?
Good question. I think it's early to tell that. But Microsoft is formidable. And I think, like, so if you look at our experience as we go and prospect, I think we would hear this answer more often that, well, like you know, we are a Microsoft customer and we already have, you know, we're getting Copilot, and so therefore, like, it doesn't make sense for them to consider us. Like we do hear that, and we hear that more often than we hear from somebody that, oh, like you know, I've embraced, you know, one of the lab products, and therefore there's nothing else that I'm going to do.
We mentioned Alex Karp's interview at the beginning, and I interrupted you and said, let's, before we dive to, like, we're not getting value. I think 2026 H2 and 2027 is the year where everyone goes, well, hang on a minute. Is this spend generating output or return of ROI? How do we think about the return on investment that enterprises are getting, and is Alex Karp right in saying everyone's going, what the [__]? Where's my return?
I would say that there is pockets of value realization today. For example, take customer support as a vertical. I think there it was easy to measure productivity. You could actually say that, like, in your company, a support agent resolves 10 cases a day, and now they're able to do 12 because of AI. So you could see that in, you know, like a very concrete measure of productivity increase, and that's a use case, you know, where AI is actually pretty good because a lot of, like, you know, that time that is spent by the support teams is about reading knowledge and then summarizing it to your customers in some ways. So there are definitely areas where there is clear value realization, and enterprises are feeling good. Some other ones are more complex. Like for example, I think the majority of the AI spend right now is on coding, and you know that coding as a practice has changed. Like most developers now actually use AI to write code. They're not writing it by hand anymore. So that part, like, you can in some ways say that yes, AI made a big impact, but are they shipping the products faster or not? And that's where we hear most of the companies saying that no, that the actual shipping speed of products has not increased, even though coding speed increased significantly, because I know that's only a small part of overall shipping a product.
Has your shipping speed increased?
I would say, like you know, it's hard to actually measure. That's the challenge because engineering productivity is one of the most difficult things to measure. It's the fuzziest of the jobs out there. If you look at some of the metrics, like lines of code written, of course we're writing way more lines of code now. But if you look at, you know, are we shipping features at a greater pace? Yes, we are. But it's a result also of we have a larger team. You know, we have a team that is more tenured than it was before. So sometimes it's hard to tease it apart. But with that, what do we do as a company? Like we are right now saying that, like, look, you know, we are just going to keep investing.
What percent of Glean code, say, do you think is written by AI now?
It's probably about almost 100%. Like nobody's actually like writing the initial code, you know, by hand anymore. Like maybe sometimes.
You've got an artisan in the corner.
Yeah. So yeah, so almost all the code is being written with AI, but we actually enforce human reviews. So you cannot actually generate tons of, you know, AI code and then just check it in the repos. We're probably more conservative than most other companies. There was in fact a discussion inside the company that, well, like you know, now AI can write so much code, and the real bottleneck has shifted from the person who writes the code to the person who has to review. And so there was a proposal to actually eliminate code reviews and then just, and many companies are doing that, you know, like let the code directly get submitted into the repos.
Well, if you have to have a stringent code review process, it almost removes the point of having a fastened code development process.
If you, yeah, it's true. So that's why. So, but I think, like you know, what it's doing right now is we're still in the learning phase of using AI, you know, properly, effectively, thinking about long-term ramifications of it. Because when you write code, for example, with AI, you know, you can write like, you know, a million lines of code, but it becomes incredibly hard to actually maintain it and understand it and manage it over time. So.
Is that not what AI does though? You have AI that does refactoring and AI that does security and AI that does.
Yeah. The only thing is that, you know, it's not that perfect right now. So you have to, I think we would rather, I think right now we're willing to pay the cost of reviewing the code. So we're still faster than before because the writing part is actually much faster now. And the person who writes is the one who actually does the first review.
So when you say AI ROI is really a throughput problem, what does that mean?
Like the first thing that we have to do is make sure that you are able to bring the right context, you know, to these AI agents. The way, like, I think, like if you think about most enterprises today, the way they're rolling out AI is that they actually just throw it into the system and connect AI with all of your enterprise systems in a rudimentary manner using, you know, MCP servers. And now you're letting, like, any piece of work that you are trying to do with AI, you're letting the models sort of brute force their way into trying to figure out and assemble the right raw materials that they need to complete the task and then do it. And in this sort of mode, AI is super slow. It takes a lot of time to actually just assemble, like, you know, the basic information it needs to do the work. It also becomes very, very costly because most of the tokens are being burnt just trying to assemble the right context for that given task. And you're trying to sort of use AI for things, you know, where it's not even good at or needed. So instead, like, you know, what we talk about is to make AI really perform and deliver, you have to sort of invest around it. You have to make sure that you provide it the right context so that it can actually work faster at a lower cost.
What does it mean invest around it? And are we wrong as CEOs to be urging all of our team members to be trying to replace themselves with AI, even if it means that we're wasting tokens?
I think it's a wrong goal in my opinion to say that, hey, like, replace yourself with AI. First of all, I think you're giving too much credit to AI, and you know, when you say that, it's just not ready right now. There's, you give me a name of one job that you can replace, you know, with AI, for example. Do you think it can replace your EA?
Mine? No. But I'm a [__] diva for most people.
I think it can do the majority. Yeah, I do. And that's the thing, it can actually take care of a lot of things, you know, for any given role, but it cannot replace the final, you know, the last intangible.
No, but that can be a tipping point where actually for a lot of people, if it does 90%, fine. You know what? You'll do that birthday present for your wife because it's once a year. It's not very often, and Claude isn't quite personal enough to know your wife's preferences of perfume. And so it's very, but that role will get cannibalized.
I'm not sure. And I'll tell you why. I think you want to be performing the best in whatever you do, and I don't think you're going to take a 90% solution.
I'm not cost constrained being a dick.
Well, I mean, like, look, it's not about you not being cost constrained. It's about you have to be competitive in your work with others. Like, remember, they also have all the AI tools that you have, but if they also have a human on top, you know, how are you going to compete with them?
So do you not think team, how many people do you have now?
In our company, we're over a thousand people now.
Over a thousand people. How many do you think you'll have in five years' time?
Well, hopefully, you know, 5,000.
But that is very atypical. I sit with the biggest CEOs in the world, and every single one of them is shrinking teams, and every single one of them is saying.
I absolutely don't believe in it.
Well, I mean, I think like first think logically. Take two companies, take Coca-Cola and Pepsi, or like you know, two companies that compete with each other. One company decides to shrink and the other one still has a lot more people. Both of them have full access to the same AI tools and technology. And so now the question is, if you were trying to do the same amount of work and you believe you can do it with fewer people and therefore you shrink, your competition can also do the same, but they chose actually not to do the same amount of work. They chose to actually, you know, elevate and build a 10x better product or build 10 times more, produce 10 times more, you know, goods because they have more people. They're going to be larger. They're going to beat you.
But I don't think more people makes for better products.
That's a different thing.
If I can cut headcount and then afford the best frontier models, the best technology for my 100x engineers because I've reduced headcount.
But I think that argument...
And I think more people slow down everything.
But that's not an AI argument. That argument has always been true.
Sure. But combined with the AI element of you're able to ship more. If you're able to ship more, I promise you when you have, and you know this, when you have more people, they'll just put up the barriers to get in the way of that product going out.
Well, look, we have these AI discussions right now, but before that, just post-COVID, like many companies felt they were bloated, they cut down 15%, 20% of their staff, and every CEO came out and said that as a result of that they're actually moving 20% faster, right? So a lot of companies came and talked about that, like you talked about. So that's an argument that's always there. Like you know, at some point, teams get large, they start to slow each other down. Humans do that, I also believe in that. But ultimately, people are also your asset and you have to be able to deploy them correctly in the right set of projects. And I don't think the world's greatest companies are going to be companies with 100 people. And look at the model companies, you know, same for them. Like why are they hiring so aggressively?
Do you not think that the best people will want to work with the best technology and we'll see an increase in technology spend by the biggest companies in the world from 8 to 12% where it is today to maybe 16 to 20%? And then actually you'll see a reduction in headcount but an increase in technology spend and the best people will want to go where they have the best tools and equipment.
I'm not sure about that either because I think technology is actually not supposed to increase in cost.
First of all, do you admit that currently this technology is priced, you know, absurdly for what it delivers?
I think it totally depends on what it's doing for.
No, I don't at all. For Cursor or for any of the dev tools, I think it's still dramatically underpriced. When you look at Marc Benioff spending 300 million on Anthropic, it's 3.7% of developer salaries. I think that's relatively small.
I would say it's absurdly expensive. I'll give an example. We have a really cool triage agent for engineering. You know, and we have a 15-person on-call team whose work was to actually triage every single production issue that happens, like any system alerts, you know, things that are going bad. And we built this agent that actually now is taking care of like 95% of those issues automatically for them. But even there, it's actually doing it at a cost which is actually questionable. Like, you know, is it actually more effective and more efficient than humans? We were spending a million dollars a month on that particular agent. And that was actually more than the cost of...
Are you buying Cristiano Ronaldo? What are you doing?
No. Like, well, I mean, yeah, cost like that, I mean, it is quite expensive. So the...
But sorry, can I go back? You said, because I discuss this a lot on the show, so you're making me much smarter. You think that spending 3.8% of developer salaries on these tools is a lot. If you think that's a lot, then these model providers are absolutely screwed.
Well, I mean, I think the point that I'm making is, well, the 3.8% number actually doesn't seem high at all like when you look at it that way.
Yeah, but I also know that already you see with open source that you can do the same amount of work for a tenth of the cost, right?
