The Exchanges

Q Can I ask, for those that don't know, can you provide a 62nd summary on what Glean is and how you work?

A So Glean, 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 sort of buried across one of a hundred or a thousand different systems inside the company. So that was sort of like how we started the Google, 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, um, the way to think about Glean is that first, it's a super set of JetGPD, Cloud, 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.

AI assessment note: “Glean is an enterprise AI company. We started as a search company for businesses.”

Q And what did you see? People went crazy. People didn't adopt it. What happened?

A There's, there's a power law, like, you know, in our company, and also at all of our customers, you will see some people who spend 10,000 dollars or 15,000 dollars in tokens every month, and then you have others who are spending 20 dollars. Um, one thing is interesting though, that everybody has embraced AI to some degree. Like everybody's using the, the basic, you know, as, as I mentioned before, the number one application of our use case for AI today in the world is information seeking, Question answering, and everybody's doing that. So you see the entire, like everybody on the team, in our team, as well as our customers, they're all doing that. Everybody's asking questions, everybody's getting some basic summarization, information synthesis going, but the advanced use cases are limited to like five percent of the employee base.

AI assessment note: “There's, there's a power law, like, you know, in our company”

Q What would you most like to change about the startup ecosystem that we see today?

A I actually do think that, you know, there is, um, too much capital available today in the, uh, for startups. And it's actually sometimes creating failure paths for people. I think they're not getting what it takes to build a great company. I like, I'll give you an example, like a startup that has raised a seed round, decides to pay half a million dollars to an engineer, like you were saying before, Um, and this is happening today, and the startup founder is okay with it, the investors are okay with it, but it's just surely not a, uh, sustainable path to actually win, and they're paying it while Google is not, and Google knows that they don't need to actually, um, uh, buy talent like that. So, so I think that is one thing that I feel, um, this overabundance of capital is Getting startups to sort of create structures which are not going to be sustainable for them.

AI assessment note: “there is, um, too much capital available today in the, uh, for startups.”

Q Are you seeing enterprise customers route away from frontier model providers towards open source?

A 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 actually get better. Like, you know, there's, the desire has been there for, you know, for, for many, many years. Nobody, 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 Enthropic 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. Um, and now given that AI has become so expensive, right? I mean, like, if you look at, you probably hear stories all the time about companies, you know, coming up with a annual budget for AI and they run, run past that, like, you know, within a month or two. So in this- Poor CFOs. Yeah. So that sort of, that has really, uh, accelerated that sort of, um, that desire You know, for open source, because, you know, and, and that coupled with the fact that we now have really good models in open source.

AI assessment note: “That's something that's happening now. So I think we are at a real inflection point”

Q 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?

A 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, To actually keep, you know, all the inferencing workload within their own private data centers. When AI just came, there was a, companies were a lot more afraid of, um, getting their data outside of, you know, their own control and, and model companies training with, you know, with their data. But that sort of is a fear that it'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, on enterprise data. So long as, you know, I've signed up You know, for the right kind of contract. So right now the, the drive is coming from cost.

AI assessment note: “I think right now the open source drive is coming from the cost point of view.”

Q How important do you think being first to market is?

A It's actually very advantageous, uh, but it's actually only, it's, it's, it's a, it's a thing that it 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, uh, 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, the giants that, uh, OpenAI and Enthropic have become. So it's a, it's a, it's a huge, it's a huge sort of asset, but neither is it a requirement, nor is it, is it a savior?

AI assessment note: “It's actually very advantageous, uh, but it's actually only... not going to carry you.”

Q How do you advise founders who are losing sleep at night, worried that the frontier model providers will come into that space?

A Oh, right. I would say, like, absolutely don't worry about that. I mean, like, like, I think as a founder, you have to, you have to actually solve problems, not worry, number one, right? So you have, yes, you know, like, you have to always, you know, anticipate, you know, what they're going to do. You have to see their current capabilities. Um, but I think, like, for almost all other AI companies that are not doing frontier model, uh, training, uh, they should see the model companies as a, as a huge asset. Um, not a competition, in my opinion. Like, you know, we, like, you know, we actually believe that everything that Enthropic 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 as, we don't think of that as competition. In fact, like, you know, they've allowed us to actually deliver a product that we could never, you know, without that help.

AI assessment note: “I would say, like, absolutely don't worry about that.”

Q 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 fuck? Where's, where's my return?

A I would say that there is pockets of value realization today. Um, for example, take customer support as a vertical. I think there it was easy to measure productivity. Uh, you could actually say that like in your company is support agent resolves 10 cases a day. Uh, and now they're able to do 12 because of AI. So you could see that being, you know, like a very concrete measure of productivity increase. And, and that's, that's the use case, you know, where AI is actually pretty good because a lot of like, you know, that time, um, that is spent by the support teams is about reading knowledge and then summarizing it to your customers in some ways. So, so there are definitely areas where, uh, there is, um, clear value, um, realization and, and, and enterprises are feeling good. Um, some other ones are more complex. Like for example, I think, I think the, uh, majority of the AI spend right now is on coding. Um, and, and you know that the coding as a practice has changed. Like most developers now actually use AI to write code. They're not writing it by hand anymore. Um, so that part, like you can, in some ways you can, you can say that yes, like AI made a big impact, but the, 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, you know, increas…

AI assessment note: “I would say that there is pockets of value realization today.”

Q anything you do as a leader to try and infuse AI as aggressively as possible? We had Nikesh from Palo Alto on. Every week he has a leadership meeting where he's like, show and tell, and everyone needs to stand up and show something that they've done with AI that week that it replaces what they do, improves their job, whatever it is. Is there anything that you can do?

A Yeah, that's, that's actually a really good idea. Like, you know, I've, I've thought about doing that. Uh, we, we never did the token maxing dashboards and I always thought that was not the right idea, um, to, to just sort of, you know, reward people who are consuming more tokens. Um, I feel, I felt like, you know, we didn't need to do that. You know, we are a native AI company ourselves and people are already kind of, um, educated enough and they will use AI when they need to. Um, but executives like, you know, sharing A success story. Uh, we haven't sort of demanded it from every single exec, every single week. Um, but we have, uh, we have the showcase, like in our town hall, for example, we'll always ask people to share those wins. Like every, every town hall, like, you know, there's a section dedicated to, these are the new AI agents that teams are using to, to work, to work differently.

AI assessment note: “in our town hall, for example, we'll always ask people to share those wins”

Q We spoke about a kind of job displacement. We had an interesting, uh, conversation around that. What job does not exist today that you think will be incredibly common in three to five years time?

A Well, the comp, comp, uh, composite roles will be, um, will be very common. So like, as for example, you know, somebody who can build a product, um, I don't know what to call them, but they, they can, they act like engineers, product managers, designers, uh, similarly, um, in go to market, um, You know, somebody who can sell the product and, and they are capable of not only doing the business negotiations, but they can actually demo the product. They can actually talk about use cases. And instead of having that segregation between account executives and, you know, solution engineers, and then both sales solution architects, I think we will see more and more generalization of roles, like away from specialization. And in fact, I was trying to drive that very, very hard even in our own company, in our company.

AI assessment note: “composite roles will be, um, will be very common”

Q So you don't worry that they all put emphasis on moving into enterprise and being that context?

A Well, they're already doing it, or whether they're doing it or not, we actually face that competition every day, um, with enterprise customers. People often ask us, well, I mean, Cloud can also connect with enterprise systems through MCP. Um, so what's different? Like, you know, what can Glean do which, you know, Cloud cannot? So we had to go and explain, um, like what, like what, you know, context really is. And why it is actually complicated to actually build it. So, so we, so we are, we are competing. In fact, actually, I would say that they probably started to compete with us before others. Like they, you know, like, cause, you know, if you think about cloud co-work as an application or cloud desktop, um, the, the primary use case for that has always been question answering, right? That's like the largest application or use case for AI in the world today. Is in fact information seeking and question answering.

AI assessment note: “we actually face that competition every day, um, with enterprise customers.”

Q 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.

A I do feel like, you know, the, the model business on its own, regardless of like, forget open source for a minute. The, there's plenty of competition, even within the labs. And then, and more and more, uh, companies are coming into that space. So the, um, the, like, you know, in that fierce competition, even in a three, three-way race, I think you can actually get, you know, good amount of pricing pressure. And now of course, you know, with open source, like, you know, it actually is an order of magnitude, um, cheaper prices. So like, I, I actually heard rumors that, that opening, I was going to drastically reduce Um, you know, their model prices in response to like these, these developments, like, you know, competition as an, an open source. So, so that, that, that have been, I think the model business, you know, on its own, um, is, is actually probably not as lucrative as everybody believes, but these companies now have a lot more things. It's not just, they're no longer model companies only.

AI assessment note: “the model business, you know, on its own, um, is, is actually probably not as lucrative”

Q Got you. Okay. And then what role do we have today will we not have? What do we look at and go, oh my gosh, I can't believe we used to do that.

A A lot of analyst roles, like, or like the, the, the data analyst roles, which are not business thinkers, you know, they were given a task that, hey, like, I need to see this data, and then they produced, like, they sort of go and build those specific dashboards, configure backend systems. I think, like, that, that kind of work definitely goes away. Um, the, uh, I think business intelligence is going to be very different, um, Um, business owners will directly be able to get answers to their questions. So, so business and business analysts, like, you know, data analysts, you know, that's sort of one, um, many HR roles, sources, for example, sources like in recruiting, that's the role that I think, uh, is going to definitely get consumed in like, you know, into like a full cycle recruiting role.

AI assessment note: “A lot of analyst roles, like, or like the, the, the data analyst roles”

Q Do you agree with him? Are they more skeptical than ever?

A 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, enterprises, leaders are also worried that, you know, are, is their sort of core IP, um, their data, their information, as well as their way of learning, uh, 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's, that's, that's for, that feeling is there for sure. But I think what he said was that AI is not working, uh, in the enterprises, then everybody's afraid to actually say so. Um, because, you know, it's, it's not supposed to be a cool thing to say.

AI assessment note: “that feeling is there for sure. But I think what he said was”

Q Do you think we will have a world of sovereign models? And do you think, given what we've seen in the last month or so, that we need to have sovereignty over our models?

A The desire for sovereign models Like is, is strong. And it was actually, I would say like it was probably stronger, uh, a year back compared to now, at least like, you know, I feel like I'm hearing less of it to some, you know, like the, there was a period where every, every nation thought that they could build one. Um, there were, when AI was still in its early stages, but then like a lot of those, uh, nations actually figured out that, you know, this, you know, that's not going to be the way. And so they're okay with, um, letting, you know, their enterprises within their own countries, you know, use OpenAI or Enthropic or all the other models. So, so I don't, I'm not an expert. I don't know, like, you know, whether this trend is on the, on the rise or, or sort of like, you know, on the fall a little bit.

AI assessment note: “a lot of those nations actually figured out that... that's not going to be the way”

Q Why would they not be okay with the 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?

A I think it's just, it's just comfort. It's just, what if something goes wrong? There's always paranoia and fear. What if there's a backdoor, some magic, you know, some backdoor that we don't even understand, like, you know, then that could be a backdoor. Um, so, so there are, there are some, 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, a variety of factors that is, um, but ultimately like it's come, it again boils down to who's willing to be bold because this is a new thing. Like, you know, in large enterprises are, I have to make this move and, and the early movers will make the move first and then it will become a more normal thing.

AI assessment note: “There's always paranoia and fear. What if there's a backdoor”

Q So when you say AI, AI, ROI is really a throughput problem, What does that mean?

A Like the first thing that we have to do is, um, like make sure that That you are able to, um, bring the right context, you know, to these, uh, AI agents. Um, the, the way, like, like, I think, like, if you think about most, most enterprises today, the way they're rolling out AI is that they actually just throw it in, throw it into the system and connect AI with all of enterprise systems in a, like, 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, and in this sort of, in this mode, AI is super slow. Um, it takes a lot of time to actually just assemble like, you know, the basic information needs to do the work. It also becomes very, very costly because most of the tokens are being burned. Um, just trying to assemble the right context for that given task. And, and, and you're trying to sort of use AI for things, you know, where it's not even good at or, or needed. So instead like, you know, what we talk about is to make AI really perform and deliver, um, you have to sort of invest around it. You have to make sure that you provided the right context, um, uh, so that it can actually work faster, uh, with, you know, at, at lower c…

AI assessment note: “so that it can actually work faster, uh, with, you know, at, at lower cost.”

Q When you look today, what have you changed your mind on most in the last 12 months?

A I've always personally, like my style has been a little bit too disciplined to be the right strategy anymore. I get that feedback, um, from my team, um, that, you know, we are trying to be conservative. We're trying to make sure that our capital goes a long way. And, and in that sort of, in that mindset, we may lose the land grab. And so I, I, I'm sort of feeling the pressure to change it, uh, myself, like, you know, just change, change how I think about, like, how we should be spending, how we should be investing. But at the same time, like, you know, I had this fundamental belief that a business is always built on discipline and, and, and the real, like, you, you have to charge for the product. It has to generate value for the customers. You know, for every dollar that you invest in marketing, there has to be some good return back from it. Um, you don't assume that you just keep raising the money, um, to make up for all those things, you know, that were not, that were not there.

AI assessment note: “I'm sort of feeling the pressure to change it... how we should be spending”

Q What percent of customers are not okay with open source models?

A This is actually so new. Like, so we actually only have You know, I, 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, uh, on that model. So, so we, we are yet to find. You know, uh, what, what people are going to tell us. And I don't, I don't like, you know, like from a point of view of open source, uh, 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.

AI assessment note: “we are yet to find. You know, what people are going to tell us.”