SPEAKER_01: Who's going to win long term?Who will have the best models and which will be the most market share?These closed models or open source models?
SPEAKER_02: I think in the future, majority of AI work is going to be based on open source models. I would say 80% of all AI inferencing or people building AI applications is going to be based on open domain models.And some of those will be fully open domain.Some of them could be open domain, which are supported by enterprises.And that's really how the industry has progressed over the last two decades.It's just really hard to beat open source.
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SPEAKER_01: All right, everybody, welcome back to the program.Today on the program, we've got Arvind Jain.He is from a company called Glean.What is Glean doing?We're going to find out today.They're trying to get corporations, enterprises, to use AI to help them sort, make sense of, and search their data.We'll hear all about it from Arvind.Arvind, welcome to the program.Thank you for having me. So tell me, what are you building and why is it important?
SPEAKER_02: So think of Glean like Google or ChatGPD, but inside your company.It's a product where people can go and ask any questions they have.And Glean will use all of your company's knowledge and data and information to answer those questions to you.So that's what our product is.We are an AI-powered search engine.We are an AI-powered assistant that helps people get more work done.
SPEAKER_01: Got it.And so you're not asking the chat GPT, for example, to answer questions or make a marketing plan.This is specifically to search the data inside your enterprise.That's right.And ask questions against it.So I see on the site, you mentioned every department that any company could have sales and, you know, marketing, etc.Which categories?What's the beachhead market?Where are you being the most effective for your customers?
SPEAKER_02: So typically, Glean gets deployed company-wide.Typically, Glean will sell to CIOs.Our top users do tend to be engineers, support people, folks in sales.Those are the three biggest user populations that we have.But in general, this is a product that actually is useful to every single employee in a business.And therefore, we don't go and sell the product to individual departments.We typically go and sell through the CIOs.
SPEAKER_01: So the CIO is evaluating new technologies and saying this is going to go across all the verticals in the organization, we need a solution to ask questions in every department.That's right.Well, that means you're going up against I assume, like intercom HubSpot, Zen desk for support then.So or are you sitting on top of those systems?
SPEAKER_02: We typically sit on top of those systems.Think of Glean as an assistant.It's a layer, it's a connective tissue that connects your knowledge across all of your different systems.So while you may be using Intercom or Zendesk as your customer CRM and your support people use that as a system of record, but when they have a case that has come to them, so they'll open the case in Zendesk or Intercom and they need to actually resolve it. to resolve that, we're going to actually help them, you know, find the right answers.And, you know, sometimes those answers may be in knowledge articles in Zendesk, but sometimes they may be, you know, answers that are in some Slack conversations or in some internal JIRA, you know, issue.And, and sometimes, you know, the answers are with people like, you know, that you can actually go and so Glean will actually help you find those people or that knowledge that sits outside of those systems and help you answer those questions.
SPEAKER_01: So it's sort of like, you know, give me like, what's the best example?
SPEAKER_02: Well, I mean, like, let's say that, you know, there is a, As a support agent, somebody files a request that my product has stopped working for some reason.And what would have happened is that maybe there's a needed release that got rolled out and there's a bug in that.And right now, people inside the company are actively discussing that issue in some Slack channel.It has not made its way into your knowledge articles yet. So when you get a request from your customers, we'll actually quickly help you tap into, hey, has other people run into it?And are there conversations inside the company that could help you sort of figure out a quick answer back for your customer?
SPEAKER_01: How do you deal with the fact that a lot of this data is confidential and maybe not everybody in the organization should see it?There's permissions in each of these systems, but you're going to index the whole thing.So somebody could ask about salaries in the company or different things.The language model has been trained on all this data.I assume you're training your language model and all this.What LLM are you using?
SPEAKER_02: Yeah, so first of all, like, you know, we are LLM agnostic.So we can work with GPT-4 or Gemini or, you know, Which one are you using right now?We use all of them.So all of them, like typically we let customers make a choice, like, you know, what language models, you know, they would like to use.Which one do they pick most often? Actually, like most of the times they will, you know, give back the choice to us.So like, you know, we get to choose.I think right now we started out with, you know, I think majority of our deployments right now are using GPT-4.
SPEAKER_01: With GPT-4, when you put that data in... How do you know if the GPT-4 model by OpenAI assures you that the customer data does not go in there?Or do you have it off-prem?How do you manage that issue with the language models?Because a lot of CIOs and CEOs are really concerned about giving OpenAI their data to train it on.
SPEAKER_02: Yeah, so see, like, you know, you're absolutely right.Like, if you think about using AI in the enterprise, first of all, your data inside the company, like has, first of all, it's private to you as a company.But second, within the company, you know, there's governance on that data, like not every employee can actually use, you know, all the information that exists within the company. So Glean actually solves both of those problems.So number one, we're not actually training or fine-tuning models like GPT-4.We're actually using them only as summarization and synthesis engines.The way our product works is that when you come and ask a question and you're one of the employees in a company, what we will do is first, based on that question, we're going to use our core search technology and we'll assemble the right pieces of knowledge and information that we think is going to be able to sort of answer that question that you have.And we will actually restrict you, so we know who you are and what content you have permissions for, so we'll only let you use the information that you are actually individually authorized to use.Now, once you've actually gathered this information safely, now we will actually take the snippets of this information and ask an LLM like GPT-4 to summarize that information.
Do you trust OpenAI?So we actually work with Azure to use GPT-4.And the way we work with these model providers is that we have a contract with them where our customers are guaranteed full privacy for their data.Their data is never logged outside of their own clean environments.And Azure or Google don't have the ability to actually go and train any models on that data.So CSR customers get full assurance.
SPEAKER_01: So you trust them with that function, because a lot of CIOs have been a little bit concerned, watching some of these, you may have seen you see the viral video of the CTO of opening I talking about Sora last week, and she couldn't answer the question of like, what training data was there, and she wasn't sure.And it kind of felt like she was lying.I think based most people's thing there.So it does seem to me like the big challenge here, you tell me if I'm wrong, is that using these third party models, even even on Azure or Google Cloud, people are nervous, are people nervous about that, and they want to move to having, say, an open source one on prem and or just, you know, in their own cloud?
SPEAKER_02: Well, see, like, you know, different customers are a different level of sort of both paranoia and security requirements.A lot of the companies today, a lot of enterprises are now comfortable with storing their enterprise information in the big cloud vendors like Google or Microsoft or AWS. a lot of like, you know, business technology and systems run in these systems.And, you know, so the trust level, you know, for these, you know, the big three cloud providers is actually quite high.And if you think about it, like, you know, like, see, AI is a new thing, you know, first of all, already, like my business data is, you know, is in these systems. Now, I'm also using some additional AI models, again, hosted within Google or Microsoft.As long as I get those VPC controls, I'm actually comfortable with that.Most of the customers feel that way.If you don't trust Google or Microsoft, then you typically are
running everything on premises.And we, as a company, we support also hosting models ourselves.So if a customer wants to use an open domain based model as the core LLM that's in their Glean experience, we also allow them to do that.
SPEAKER_01: Yeah, I mean, there was a big instance of a bunch of Samsung employees, I guess, using chat GPT-4.And then their source code and other information was then trained into chat GPT-4.I'm sure you've seen that.And I'm guessing that comes up with CIOs.Can you explain what happened there?
SPEAKER_02: Yeah, so in that particular instance, the employees in the company, they were actually like, you know, using the standard chat GPT product.And they were actually pasting, you know, their code inside of that, like, you know, code or sensitive, you know, documents within the company, you were posting that, you know, within that chat GPT interface, and then asking, you know, chat GPT to do some work on it. And so when you actually use these services directly, like these are meant to be consumer services, if you use them directly, you don't have any controls.Like, you know, every, you know, data that you actually put in that system, like, you know, like OpenAI, you know, has, you know, like, you know, is allowed to actually go and like train their future models, you know, using that information.
SPEAKER_01: On the public interactions, right?
SPEAKER_02: On the public interactions.And that's what happened there.The way to sort of, you know, make sure that, you know, you are not, you know, exposing your private as a CIO, like, you know, to ensure that your employees are not sending information, you know, to these public consumer based products.And that's, that's when you use a product like lean, because if you use lean, you know, now you have a very safe and secure environment, you know, where people can still go and ask questions.And, and, and we will make sure that like, you know, any information that is being sent to Azure or to Google.It's actually following that contract and that security agreement that you have from them that they're not going to use that information to train.
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Head over to openphone.com slash twist to start your free trial and get 20% off. How do you compete against the native tools, you know, getting more and more robust?So you can't possibly write an LLM that's going to work as good as, you know, the one that's built into, you know, Salesforce eventually.Does Salesforce build in a language model yet or no?
SPEAKER_02: So, so typically, like, you know, companies like, you know, SaaS companies like Salesforce, or Atlassian product, you know, or, you know, any of these systems, they're not building language models, language models are built by No, but they're building language models into their product.
SPEAKER_01: So and you're not building language models either, right?
SPEAKER_02: Well, so it's complicated.So we build language models.We build smaller models, you know, to sort of, you know, build semantic understanding of your company knowledge.But we, as well as Salesforce and, you know, like other product companies, all of us use, you know, APIs to, you know, to these large language model providers like GPT-4 or Gemini to do some work.So typically in that model what happens is that you're basically sending prompts to these models and having these models do some work on that prompt and return back a response and you sort of create these AI experiences within your application. So now to think about like, you know, if you think about, you know, Salesforce, they're going to actually have some AI features within their product.You know, Coda will have some features within theirs, Jira will have some.So yeah, so you're right that, you know, every application in the future, you can imagine that they will have some AI smarts, right?They might even move to the chat interface, right?
Yeah, they may have a chat interface in addition to actually do some of the work that people do with those products.For example, today, if you want to create a new issue in Jira, typically you'll go in that app and click a button and then fill a form.But you can imagine that they may have a chat interface that allows you to go and create that new issue using a natural language interface.Those things may happen in the future, but that's not what Glean is actually solving for.Glean is solving a different problem, which is that if you think about your work inside a company, it happens across many different systems. I'll give you one user journey as an example.As an engineer, let's have to actually go and build this new technical component.And so that journey for me is going to start with first talking to people.I'm going to be actually having some conversations like this one, like on Zoom, I'm going to be talking to some other engineers.
talking about design choices.I have some conversations in Slack where we're talking about like, hey, what about this approach versus that approach?There's actually a JIRA which actually tracks why am I even building this technical component?What were the problems that we're trying to solve? Then, so, you know, first, like, you have all of these different things.Then, like, at some point, I'm going to actually write a design document, like, maybe in Google Drive to sort of describe my design.And then later on, I'm going to actually write code, and that's going to go in GitHub.And so if you think about, like, this whole journey, all the information about this project, like it actually spanned all of these different systems.So now think about like, you know, six months down the line, somebody comes and asks a question that, hey, why did we use, you know, this programming language, you know, to build this component?
Where is the answer?It's not in one place.It's actually, you get that answer by actually consulting, you know, all the knowledge that sits in like those five or six different systems.And so that's where the power of Glean comes in.Like if you think about, you know, your work, we are tying together knowledge from all of these different systems. in one place.And we give you as a user, like we remove that burden from you, like, you know, hey, where should we go and find things?Where should we go ask questions?
SPEAKER_01: And it's a great place to start.If I was joining the company, or I'm the CFO, and we're the chief operating officer, and somebody tells me about Project Blue, bluebird and i don't know what's going on project hey give me an overview of project over bluebird and it gives me all those but if i'm not the the question i have uh the next question i have there because that's kind of a cool feature to be able to go across the entire enterprise so i totally get that But there's a discussion going on in a Slack room that I don't have permission to, permission for.So how do you deal with that?Maybe there's a Bluebird, Project Bluebird Slack room.It's got 20 people in it.But I am the COO and the CFO.I don't have access to that room.I was never invited.
But you have that in the LLM.So how do you let me know that there's a conversation there that I don't have the rights to see because of the way Slack works or Jira works?The COO doesn't even have a Jira account.They don't have a GitHub account.So how do you deal with permissions?
SPEAKER_02: Yeah.So two questions.First of all, like, you know, the way our system works, there is no data, none of your enterprise data is actually in the LLM.The LLM is basically just the standard, you know, like language model that is trained on the world's public knowledge. We're not using it to store knowledge.But now, the way Glean works is that when we actually connect with all of these different systems, we have actually built an understanding of how permission works in those systems.So for example, when Glean connects with Slack, It knows the concept of channels.It knows that certain channels are private, some of them are public.If it is private, it knows who are the members in those channels.
So now, we're going to index every single message or conversation and we know exactly who are the people who have access to it. All of this information is stored in our search index.Same for a document on Google Drive.We know who are the collaborators on that doc.We store these permissions.This is unique about the Glean technology.It's fully permissions aware.Now, when you come in and ask a question in Glean, you have to be, first of all, signed in.You have to present to us your identity, who you are. um and we are able to now you know retrieve documents from the index but only work on the documents that you know we already know that you have permissions for so so so building that like you know that's part of our core technology is to understand you know like these authentication and permission models in each one of these individual labs and sort of um and make them make them work so you know jason the coo can't see project bluebird i'm not in that group so when i do a search
SPEAKER_01: It won't show you anything.You will show me anything.
SPEAKER_02: Yeah, we won't even tell you we won't even tell you that, hey, there's some useful information.But we're not like we can't share with you talk to somebody else.Because sometimes even that is dangerous.
SPEAKER_01: Like even I was about to say, like, let's say you did a search.Who's on a performance improvement plan?
SPEAKER_02: That's right.
SPEAKER_01: It's like, there are seven conversations about performance improvement plans happening with these people in them, with these names.And you'd be like, wait a second, who are the seven people who are in a... Just the fact that there is a file in and of itself is information.That's right.And do these... You know, Slack has pretty robust AI coming on board.Notion and Coda also have AI built in.Have they built APIs into it?Or are you just having to rebuild all of that from scratch against their services?Or are you doing the search in Slack as if I was logged in?
SPEAKER_02: We do search natively in our system.So the way our system works is that we bring content from those systems and index them in Glean.So as content is being produced, as somebody sends a new message, we get a notification and we'll then take that message and index that in our system. So this is continuous.This is done in real time, all the time.And now when you come into a search, that search is entirely served from within our system.And that's important because for two reasons.One, you need to be fast.You can't actually, in an enterprise, you have 1,000 systems that you're using.You can't actually, when a user comes and asks a question to you, you can't actually send a message to all hundreds of them and wait for responses to come back.
SPEAKER_01: Yeah, no, search correlates to everything. search usage correlates directly with the speed of returns.That was Google's big lesson, right?
SPEAKER_02: That's right, exactly.
SPEAKER_01: If you make it faster, people use it more.
SPEAKER_02: Yeah, in fact, that's one of the things I worked on at Google, you know, was actually making it fast.But the second thing is, the second thing is also, like, search is a hard problem.It's sort of like magic.Like, you know, you come in, you type two words, and like, I need to sort of now figure out from those 10 million documents the one that exactly you're looking for.So there's a lot of work that needs to be done to build a great search.And I think, like, like what we've seen typically is that like each one of these individual SAS products, like, you know, they're, they don't have so many resources to put on search, like we have hundreds of engineers to actually make search better.
SPEAKER_01: It's such a good point, right?Search is like an afterthought for them, or they may just use some open source library, they never update their search.Even like, I've been complaining about Twitter search since Twitter was born.And there was a third party called some eyes that they bought to make their search a little bit better.And that was 10 years ago.
SPEAKER_02: And because you think about like enterprise, enterprise software companies, they don't win customers that way.Like, you know, in Jira and Asana are competing that they're competing on features, not by saying that, hey, my search is better than yours.So I think that's another thing.To really solve the search problem, you have to do a lot more work.I'll say just one more thing on this.Why thinking about search in the way we think is important?It's really important to take all of your enterprise context.That sometimes gives you signals on both you know, what information is actually relevant and important and to whom.
So one example, let's say that, you know, somebody writes a, there is a document that, you know, that talks about benefits.But whenever somebody asks a question in Slack that, hey, like, you know, where's our benefits policy?Like, you know, somebody in HR shares that document with you.So there's a lot of, you know, like there's exchanges that are happening in Slack, which tells you that, hey, this particular document is authoritative for that answer.
SPEAKER_01: So Slack's great training data in that way.
SPEAKER_02: Yeah, and Slack just being one example, if you think about there are these interconnections between how a JIRA issue was created and how it's referenced in the Slack conversations.When you think about your enterprise knowledge, it's a graph.There's knowledge, there's a lot of different pieces of knowledge and they have all interconnections with them. And similarly, there are people.And people, of course, we're talking about there are engineers, there are support people, there are salespeople.And we build these learnings that engineers are actually clicking on this or using this document a lot more than salespeople and vice versa.So all of those learnings is sort of what enables you to find out what is more relevant information for whom. And that's the core of what Glean is and that's why it's so important to actually have that full enterprise-wide view of your people as well as your knowledge.
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So for example, people think like their DMs on Slack or their emails, even if you delete your emails, those things are stored if you have the settings done properly, like in Google Docs or Microsoft Teams.And so because you ingest everything, you do have the ability to do a God mode where the compliance could say, hey, did anybody... say this, let's say it's insider trading, you know, did anybody share Project Bluebird?Let's say Project Bluebird was an acquisition.Did anybody say the word Bluebird?And you could actually see across all documents.Yeah, does that exist like a super God compliance mode?
SPEAKER_02: There is there is a compliance mode, which is highly restricted, and it's available only to your governance, you know, and legal teams, like, you know, for exactly the use cases like that, you know, eDiscovery, or, you know, also like for privacy compliance, like, for example, you know, a big use case, you know, that that that's out there is, you know, you have a, you know, a, you know, a, you know, previous, like an ex customer, or an ex employee, you know, who comes and tells you that, hey, you know, delete all my data. right, you know, and then you have to sort of, you know, there are laws that actually required to do that, like, you know, and so how, but how do you even figure out like, nowhere is all that information?
SPEAKER_01: Where is that data?Facebook had this issue, because Facebook had been doing backups of backups of backups.And that's why when you delete your account, people are like, Oh, you know, they say it takes 30 days.I think that's because they have all kinds of mirrors and mirror images of data so many different places, they want to be thoughtful and thorough about it would seem like a CEO God mode would be one of the great features of being able to look across this whole amount of data if you're working at a company you should just know by default anything you do on your laptop is your company's and every email you send never use corporate devices yeah order from amazon or do private communications gosh it's 2024 i don't know why i have to say this to people but i'm shocked that people will because i'm on the board of so many companies or investments some story will come up that people were sending things to each other on slack or teams or doing something on a corporate device that is completely insane that you should not be doing.
SPEAKER_02: That's right.Yeah.And I think the, from our perspective, like, you know, we, like, you know, we help companies, you know, from a compliance perspective there.But, but Glean is actually not a system of records.So that's not like, you know, another system that you do worry about, like in the sense of when you need to delete data for somebody, like, you know, you don't have to actually go and explicitly go and delete that in Glean.
SPEAKER_01: Well, you do have to get rid of the data and glean if it's indexed it, right?
SPEAKER_02: Because you have the index.We stay in sync with the actual system.So like if deleted in Slack, it's automatically deleted in our system.
SPEAKER_01: Wow, that's super complicated to take care of all that, huh?
SPEAKER_02: Yeah, that's where the complexity is. But actually, it's an interesting thing, like, you know, you talk about AI, you know, like, everybody wants AI.And this is one of the key problems that businesses are running into, which is like, look, you know, we have all this information in our company.And yes, like, you know, we've set some rules, you know, permissions, but you don't always get it right.Like, you know, oftentimes, you know, there'll be some documents somewhere that like, you know, sensitive and the person in HR, like they didn't know how to set correct permissions, they made it open to everybody in the company. And, and you start living with that, like, because nobody can find anything anyways.Like, who cares?Like, it was a toxin somewhere that, you know, but yeah, I was just going to say that.So that's a, that's a big issue today.Like, you know, with like AI, because now AI does all of that work for you.
Like, you know, like in this new world, you just get to ask questions.Right.And, And there's this AI, you know, like, for example, our product is connected with all of the company information, and it's going to actually answer questions back for me.So it sort of makes, you know, these governance gaps, you know, like, you know, you're going to pay for it now, like, they're going to become a big problem.And so that's one of the things that we hear a lot from, you know, CIOs, you know, they feel AI is powerful, but they're also scared of it.
SPEAKER_01: When you see, I mean, you must have seen the Devon demo last week, the AI coder going out and like doing jobs on its own.Did you see that last week?
SPEAKER_02: I didn't see it, but I've sort of, you know, seen like things like that and heard discussions about it.
SPEAKER_01: So, you know, now that you're indexing the whole company, you're watching all this data and code and customer support tickets and sales all occurring.It would seem to me that you understand, you know, what a salesperson does all day. what a coder does all day, and all their activity buzzing around.Yeah, so that's great.I mean, you understand who the most productive employees are on a certain level, right?You could tell me who's working, who's making the most commits, and this exists already in JIRA.You know, you could tell me, hey, this person's work hours, they work three hours today, according to the data we've seen.So is there some idea here of looking at productivity?There are certain apps that people are using to monitor their own productivity. Then there's like people tracking their hours.
But it does seem you could tell me, hey, you know, this person hasn't done anything for four days, I guess they're on vacation, or, hey, this person is putting in, they're dropping, you know, data into all these different resources, 12 hours a day, they're working 50% harder than the average person.
SPEAKER_02: Well, I mean, so we didn't start our company with the goal of sort of building these analytics or like some people analytics in some sense.Our goal has always been to help people get work done faster.
SPEAKER_01: I'd make them more people analytics is a really fascinating topic.
SPEAKER_02: So it is and so the data is there, like, you know, say with or without us, you know, like that data is there.And you're right that like, you know, when you bring it all together, like, you know, how you bring it in clean, somebody can actually run those analytics on our platform and gain insights faster.But I would say we haven't really seen... People talk about it, but I think we haven't seen actual attempts where somebody is trying to actually build reporting like that using the data on our platform.
SPEAKER_01: The negative... interpretation of it is employee monitoring.So you can you can see employee monitoring, and then there's employee productivity.
SPEAKER_02: That's right.
SPEAKER_01: And you know, they they're just, if you're doing if you're running a call center, you really need to monitor it.Because people might say something stupid to a customer and somebody who's on a call center all day, they expect all of those interactions to be monitored.Now, a higher level knowledge worker, sales executive developer, they don't expect it.But they might very much want to be productive.And so yeah, I know people who run productivity software, and you have it on your iPhone, right?It tells you which were the most popular apps.
SPEAKER_03: Yeah.
SPEAKER_01: And I've looked at it a bunch of times.And I'm just like, I want this.I know, like six people in my organization want it. But I bet there's like 10 who are absolutely paranoid about that data being there.But what's important for people to understand is with AI, with a system like Lean or any other system, the byproduct is your collective work is going to be in a database somewhere, which means you can really study it and figure out what is this person doing in our organization?Do they need to be here or do they need a raise?Does this job need to be eliminated or do we need 10 more of these people?Or do we need to study this person?That people analytics to me... Yeah, it's incredible.
SPEAKER_02: Yeah, I think that's, that's really powerful.And actually, like, you know, but but I would say one more thing, which is, you know, today, you know, part of it is that, you know, you can have, like, you know, a few people in the company that could sort of do these analytics on an organization wide basis.But part of it is like, what about you yourself?Like, you know, like, you know, you can go and glean today and say, like, hey, tell me, you know, where I spent my time last week. I'm just going to tell you if you're meeting a lot.For example, I can ask in Glean how many hours of meetings did I have last week?It has access to that information.It's going to answer that back to you.Part of it is how can we help you as an individual have more insights into your own work? And so that's so we think more about that, like, you know, from that perspective.
And, and like, so like, one of like, one of the one of the very popular use cases are like, popular questions.And you know, the people asking Glean is like, every Monday morning, they will ask for, like, summarize, you know, all the work I did last week. because they need to share that information with their manager or with their team, posted in whatever their scrum notes.So you can do that in Glean and Glean will go through your Jira's and your GitHub's and your Slack conversations and sort of give you a really nice summary of what you did last week. Um, so, so there are the analytics, um, or summarization that, that you can actually bring to each individual for themselves.And, and like, you know, like, and you sort of start to like, you know, bubble up, like, you know, as a, as a manager of a team, you can ask the same question.What did my team do last week?And it'll actually do it.It will do it for you.Like as a manager, you'll, you'll be able to sort of get that summary, but only with information that you as the manager actually have access to.
So you could actually like, so if there was a employee doing something, you know, writing, you know, working on a dog that they're not shared yet with the team you know the manager won't get to see that uh but but yeah so yeah so there are use cases like those we are actually you know you know going from the angle of like helping each individual with their work uh and you know with their own sort of productivity uh we haven't seen that much of like you know the like what you mentioned which is that there could be yeah i mean i have my own little ways of doing it
SPEAKER_01: Sometimes I go into notion.
SPEAKER_03: Yeah.
SPEAKER_01: And it will show me I think I'm the administrator is why it shows it to me all of the changes in the database.
SPEAKER_03: Yeah.
SPEAKER_01: And I click on it.And I see Bianca, Andre, Heidi, coming up over and over again, the three people on my investment team.And I'm like, wow, they're super productive, productive inside of notion all day long.And I noticed that like, oh, wow, they're really taking good notes.And sometimes I'll just take a look at the document.Now all those documents are public, anybody can look at them.But it's really nice to see the pulse of the company, right? yeah and then there was this really cool reporting that i got just by opening up slacks admin to add somebody it'll show you how active each person was in the last 30 days yeah i just told you like how many messages they sent yeah how many they got and then how many days out of the last 30 they logged in yeah i was like shout out to these you know 30 of the company that logged in you know 28 29 or 30 out of 30 days like you can take a day off of I would never not check my slack.That to me would be crazy as the CEO.
SPEAKER_02: Yeah.That's the one I actually like a lot myself.I think it does tell you a lot about the company.Like, you know, when you sort of look at these data.
SPEAKER_01: Or then you look at the bottom and you're like, like one time I was like, oh my God, this person was logged in like 14 out of 30 days.And I was like, oh, they took two weeks off.They had a honeymoon or something. Totally fine.That's the time you want to turn it off, right?But then other times it's like, should that person have turned off their Slack for two weeks or whatever number of days?Might be time to have a conversation about that.Let me ask you about search.You were at Google.Yeah.
In five years, will people be doing search engine searches or will they be doing chat GPT searches or like, you know, chat interface searches?I'll take OpenAI out and I'll take Google out since you work there.Search engine versus chat interface and just having a conversation, which will be the majority of, you know, users searching for knowledge, which will be the majority in five or 10 years.
SPEAKER_02: Well, I think in five or 10 years, there won't be two different interfaces.There's only one. You know, because ultimately, like, you know, what are you doing?Like, you know, you have a question, you need an answer.Sometimes, you know, sometimes your question is about research, like, you know, you want to read a read a document actually, again, in response to like, what you're looking for.Sometimes, you know, looking for a one line, we'll just move to a chat interface.
SPEAKER_01: No, we won't be on this like 10 blue links.
SPEAKER_02: No, that's not what I said.What I'm saying is that there's only one interface, but that interface is adaptive, is rich.It takes what's the kind of question that's coming in and appropriately gives you the right answers to that.If you think about it, I think there isn't actually this dichotomy that we make of right now.Even in Google, for example, well before... well before like this whole generative answers and like the conversation interface that you could go and ask, you know, in Google, like five years, you know, from, you know, like five years, you know, back, you could ask the question, Hey, what's, what's the temperature like in, you know, what's the weather like in Los Angeles today?Sports for weather time.Yeah.
SPEAKER_01: So currency exchange stock ticker price, and it just gives you the answer, right?
SPEAKER_02: It will give you the answers.And it will actually also, it will also tell you like, you know, other interesting questions you may actually ask, like, so there was sort of this is that this has been a progression, right, you know, where I think the the search interfaces will sort of be like that, where, you know, you're going to understand the intent of the user. and what they're trying to look for.Sometimes you can actually give them like, you know, resources, links, you know, to go and, you know, they should go and read more details.Sometimes you're going to see summaries or like, you know, quick answers on it.
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So what does that do to the cost per click business model of the internet?Because right now, if a search engine, I'll just say any search engine, it could be any of the popular ones gives you doesn't answer your question.And it forces you to click some number of people will click the ads because the ads are generally answers to the question, you know, how much Does the latest Volvo have, you know, is there a convertible Volvo and it might be an ad for convertible Volvos or use convertible Volvos.But if we're just going to just answer people, hey, yeah, the Volvo, you know, made four different convertible models.There are one active and these are the three historical ones.Okay, I'm done.I don't click on any of the ads.So what's going to happen to the cost per click and model over the next 10 years as AI just answers everybody's questions?
SPEAKER_02: My view is that I don't think the model sort of disappeared.Already in Google, there's a concept of cost per click.Google always also would talk about cost per conversion.There are all these different degrees of how you're actually ultimately driving a sale.And you as the sort of facilitator in that, what is your cut of that transaction? So there's cost per impression, there's cost per click, there's cost per conversion.And so I think what will happen is that like in the future like for uh when you ask a question that has commercial intent and that is there are four different commercial parties you know that could actually you know all provide you with an answer they're going to compete and you know like you know the search engine may show like an answer coming from one of them and potentially like you know there is like further follow-ups you know that you take you to those sites and and then you get like you know higher higher
SPEAKER_01: So there could be a different type of funnel or modality for monetizing answers.So you give the answer about this Volvo.And at the end, it says, would you or, you know, follow up questions?Where can I buy a Volvo?Where's the closest Volvo dealer?Are there any incentives for buying a Volvo?Who can lease me a Volvo?Are they use Volvos and all those?Yeah, if you click them could include either cost per click links, or it could put you into a conversation. you know, that's the ultimate Hey, yeah, what kind of car are you looking for?
What's your budget?And then give that lead to Yeah, I local Volvo.
SPEAKER_02: Yeah, I think we're summarized it like, you know, very, very simply, I think, like, Google is getting paid, you know, because, you know, that's where users are going and seeking those answers.So as long as that stays, that means, you know, they wish they should be getting, you know, they're cut off, you know, that how long were you at Google for? I was there from like end of 2003 to 2014.Wow, you were there during the early days.
SPEAKER_01: You haven't been there for 10 years.So what do you think of all this, you know, brouhaha? this Donnybrook around the Gemini project, and all this woke dei stuff that was included in it.How does something like that happen at a big organization?And what do you think Google's chances are of kind of being able to release product faster?Like, how did it get to this point?Because it did seem like Google was so efficient in the period you were there, and just giving us products that solved our problems as consumers.And now it seems like they're doing something completely different.
SPEAKER_02: Well, my take on just the AI models first, like, you know, from Google is that I personally feel like, you know, they're actually in a strong position.Like, you know, whatever goes wrong in the model, like, you know, they get more attention than anybody else.But if you think about Gemini, like actually, you know, it works really well, like as an AI model. You know, they also have, I mean, like, you know, if you think about Google, like, you know, they're the best set of engineers, the most AI talent, like by far, even now, you know, they have the world's biggest data centers, they've got all the machines, they've got all the money.So I think the calls for like you know the the doom like you know scenario i think like it's in my opinion you know it's sort of like i think it's it's like it's fun for people to talk about but i think like i feel like the company is in a really strong position
SPEAKER_01: Yeah, you think they can still win?I think so.
SPEAKER_02: Yeah.
SPEAKER_01: Yeah, it just seems like maybe they've got maybe too much process.Like it used to move much faster, right?
SPEAKER_02: When you when it was a smaller org?Part of it is yes, like, you know, they need to organizationally make improvements.But part of it is also like, you know, the burden of like success.I mean, I think about Like, you know, they could not, like, as a company, you know, like, whose core business is to help, you know, people find information and correct information.Like, you know, like, they were sort of rightfully cautious about, like, not putting these models, you know, that hallucinate, like, in front of people.
SPEAKER_01: Yeah, giving giving the wrong answer is really anti Google's mission.And it does seem like this is why Apple hasn't released a ton of AI features is because they also like to have a lot of fit and finish and polish on their products.
SPEAKER_02: And so that's like an upstart, like, you know, they can they can launch whatever.And that is sort of like, you know, like, you know, in reality, like sort of what is like, you know, like, you know, cause them to be a little bit on this backseat.
SPEAKER_01: What do you think is going to win?Open source, Elon just open sourced Grok over the weekend.Obviously, Facebook and Meta, all their models are open source.Apple is working on an open source image editor generative product.And even OpenAI started as open and then went closed. Who's going to win long term?Who will have the best models and which will be the most market share?These closed models or open source models?
SPEAKER_02: I think in the future, majority of AI work is going to be based on open source models. I would say 80% of all AI inferencing or people building AI applications is going to be based on open domain models.Some of those will be fully open domain.Some of them could be open domain, which are supported by enterprises.That's really how the industry has progressed over the last two decades.I think it's just really hard to beat open source. on any technology like the momentum you get you know with it so that's sort of what I feel like you know is going to happen the from a who's going to win yeah
SPEAKER_01: How far ahead is OpenAI, if at all?Do you think OpenAI's 4.0 is much better, 10% better?How big is their lead, if you were to say, in the number of months or quarters?And then how soon before open source and everybody else catches up or exceeds them?Yeah.
SPEAKER_02: Yeah. So on text-based models, I think right now they are testing internally.It feels like they're still ahead, but the gap has been closing every quarter.It's actually not significant right now.It's not significant in the sense that I think now our team, for example, is continuously thinking about we need to actually use the smaller, faster models because they're faster, because they're cheaper.It's sort of like how you design.Sometimes if you make 10 requests and triangulate those interesting things, you can actually get a better response than making one costly request to a costly model. So it's already in that domain where it's not straightforward anymore to decide what's the right model.So the things are getting quite close.
SPEAKER_01: How do you define AGI?You must talk about this and think about it.General intelligence, what's the test that you put on it?I mean, obviously, we have, you know, all kinds of the classic tests, but what do you think would be a reasonable definition of artificial general intelligence that we could all agree on or you might agree on?
SPEAKER_02: Well, like, you know, in an enterprise, like, you know, when you feel like, you know, there is a person today, you know, they have a role, you know, to perform and that role is not completely taken over by an AI agent.And I think that's sort of what I, you know, what's our definition, like, you know, within our context.But I would actually also tell you, like, you know, we talk about big things and I think we're far behind, like, you know, in terms of, like, you know, where real technology today is. You know, people talk about having copilots.I, you know, I feel like, you know, that's, that's a big bar, like, you know, as a word, you know, to describe the technology that we actually have in front of us today.I mean, like, you know, this is really powerful, but there's a lot of work to be done.
SPEAKER_01: I mean, like, yeah, it's a copilot, we're in the copilot phase.
SPEAKER_02: And the next phase will be, I feel like it's not that we're not in the copilot phase, maybe just developers are. I think you are getting maybe 10% of what an assistant would do for you.Copilot is actually a lot more stronger than an assistant.You think about your own personal life, you can have an assistant, you can actually have somebody who can replace you as a copilot. I think AI technology is actually like not even at a place where they can do a better job than your EA.Interesting.
SPEAKER_01: So I would agree with that.Yeah, it feels like I like your definition.One of the employees at work gets replaced, and you don't know it's an AI.I like that.Yeah, pick a random person in your organization, replace them with an AI.And when you talk to them in Slack, or you talk to them in, you know, GitHub, or whatever, you talk to them in a Google Doc, Yeah, you can't tell the difference.That would be a pretty good one.Yeah.It feels like we're making steady progress there.
But yeah, it feels like we're in the co-pilot era.But yeah, you're right.I never thought about it that way.You wouldn't hand them control of the plane right now.You wouldn't go to the bathroom and let them fly the plane.That's right.She'd be like, I'm going to stay here and watch you fly the plane.I'm not quite sure.I trust you.
SPEAKER_02: But at the same time, there's real value.I think even with Clean, we want to be that assistant for everybody who works.And I think we're bringing a great deal of assistance, but it's a long road we're going to be working on.
SPEAKER_01: How do you charge for it?Is it per seat?Is it by data source?Yeah, per seat. Per seat, $10 a month or something, $20 a month?
SPEAKER_02: Yeah, a little bit more.A little more?
SPEAKER_01: Okay, yum, yum.
SPEAKER_02: But yeah, that's the model.Got it.Yeah.
SPEAKER_01: Got it.So if a thousand people, a couple of hundred dollars a year per person, it's probably, and that's what you're going after.Midsize organizations need this.It can't be like a 50 person company, maybe not worth the, the choose ain't worth the squeeze.Or are you going after the midsize?
SPEAKER_02: We are focusing on companies from like a few hundred people all the way to the largest enterprises of the world.The need is quite universal, but like from a focus perspective, like, you know, we are like majority of our business is actually an enterprise.
SPEAKER_01: It takes a while to get all these services into the database, right?It's got to take a couple of weeks or months to tweak everything and get it all plugged in, right?
SPEAKER_02: No, Glean is actually very turnkey.That's actually one of the big requirements for when we started our company was we can bring Glean to, let's say, a 2,000-person enterprise and it's up and running within a day.Oh, that's pretty great.Yeah, because I think... the I think one thing that helped in that has helped is that like, you know, like the new like, you know, SaaS based IT environments are actually quite accessible.And you can be up and running pretty quickly.
SPEAKER_01: All right, listen, everybody, check out glean you have glean.com which should mean yeah, glean.com.All right, good domain.Pretty great domains a million dollar, maybe half million dollar domain right there in my estimation, it's in the dictionary.Great job, and everybody check out glean and we'll see you all next time.Bye bye.Thank you so much.
