A developer's toolkit for SOTA AI

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Welcome to another edition of the Practical AI Podcast. My name is Chris Benson. I'm your co host today. Normally we would have Daniel Whitenack joining us, but Daniel has just gotten off a plane. He flew halfway around the world and we decided to give him a break from today. I would, I would be. He was more lucid than I would be under the same situation. Today I wanted to dive right in. We have a super cool topic. It is not dissimilar from some of the other general things we've been talking about, but I have two guests today. I'd like to introduce Varun, who is the CEO and co founder of Codium, and Anjul, who is the lead of their enterprise and partnership. Welcome to the show guys.

Thanks for having us.

Thanks for having us, Chris.

Yeah, you're welcome. Really interested in learning more about Codeium. When Daniel lined you guys up, he's like, Chris, he sent me this thing saying, you got to look at this, this is really cool and everything. And I'm like, get him on the show. He's like, I'm already doing that. So really glad to have you guys on. And he's going to be bumming that he missed the conversation because he was pretty excited about it. And so I guess I wanted to, before we even dive into Codium and the problems it's trying to solve and such, if you guys could each just tell me a little bit about how you found yourself arriving at this moment, kind of a little bit about your background, how you got into AI and how, how this became the thing. Varun, if you want to kick off and then Anshul afterwards.

So maybe I can get started. Actually starts in 2017. I started working at this company called Neuro that does autonomous goods delivery. So it's an AV company there. I sort of worked on large scale offline deep learning workloads. So as you can imagine, an autonomous vehicle company needs to run large scale simulation. They need to basically be able to test their ML models at scale before they can actually deploy them on a car. And sort of in 2021 I left neuro and started Exifunction. Which is the company that is building out this product. Codium and Exafunction started out building GPU virtualization software. So you can imagine for these large scale, deep learning applications, one big problem is GPUs are scarce, they're expensive and also hard to program. And sort of what exifunction started building was solutions and software to make it so that applications that ran on GPUs were more effectively using the GPU hardware. And we realized that our software with Exifunction was best applicable to generative AI tech and started building out Codeium around a year ago.

Very cool. And before I dive in, because I have several questions for you, but I want to give Anshul a chance to introduce himself here. Go ahead, Anshul.

Surprisingly, my story is actually quite similar. I was also working at Neuro, so Brune and I used to work together back in the day. I was not actually working on the ML infrastructure side of things. That was something that Varun had hands on. But I decided to kind of also join the team at Exo Function. And I think, yeah, as Rune mentioned about a year ago, I think we noticed there was like, I think three things kind of happened at the same time that we noticed that led us to codeian. I think the first one is that we're engineers. All of us here are engineers and we had all tried the GitHub copilots and all these cool AI tools for code in their beta and we're like, wow, this is absolutely going to be the future of software development. But at the same time it's like still scratching the surface of potentially everything that we do as engineers. So I think like, number one, I think that we realized and then number two was, you know, talking to a lot of our friends at, you know, these like bigger companies or anything like that. A lot of them were just saying like, oh yeah, it's cool, I've tried it for my personal project, but I can't use it at work. Right. My work's not allowing me to use that. So that was like the second thing we heard. And the third thing was exactly what Varun alluded to. We were building like ML infrastructure at scale for really large workloads. Like when this entire generative AI wave started coming, we're like, wow, we're actually kind of sitting on the perfect infrastructure for this. So I think all those three things kind of combined together for us to be like, do you know what, let's build out an application ourselves and build an application that we as engineers are Customers ourselves. Right. And that ended up becoming codium.

As you were getting into doing GPU software, what was in general, some of the challenges that you were seeing, you know, with Nvidia has their various software supporting things like that. Clearly you saw that there was a need for something beyond that. Can you talk a little bit about just the layout that you saw in the, you know, in the environment before you got to all the generative stuff and the fact that you had infrastructure? What, what positioned you for that and what was the thing that you decided that you needed to address?

Maybe I can take it a step back of why these GPU workloads are just a little bit annoying compared to CPU workloads.

Okay.

One of the really sort of unique things about GPUs is that unlike CPUs, it's kind of tricky to virtualize. One common thing that we have with CPUs is you can put a bunch of containers on a single VM and then you can kind of make use of the CPU compute effectively. Right. You can basically dump 10 applications onto a CPU and it's perfectly fine for GPU. It's a little bit more messy because the GPU doesn't have a ton of memory. So you can't just load up infinitely many models on there. Like, let's imagine you have a GPU with 16 gigs of memory, and each of these models takes like 10 gigs. You can't really even put two applications on there. So then that already becomes a big issue. And that's sort of what a lot of these large deep learning workloads were struggling with. So when I was at Neuro, one big problem we had was we had around like tens of models, but we had these workloads that needed hundreds of GPUs, some of them even thousands of GPUs, and we struggled to basically make it so that we were even able to use the hardware properly. And then you could imagine the complexity then stacks with. Now we're in a state where companies have trouble even getting access to 10 GPUs because of Nvidia sort of scarcity issues. And then also the cost of a GPU is not like a CPU, it's significantly more expensive. The cost of a single H100 chip is well over 30 grand. So these aren't like very cheap chips. So there was a big need at the time to figure out how do we leverage the hardware properly and sort of that's what we had to build software for.

And just to clarify for me was that while you were still At Neuro or was that after you started Exafunction?

Yeah, so while I was at Neuro, we sort of worked through or I sort of led a team that sort of built software that kind of fixed these problems. But Exofunction was focused on generically. How do we make sure deep learning based applications could best leverage GPUs? That's sort of what we started out building, actually. And then Codeium came out from that, actually.

Gotcha. Tell me a little bit about as you have been right in the middle of this progression, just to frame it for a second. If you look at the last couple of years in particular, and the pace of change has been so much. And so you were right there starting at Neuro and then creating Exofunction, seeing some of the challenges. Could you talk a little bit about how the industry was, was evolving and changing as you were seeing it, so that we can get a sense of kind of how you moved toward Codium, you know, to give a little bit of the history instead of just starting from where that is. Can you talk a little bit about, you know, the itches that you were scratching and why it led that direction. What did this AI industry look like to you?

Yeah, so when we started, like you can just imagine everything was a lot more smaller scale. Right. The hyperscalers or the cloud providers just didn't have nearly as much GPUs. Like, if you ask them, like what fraction of cloud spend is GPU spend, it was probably like very small single digit percentage points, maybe even less than that at the time. So this is like a very small workload for them. When we sort of started, both me and Ocho started at neuro in like 2018. But then over time this grew a ton. Like we could see it from the training workloads. These were no longer like even single node training workloads. Like back in the day, a single GPU node that had maybe like 8v1 hundreds or something was like considered a lot of computer. And suddenly now we were able to witness the fact that this was slowly becoming eight A100 nodes. And then more than eight of these nodes were necessary then even to train these models. And similarly, to prove out that these models were capable in an actual production setting, you needed to run offline testing at massive scales on the order of 5,000 to 10,000 T4s scales, which is kind of incredible in terms of raw flops. So we were able to see this hockey stick happen in front of us. And then that's sort of what made us Want to start AXO Function in the first place? We realized that there were going to be large deep learning workloads. One interesting fact is for us, like for just the exo function GPU virtualization software that we ended up selling to enterprises, we ended UP managing over 10,000 GPUs on GCP in a single GCP region. So we ended up managing more than 20%. And we realized that, that hey, this was only going to keep growing. Like when we talked to the cloud providers, they were only going to keep growing the number of GPUs. And, and we realized, I guess the interesting thing was in the future, Generative AI was going to be potentially the largest GPU workload though that was the big thing we realized was GPT3 came out, which was I guess in, in 2021 now.

Gotcha. So, but you had already, at that point, were you already into EXA function? Had it already started at that point?

Yeah, it had already started. And we were sort of selling GPU virtualization software to large autonomous vehicle and robotics companies.

Gotcha. And so basically, if I'm understanding correctly, the whole Generative tsunami just kind of landed on you when you were already sitting in that space doing GPU virtualization already. So you're, you just managed to land right in front of the wave, it sounds like.

Yeah. So we started working on codeum like maybe four or five months ago, before ChatGPT. It was interesting just because we realized that an application like GitHub Copilot was going to be one of the largest GPU workloads, period. Like, I don't know if you've, you've probably tried the product out. It's like every time you do a key press, you're going out to the cloud and doing trillions of computations. Right. So it's like a massive workload. And we had like, as Anshul said, the perfect infrastructure to basically run this at enormous scale. Not to mention we were in love with the product from day one. Like we were early users of the product the moment it came out in 2021.

Very cool. So as Generative is starting to take off kind of with ChatGPT hitting the world and really changing things quite rapidly, you know, I think people are still shocked at how fast things have moved. You had started Codeium already. What kind of synergy were you starting to see there in terms of knowing that you have one of presumably many GPTs coming and other similar generative models, you had just gotten into Codeium. Can you talk a little bit about what that was. And what were you putting together in your minds to recognize the opportunity that it was?

Yeah. So I think one of the great things about entire ChatGPT wave is that, you know, everyone was using it. This is a thing where, like, literally every individual is using AI, and so it helped us in general. Right. You know, like a big wave, like, raises all ships kind of thing. You know, it really helped us. We weren't really going out and telling people, like, hey, a tool like Codium can help productivity. Because that was kind of just now assumed by everybody, like, oh, yeah, if I do any kind of, you know, knowledge work, then there's potential for AI to help. Right. And I think so from that sense, when the star, like, you know, chatgpt wave really came about, that overall kind of just like helped us in terms of convincing people to even try the product. The other thing that we, we recognize is that we were positioning ourselves very specifically from the beginning. Right. When it comes to code, code is like, actually a very interesting modality. Right. It's not like your Standard, you know, ChatGPT where you have a, you know, long context that a user puts in and then it produces context coming out. Code is interesting in the sense that, as mentioned, it's an autocomplete that's like a passive AI rather than an AI that you're actually instructing the model to do something. It's happening every keystroke. So it has to be a relatively smaller model. You have these hundreds of billions of parameter models being used. It has to be relatively low latency. And then code itself is interesting. If you have a cursor in the middle of a code block, the context both before and after your cursor really matters. It's not just what comes before. So there's all these interesting kind of situational kind of constraints about code that you put all these things together and we realize that, okay, all these chatgpt waves and conversational AIs are happening. That's great, but we're still not going to be rolled over by that because we're kind of focusing on a very specific application and modality of LLMs that was pretty unique in many ways.

Could you take a moment as we're diving into codeium and Generative AI and its unique capabilities there and just differentiate a little bit about. For those, you know, so many people have tried Copilot, and so it's kind of inevitable that you're going to get that comparison to some degree. Can you talk a little bit about what Copilot's not doing for generative AI or how you're approaching it that allows you to show people this as a better way forward from your perspective.

I mean, we have tons of respect for it for the Copilot team. I just want to start with that. Right. I mean, as Marin said, we were all early users of it.

Definitely not putting you into conflict with them just as a starting point for people.

Absolutely. Yeah, I think. But the way we kind of view this, and I kind of like alluded to this earlier, is that you're writing brand new code. Right. With Autocomplete is really just one small task that we do as engineers. Right. We refactor code, we ask for help, we write documentation, we do PR reviews. And so kind of our general approach has always been let's try to build an AI toolkit rather than AI autocomplete tool.

Got it.

So we can get more into this, into the weeds here. But like Autocomplete is just one of our functionalities that we provide. We provide like an in IDE chat, so things like ChatGPT, except integrated with the IDE natural language search over your code base using embeddings and vector stores in the background. So we're really trying to expand how can we address the entire software development life cycle. So I think that's probably the most obvious difference with a tool like Copilot from an individual developer point of view. But then the other thing which really kind of builds off of all the infrastructure that Varun was mentioning earlier is that we were already deploying ML infrastructure in our previous customers, private clouds. We already had all this expertise of how can we take actual ML infrared, deploy it for a customer in a way that they can fully trust the solution because we're not getting any of their data. And so another really big differentiator for us was like, okay, I think this might actually be a tool that enterprises can use confidently and safely because we have the infrastructure to do the deployment in a manner that they would be open to using. So I think that was like the other differentiator when it came specifically to enterprises. But we can dive more into that later.

No, that sounds good. I want you to connect one more thing for me. Going from being able to deploy the infrastructure and helping your customers in that way to Codeium as a tool. What's the leap there that got you from one to the other? How did you get from infra focused to Codeium focused?

Oh yeah, I think we had to do like a full like 180 when we started. We like went from full like infra service company to like let's like create a product for consumers. Right. Like it was a full 180 in terms of product.

A pivot.

Yeah, full in some degrees a pivot because we knew that, you know, eventually, okay, we'll Deploy to customers VPCs that sound great, but like if we're going to ship something to a customer, we had to be like super confident that it was a product that would work well. Right. Because we're getting no feedback from their developers. And so we actually first focused for the first six or so months of Codium, just building out an individual tier. Any developer can go try it, we can see how they like it, try our new capabilities, get feedback from an actual community, do all these community building things that we hadn't really done as infra as a service company. But that was a really huge focus for us. And we've grown our actual Codeium individual plan to over 100,000 active developers using us for many hours a day. Because you code for that long, if you're a developer, that's plenty of feedback to us. Plenty of people actually using the tool telling us like, yeah, this is good, this isn't good. Oh, you tried pushing a new model, that's worse. All those things we actually learned so that we can get a product that's good. So that was like the, I guess the intermediate period. Right. Really learning from actual developers what is a good product and what is not. And I think that's always going to be a key kind of part of our development cycle.

You're coming into this with this rich knowledge and infrastructure for customers. That's a huge area of expertise. It's an area of expertise that even though you were moving forward into the kind of the Codium era, if you will, in my words, that is a skill set and level of expertise that very few organizations have deeply that you would have had there. How did that inform you in terms of Codium and differentiation against whether it be Copilot or other tools that are out there or just developers throwing things into ChatGPT. What did that background give you that gave you that differentiation in the marketplace?

Yeah, so I think when we started, the thing we started with is like no one cares if we have better infrastructure once you're a product. Like if we have better infrastructure, that's great. But if that makes a, a product that's the same, no one should care.

They just assume that you should.

Yeah. So what we started with is we set a very high bar for ourselves. Codium is an entirely free product. So for the individual user, it's something that they can install and use immediately for free. There are unlimited, there's no limits at all. So when it comes to autocomplete, you can use it as much as you want. And this has, by the way, forced us to do things where our infrastructure is as efficient as possible. Just to give you a sense of the numbers we're talking about here, we process over 10 billion tokens of code a day. That might sound like a large number, that's like over a billion lines of code a day that we process for our own developers. We're forced to do this entirely for free. And then on top of that, we probably have one of the world's largest chat applications also because it's in IDE as well. And all of this put together has allowed us to build a very, very scalable piece of infrastructure, such that we're the largest users of our own product. We are the largest user of our own product. We learn the most from our users, and we can then take those learnings and deploy in a very cost effective, very efficient and optimized way to our own enterprise users. It's one of those things where we force ourselves to learn a lot from an individual plant and then take all those learnings and actually bring them over to the enterprise. And a lot of the learnings we were only able to make because we place like very, I would say, like annoying infrastructure constraints on ourselves by saying, hey, you guys got to do this entirely for free, basically. And we're committed to building Codeium is going to be a free product forever, actually. The individual plan will always be free. And it's one of those things where our users are just always like, how are these guys even doing it? Like, what are they even doing to make this happen? And most of our users, by the way, are users that have turned off of Copilot. We have spent very little, if not anything on marketing. So it's just one of those things where our users are like, how do we make this free? We take the approach of we think some of the best products in the world are free. Like products at Google, right? They're entirely free. Google doesn't tell you all the time that they have the best infrastructure, but they do have the best infrastructure. It just so happens to be the case that that shows itself off in the best product. And we could talk a little bit more about how we take our sort of focus on infrastructure and make a much better enterprise product as well, but that's the way we sort of look at it, it's like how do we deliver materially better experiences with our infrastructure? And our users shouldn't care that we actually did that.

You've brought it up. You got to go there now, man. Go ahead and dive right into it.

I guess one of the interesting things just going to how we run one of the world's largest LLM applications, what that sort of focus forced us to do is given a single piece of compute, like let's say a single node or a single box of GPUs, we can host the most number of users on there. So let's say a large company comes to us, they can be confident that whether they're on prem or they're in vpc, we can give them a solution where the cost of the hardware is not going to dominate the cost of the software itself. Because right now there's kind of this misunderstanding that the GPUs are really expensive, which is true, they are. But the trade off is they have a lot of compute like modern GPUs, like a 1/ hundreds can do 300 teraflops of compute, which is like some ungodly number, right? Like that's a crazy number compared to what a modern CPU can do. And we can leverage that the best. And we've sort of been forced to do that like you know, if we didn't do that properly, we'd have outages with our service all the time. Because of that, enterprises trust us to be like the best solution to run in their own tenant in an air gapped way, which is fantastic because that's like the way that we can build the most trust and deploy these pieces of technology to them the most effectively because they don't want to ship their code outside of the company. Anshul can talk a little bit more about how we leverage things like fine tuning as well. That's like a purely infrastructure problem that's very unique to us versus like any other company as well. Anshul, do you want to sort of take that?

I mean. Yes, I think, you know, as Vernon said, there's a lot of things that we do from like the individual infrastructure point of view so that we can do crazy things like make it all free for all of our individual users. But once we actually self host, there's actually a lot of things that you can do that you know, just any other tool can't do without being self hosted. And one of the ones that everyone just mentioned is personalization. If you're fully hosted in a company's tenant, you can use all of their knowledge bases to create a substantially better product. I think the way we generally think about it is that you have a generic model that's good, it's learned from trillions of tokens of code in the public corpus. But if you think about any individual company, they have themselves hundreds of millions of tokens of code that has never seen the light of day. And that's actually the code that's the most relevant for them if they want to write any new code. Think of all the internal syntax semantics, utility functions, libraries, DSLs, whatever it might be. And a model like a copilot or a codeium by the nature of it having to be low latency, can only take about 150 or so lines of code as context. So this is not like one of those ChatGPTs or GPT4s where you're putting in files and files of context. It's really small what you can put in. And so there's really no way for a single inference to have full context of your code base without actually fine tuning the base model that we shipped to them on all of their local code. And so we've actually done a bunch of studies and we're like on how this actually massively reduces hallucinations and all these other things that you always hear coming up with LLMs, but things like this, things like providing more in depth analytics, all of these things actually come up by being self hosted. And as Roon mentioned, these are all at the core, to some degree an infra problem. How do you actually do fine tuning locally in a company's tenant? That's actually an infra problem that we're happy to talk more about, but maybe I'll pass it back to you, Chris.

Actually I'm about to ask a follow up about that because you've got me really thinking about some of the use cases in my own life on that. And so with the self hosting model and you're able to now kind of like, you know, OpenAI. I said, you know, with ChatGPT4 there's only so far we're going to go because we've kind of, we've used the public corpus of knowledge out there on the Internet, you know, so there's only so much more vertical scaling you can do on the model learning. And so, you know, you're touching on the fact that there's so much hidden IP in code, hidden information and code that is of huge value, particularly to the company that it's in because it's representing their business model and the way their business has evolved over time. And so if I'm under understanding you correctly, you're basically saying that your solution can take advantage of that on their behalf and really, really hone against it. What are some of the limits on privacy? Are they able to do that? Because that's a big topic. We've actually talked about it on the show before about, you know, in this generative AI age with IP concerns and privacy concerns and you know, getting the lawyers involved, are you able to do the training on their site and keep it to the customer entirely or do they have to let their IP out and stuff? How do you approach that, that problem?

Yeah, I mean so one of just the answer to any question of like does any IP leave coding for enterprises? The answer is always no. So in pretty much every part of the system, like our guarantee is to actually be able to deploy this whole thing fully air gapped. We've even deployed in places like you know, AWS GovCloud, which is like entirely, you know, doesn't even have connection to the Internet kind of scenario. So nothing ever leaves there to address some of the points you brought up there, Chris. Yeah, I mean we're not the only ones who are saying like oh no, the data that a company has privately is super important and it's potentially even more important than the size of the model. I think a good example of this is actually meta. Instead of using a GitHub copilot or any generic system, they decided in I guess classic meta fashion to train their own autocomplete model internally using all of their code. And they actually published a paper I think a few weeks back and their model was like in terms of size, I think 1.3 billion parameters small in respect to the LLM world and it just massively outperformed GitHub Copilot on pretty much every task. There's some corroborating evidence to what we're saying about fine tuning, that doing this actually does lead to materially better performances for the user in question. Now does that meta model going to be good for everyone else? It could probably not, but that's also not the whole point. And in terms of being able to fine tune locally, yeah, we're able to do this completely locally. And again it comes down to scale of data. Our base model has been trained on trillions of tokens of code. That's a lot. That's why we need this multi node GPU setup to do all this training. But an actual company, if they have say even 10 million lines of code, that's about 100 million or so tokens. There's a huge order of magnitude difference still between this pre training and the fine tuning, which is why we can do this kind of locally, surprisingly, whichever hardware they choose to provision for serving their developers. So again, this comes to some of our ML infra background and all the stuff that we know how to do, we actually can do fine tuning and inferences on that same piece of hardware. So we don't actually ask companies to provision more hardware. And even more critically, we are able to do fine tuning during any idle time of that gpu. So whenever that GPU is not being used to perform an inference, it's actually doing back prop steps to continuously improve the model. Fine tuning is just one aspect of a larger personalization system. But we've instrumented all of this on hardware, using our inforoots to actually create a system that is relatively easy to manage. There's not a crazy amount of overhead for any company to to manage or use codeium but still get the maximum possible wins from these AI tools.

Okay, so that is super cool. And you mentioned things like govcloud, which I have actually worked in because in my day job quite a bit and I can think of a whole bunch of other use cases for me personally, which begs the question about kind of going back for a moment because we are practical AI and we like to always give some practical routes for people into that. So if we're going to go back toward the beginning of the conversation for a moment, and we have some folks that are listening to this right now and they've been using Copilot for a while, they're probably putting code into ChatGPT and trying to accelerate there with, with varying degrees of success. They've been experimenting with Bard and Bard's gotten better on code lately obviously. And so, so many people that I talk to are still very frustrated with kind of the workflow of the whole thing and recognizing that there are these, you've outlined these differentiators, you know, from CO and other competition out there in a friendly competition kind of way. Talk a little bit about some of the specific generative AI use cases that would be good if someone was in that position where they're like, yeah, I'm using the stuff, but I'm not, I'm a little bit frustrated with it, I don't have it down. And if they were to give Codium that chance and dive in on it, can you give me several kind of layout the use cases on what is what are they going to get when they move in? From a very practical, like, for me now, as the coder perspective, what will that look like? What are they bonusing? And maybe give me a couple of different ones because I'm really, I'm really curious and selfishly, I'm probably going to go try each of these that you're telling me. So I'm scratching my own itch by asking the question.

I think you pointed out like, yeah, workflows and the user experience for a lot of AI tools, like, everyone's still kind of trying to figure it out, right? We're still in very early days of these AI applications and this is our learnings of trying to become a product company. We're actually taking like the UX quite seriously. Right. And this is actually what the individual plan is great to get feedback on, I think very, you know, concretely. I think a lot of people have that frustration of like having to copy a code block over to ChatGPT, write out a full prompt and like, you know, remember the exact prompt that they typed in before that gave them a good result, and then copying the answers back in and then making modifications like that. Workflow is clearly kind of broken. So when we actually built our chat functionality into the idea, we're like, okay, what are all the parts here that can get totally streamlined? And so we actually did things like on top of every function block, there's little code lenses that are just these small buttons that someone can click explain this function and it'll automatically pull in all that relevant context, open up in the window. You're not copying anything over and it's like writing it out in human text. Or if you say refactor a function or add doc strings or write a unit test, these are all just small little buttons or preset prompts that you can just then click. It'll do this generation on the side. And then we even have a way of clicking like apply diff. And because we know where we pull the context in, we can apply diff right back into the context. Right? And so you're not copying things back and trying to resolve merge conflicts. All of these things are done kind of automatically. So there's a lot of really cool things you can actually do when you start bringing these things into the IDE where developers are. And we spend a lot of time really thinking, as you said, from a workflow point of view, how do you make this like super smooth varun?

Could you talk a little bit about maybe some specific tasks that you're seeing people doing. When we talk about generative and it's expanded and you know, from LLMs and we're, you know, we're doing things in video, we're doing things in natural language, all of the different modalities are gradually being addressed with these different models and different tools that are being built around it. Could you talk a little bit about, you know, what are people trying to code right now? What specifically is Codium helping them? Like what? Not just about Codeium, but the actual use cases themselves. So they go, ah, I can see a path forward. I can, I can go do that. I know how to generate this or that or the other with Generative AI Encodium. Can you talk a little bit about those at something of a specific level?

So interestingly, just a little bit about multimodality, I think we're maybe a little bit far from leveraging, I guess, other modes beyond text for code. I think maybe that that'll happen, but I think there's not enough evidence right now yet for Autocomplete just to be open about the sort of the functionality we have. We have Autocomplete, we have Search and we have Code base Aware chat, right? So for we recognize right now that of the usage, autocomplete accounts for more than 90 to 95% of the usage of the product. It's because chatting is not something people do like even every day potentially they might open it up once every couple days. But Autocomplete is something that's like always on very passively helpful and people get the most value out of it, which is kind of counterintuitive. I think people don't recognize that immediately. But when people are doing Autocomplete, we've recognized there's like two modalities, right? Of the way people type code. There's a modality of accelerating the developer which is like, hey, I kind of know what I'm going to type and I just want to tab complete the result. And then there is also an exploration phase which is like I don't even know what I'm trying to do. Based on that, I write a comment. This is like a classic thing where like my behavior writing code has materially changed because of tools like Codium, where I'll write a comment and I kind of just hope and pray that it pulls in the right context so that it gives me the best generation possible. So in my mind for the acceleration case, Codium is like very helpful, right? It can like auto complete a bunch of code, but to make the exploration case that's where the true magical moment comes in where I had like no clue like how I was going to use a bunch of these APIs. And that's sort of what we're focused on trying to make really better, whether that be in chat as well as with Autocomplete. How do we make it so that we can build the most knowledgeable AI that is maximally helpful and also minimally just like annoying. The interesting thing about Codium as a product or these Autocomplete products is they get a little bit of getting used to. But even despite the fact that they write wrong things, it's not very annoying because you can very easily just say, I don't want this completion. Or it didn't like write an entire file out and you need to go and correct a bunch of functions. It was like a couple lines or maybe like 10 lines of code. You can very easily validate that it's correct. Right. That comes back to then what Anshul was saying, which is how do we make sure we can provide always the maximally helpful sort of AI agent? The answer is have the best context possible. And a couple nitty gritty details we do is currently our context and we'll write a blog post about this is double what Copilot is. We allow double the amount of context for Autocomplete than what they do. The second thing is we're able to pull context throughout the code base. And this is actually that same piece of technology that is pulling context throughout the code base through search and all these other functionalities is getting used as part of chat for code base Aware chat, which is something that Copilot doesn't even have today yet. The third piece is finally for a large enterprise, is how do we make it so that these models actually semantically understand your code, which is where fine tuning comes in. It's like for us, context gets us a lot of the way, but it doesn't get us all the way because you can just imagine, even with double the context, let's say we can pass in a thousand lines of code. For a company with 10 million lines of code, we're scratching four orders of magnitude less code than the company actually has. So this is where our vision is like. We want to continually ramp up the amount of knowledge these models have and the ways in which they can be helpful. I don't know if that answered the question there.

It did, actually. Your acceleration versus exploration analogy, that was for me personally, different people get different things. That really clarified for me where I might be using Copilot or where I would go and use Codeum on that, because I do struggle on the exploration side myself. It's a lot easier on the acceleration. Yeah. Into the line and the line, you know, and crank through that fast, which I've been able to do with these other tools. But I have struggled on the exploration side because I kind of want to do a thing and I'm kind of trying to figure it out, and I'm just going to kind of see where my fingers lead on that. And having that ability to support that in the way you described that gave me a very clear understanding from my standpoint. So I'd like to ask each of you where this is going, both in the large and in your specific concern with Codium. You know, things have never moved faster than they're moving right now in terms of how far fast these technologies are progressing. And Daniel and I have a habit. We were commenting on our last episode about this. We have a habit of saying, yeah, we recently mentioned this thing and that we'd get to it, but then we turn around and we end up talking about that we just got there way faster than we ever anticipated with the speed of generative AI. And, you know, you're already creating these amazing tools and stuff like that, and you're having to stay out front. Where's your brain taking you at night? You know, when you. When you stop and you chill out and have a glass of wine or whatever you do, and you're kind of just pondering what does the future look like? And I'd like to know both from your own specific personal standpoints in terms of your product and that, but just the generative AI world in general, how do you see it going forward? I'd love your insights.

Yeah, I think the classic question and in the grand scheme of things is like, oh, my God, is like, generative AI just going to, like, totally get rid of my job or completely, like, invalidate. I think for us, we will be the first people to say that we do think AI will just be like the next step in a series of. At least in code, a series of tools that have had made developers more productive, that have led them to be able to focus on more kind of interesting parts of software development and be an assistant. Right. All these tools are called AI assistant tools, I think, for a reason. We're definitely not at a place yet, I don't think, for a while where there isn't going to be a human in the loop, in control, guiding the AI and what to do so from that kind of respect. The doomsday scenario I don't want to speak for, but I think we're pretty far from that mentality. But we do think, I think we wouldn't have gotten into Codeium if we didn't genuinely think that there was just so many things that we do as a day to day as engineers that are just a little frustrating, boring, take us out of the flow state, slow us down. Those all seem like very prime, ripe things to try to address with AI. Right. And I think that's kind of our general goal. Right. I think there's a lot more capabilities to build. Right. I don't think search chat, these are going to be the last, I guess like building blocks that we build. We have more capabilities coming up that we're super excited about. But yeah, it's also like, you know, going to be a thing where as you said, this is moving super quickly. Right. Like we have like research, open source, like applications all developing at the same time at breakneck speed. And so I think part of what we're also looking forward to is like how can we also just educate at least software developers on the best way to use AI tools, how to best make the most use of it so that they are part of the wave and that they also can get a lot of value.

Well said, Varun.

Yeah, maybe if I was to just say you were asking me what the big worry is. For me the big worry is there's going to be a lot of exciting new demos that people end up building. And obviously for us as a company we need to make strategic bets on like, hey, this is a worthwhile thing for us to invest in. For instance, I think a couple months ago there was an entire craze on agents being able to write like entire pieces of code for you and all these other things. For us though, we had lots of enterprise companies that were sort of using the product at the time and recognized that the technology just wasn't there yet. Right. Like take a code base that's like 100 million lines of code or 10 million lines of code, it's going to be hard for you to write. C, that's like five files that compiles perfectly and then also like uses all the other libraries. When you have context that's like, you know, five files. It's not going to be the easiest problem. And I think that's maybe an example. But for us we've currently, I would say just a pat on the back over the last eight months iterated like significantly faster than every other company in this space, just in terms of the functionality. But we need to make strategic bets on what the next thing to sort of work on is at any given point. And we need to be very careful about, like, hey, this is like a very exciting area, but is it, like, actually useful to our users?

Right?

Like, is it actually useful in that? Hey, like, maybe we could do something where a great example is given a pr, we generate a summary. And I think Copilot has tried building something like this. And we tried using the product that Copilot had, and it was just wrong a lot of the times. And I think that would have been an interesting idea for us to pursue and keep trying to make work. But then there is, like, diminishing returns. And I think Anshul and I have seen this very clearly in autonomous vehicles, where we had a piece of technology that was kind of just not there yet. Like, it needs a couple more breakthroughs of machine learning to kind of get there. And the idea of building it five years in advance. Right, you shouldn't be doing that. You just 100% shouldn't be building a tool when the technology just isn't there yet. And that is something that keeps me up at night, is like, what are the next things we need to build? While keeping in mind of this is what the technological capability set is like today, if that makes sense.

It does. And it's a very practical AI perspective, if you will. So very fitting final words for the show today. Well, Varun and Anshul, thank you very, very much for coming on the show. It's fascinating. I got a lot of insight, a lot of new things to go explore from what you just taught me, and I appreciate your time. Thank you for coming on.

Thanks for having us.

Thanks a lot, Chris.

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