Knowledge Systems: The New GTM Stack — Jeffrey Wang, Exa

Exa, and why engineers should care about go to market

00:12Hey everybody, I'm Jeff. I guess I was introduced, but I'm the co-founder of Exa and uh today going to give a talk on turning go to market into an AI engineering problem in the spirit of this uh AI engineering uh fair. And just a quick show of hands just to like understand the audience like raise your hand if you're technical.

00:30Okay, great. Okay, so I kind of uh oriented this talk around like go to market as presented to to engineers. So I'm happy I did that. Cool. So first just to like ground the ground like what what EXA is because it's sort of relevant inside of this uh presentation.

00:47Ex is a XA is a search engine for agents. Uh think like agents are really smart but they don't have access to the web. We're like this web MCP web tool that agents can access. We power cursor, we power cognition, we power a lot of the ecosystem at this point.

00:54And uh before we start, I also just want to like talk about, you know, especially as a technical audience, like why should you even care? Like why should you care about go to market? I guess this audience cares about go to market because you chose to go to this uh go to market talk.

01:11But I think there's this like funny narrative right now which is like people are like oh like product is the only thing that matters or distribution is the only thing that matters. And there's all sort of like all sorts of like Twitter flame wars like like oh is Cluey going to succeed because they're really good at distribution but they're like what what the heck is their product?

01:26And then and other people are like oh the like the the product needs to be super good because agents um you know agents shop for the product so they'll shop the for the best product. And so my view and my experience in the last few years is that you just kind of have to do both.

01:34Like I think you have to get product right and you have to get go to market right. Like you got to build the thing it's got to be good and then you got to get it into people's hands. If you don't do both things, then you don't have a company.

01:51Uh, so that's kind of my view on the matter. And I would say like a really funny thing also is like as a technical person when you start a company or you start some sort of project like very much so the bias is like, hey, I'm going to just build the thing.

01:59I'm going to make it really really freaking good, right? Like that's kind of like the bias you have as like an engineer. That's the bias we had when we started Exo and we were like honestly pretty bad at go to market. Like we were we were not doing enough marketing.

02:16maybe you're not doing enough sales. Um, but I think the cool thing about a about um go to market particularly in 2026 is you can treat go to market like an engineering problem and particularly an AI engineering problem. And so I think that's like a super exciting thing like it's like more fun for engineers than ever to do go to market because you can automate things you can you can do so much as one person and uh etc.

02:38Also um I want to make this interactive. If if anybody has questions at any point, please please ask because I'm aware there's a lot of talks and I don't want to bore you. Cool. So, um, cool. So, so the hypothesis I have is if you're an engineer or or if you're anyone, you can treat go to market like an engineering problem.

02:55So, first I guess like what is what do go to market teams do? So, I have like a laundry list of things here of things that go to market teams do.

Go to market is a data problem

03:05But here are a few like one is you got to research like your customer, right? You got to research your targets. You have to find out information about your about targets. You have to find the right people at particular companies. You have to build PC's.

03:16Um there's like a just a ton of stuff you have to do, right? Um so, you know, I'm not going to not going to list everything here, but like what is the grand unifying theme? Well, go to market is a data problem, right? So you have all you have this like entire world of uh of of what your product does and then this entire world of like all your potential customers and you're just trying to like learn and figure out what your world looks like.

03:45And so this is my this is my uh proposal. It's a data problem and we have to solve it from a data perspective. Cool. So okay so what is the data that is relevant? uh I propose that you need basically a live model of your world

A live model of your world that agents can act on

03:58that agents can act on. And so what does that mean? Okay. Well, one is you have a ton of internal data, right? There's all this information that you know about your customers, about people that are at your company, uh data about how people use the product.

04:06That's like internal data that you know. And then there's all sorts of external data, right? There's over 60 million companies in the world and there's like billions of people like over a billion that are on LinkedIn for example and all sorts of stuff are is is like happening every day right like there's all this news and so when you're building like this data go to market system um it's important to keep in mind just like all the different sources that exist and and and uh and are available to your agents

04:42and cool so I'm going to like go through hopefully pretty fast just all the different components of what we've built at EXA. And just for like context, uh I've been really passionate about this for a long time. So like EXA was launched in uh the middle of 2023.

04:50And so we were post GPD4 and GP4 was really incredible because it could actually even then even though it's way worse than like Fable or whatever, like it could actually just automate entire parts of go to market. And so from the beginning, I've been thinking about our go to market from from a very very uh AI agent first perspective.

05:16And so we're going to go over two interfaces that we have that help us and then two agents. Cool.

The ICP dashboard: classifying your whole market

05:26Cool. Okay. The first is what we call our ICP dashboard. And the ICP dashboard is an product that we have internally that answers the question like what is our world? Like what is the world of customers and use cases that we care about? And what we actually do is we go ahead and use Exa.

05:44And again, Exa is this like uh arbitrarily powerful search engine for AIS essentially. And we just classify basically like every possible company that is inside of our total addressable market. And uh I kind of blurred out some of the details on like how much money we make from each category and stuff like that.

06:00But yeah, we have like categories like model providers, AI coding platforms like say cursor, go to market intelligence tools. Um, and this makes up our TAM and we have an understanding of literally like almost every company within those segments.

06:16And then for each of those companies, we can deep dive, right? So here's the example of SpaceX. We can see how much annual spend we could anticipate them to have. Then all this like metadata about the company. So we have a list of all the companies and then a ton of data about each company.

06:24Uh, how do we do this? Again, we're able to do this because Exa is this search engine. We take the internet, we crawl it, we train uh we train embeddings to do web search really well. And so basically from a technical perspective, you could think about exa as like embeddings over the internet.

06:40And when you have embeddings over the internet, you have this like arbitrarily powerful semantic uh filtering and slicing and dicing of any type of data that you want. And so we use that to generate these this like gigantic list of potential ICPS.

Request Lens: alerts when something matters

06:59Cool. Next, uh we have a tool we call request lens. Request lens. What is request lens? Well, it's basically a system where anytime something significant happens with any of our customers, we're alerted. Someone signed up, someone used a ton of searches, someone stopped using searches, someone showed up that we really, really care about.

07:16All these things are signals that we are notified about and that our team can act on. Cool. Um so those are the two interfaces that we have and then I'll go over two types of agents that we have. So uh one is coding agents. So our go to market team is crazy crazy crazy deep on agents.

07:37So like like our our our engineering team uses a lot of agents

A dozen agents in Slack, and the go to market spend

07:45but our go to market team is like like you could look you look at some of their like devon spend and like other agent spend. It's really freaking high. And that's because everybody on our go to market team is constantly asking agents about our customers.

07:52Uh we have like like account executives that build demos for our customers. Like it's just this crazy ecosystem where we have like maybe a dozen different agents inside of our Slack and anybody can use any of them. They all have access to tons and tons of our internal data.

08:09And uh yeah, anytime we want to dig deeper on account, anytime we want to make a demo, etc. We depend heavily on agents.

Jeffbot: cloning yourself from 760 emails

08:22Cool. And then I want to talk about another really cool agent that I'm pretty proud of. We call it Jeffbot or I call it Jeffbot. Uh Jeffbot is an AI clone of myself uh as much as possible. So what is it? Well, basically this winter break uh I'm sure a lot of you spent that break playing with Opus 4.5 and I was no different.

08:41So I was in Puerto Rico. I was in Mexico and I was I had a week off and so my goal with that weekend with Opus 4.5 was to uh just try to make a digital code of myself. And so I did things like analyze like 760 of my emails to figure out what my email voice is.

08:57Like oh I use 18 words on average per email. And I like to end emails with best and not sincerely like all all that type of stuff, right? So I made like a like a voice for myself. And then I also made a decision-making framework. So I made like a decision-making framework where I analyzed hundreds of decisions I've made in the past and I I analyzed them and I created evals.

09:14So I actually created evals from those decisions and calibrated this agent system to behave like myself. And then finally I gave it like read and write access to all the data that I personally have. And there's a cool advantage to this because like I basically have access to every single system at the company uh because I'm in the the the nice seat of of having that.

09:43And so like um yeah this thing has access to like everything. And basically what happens is anybody at the company can use JeffBot to create drafts of Slack messages that are basically like answers or decisions that are made. And this is a huge great thing like go to market team uses it to like draft emails for example.

10:03Cool. Um all right so those those are the systems that uh that we have at at Exa. It works pretty well. Our go to market team is very lean but very productive. Um and so yeah, I just want to cover like lastly just a few principles

Agent first means API first

10:19um principles I have around what it means to be an agent first company. So firstly to be agent first you must be API first right? So like all these systems that we built whether there was those whether it was those agents or whether it was those gooies that we have like if there did not exist really good APIs on top of any internal and external data we'd be we'd be out of luck right like you need to create really good APIs if you don't have really good APIs your

10:46agents are not going to be able to have data access so you can think about this as MCP CLI whatever right like it doesn't really matter uh you just need some interface that's programmatic secondly is like I I think there's like still this mistake in the agent world which is made that's like hey does everything need to be a chatbot.

11:01Uh I think the answer is no. Like I think I think both guies and chat bots are are both super useful and have their own benefits. Like uh I don't know how many people in this room have thought about dynamic user interfaces but like yes dynamic user interfaces are amazing.

11:25Like yes, technically AI can just produce a new UI for any use case that you have. Like just to answer a question, it could produce like an HTML markdown file, right? But I think there is something really nice about being able to visit the same consistent UX for the same use cases over time so that you can like learn how to use some tool.

11:41Um, so yeah, I think like having crystallized UIs and then also arbitrarily powerful flexible chat agents are both important components of being agent first. And then finally, uh, you know, there's this question like, hey, should you like shop for like Salesforce or should you

Buy or build is a false dichotomy

12:01like build your own CRM or something, right? I actually think this is like a false dichotomy. It's like like it's not a choice. Like we don't live in a world where the choice is between purchasing SAS and building things yourself. Like the way I like to think about it is like you should just be using something that is arbitrarily customizable, right?

12:18Like whether you like obviously if you build something yourself then it's arbitrarily customizable because you can vibe code and make it better at any given point. But also if you procure SAS um if you can make that SAS work on your behalf and be arbitrarily customizable then that works too right like you don't need to build this like gooey and like have a proactive road map as to like what features make really great sense inside

12:46of some system like if you can arbitrarily customize the system even if it's a system you've purchased then you're like pretty good right so like for example we use Salesforce like we use Salesforce at Exa and uh it's great because uh it's a really good good database.

12:54It's made a lot of amazing choices around what sales should look like, choices that we don't want to make ourselves. And then it's exposes MCP, so all of our agents have access to Salesforce MCP. Works really well. Our team uses every day. And so, yeah, I think infinite customizability um is is really the highest order bit.

13:19Cool. Um that's that's all I had. Uh yeah. Does any have any questions? No, we have time for questions. Okay, coming.

Q&A: security boundaries and the FDE model

13:36Hey. Um, so you said you uh took all your past decisions. Can you elaborate a bit about that? What what artifacts are those? Usually people don't save like their decisions. Is it Slack? Is it email? Is it other other artifacts? Yeah, good question.

13:52I looked at decisions I made within Slack and email. I mean a surprisingly large amount of everything that goes on at companies in is is on Slack, right? So like if you just read like a ton of Slack history, like you can definitely find hundreds of decisions that you've made in the past.

14:09Awesome. Quick question uh over here. Uh so your go to market team, what's the split between uh are they just all like AI cracked or do they also have like the domain expertise too? What's the split between technical and nontechnical? Because obviously you need them to like know how to do marketing, sales, etc.

14:27But then do they also are they also upskilling in terms of using AI systems? Are you handing them tools or are they building their own? That's a very good question. So our go to market team is comprised of like there's there's account executives which like run the deals.

14:43There are like sales like SDRs that help with uh demand generation and then there are separately there's separately like a FTE or so. So forward deployed engineering organization and what I'll say is that like everyone that's okay everyone that's uh not not an FDE is like has learned how to use AI really well.

15:11So like the answer is like they're not vibe coding they're not generally with you know some there's some exceptions they're not generally vibe coding these interfaces that we have um but they are using the tools really really well and like we make sure that we have training sessions and like just make sure that people really understand how to use these tools and then this funny we have this funny thing which is like our for deployed engineering organization is actually the one that like does a lot of the maintenance and feature building uh of these AI systems and so they're both running deals

15:41and like like supporting deals um but then also making everything smoother by like doing sales but then also building the sales system like it's it's kind it's kind of a funky thing we have going on. Yeah. How do you think about uh different security Oh, how do you think about different uh security boundaries within your enterprise?

16:05What you s what you said suggested that you've got Jeffbot which have runs with all of your full privileges and then it's available to everybody which suggests that there's one security level and everyone can see everything all the time. Is that what you're going with or is there some uh other guardrails in place?

16:22Yeah, that's a good question. We we pay pretty special we we pay pretty careful attention to guardrails. So for example um in the case of Jeffbot um when I use Jeffbot and I call Jeffbot has access to a ton of systems and it can for example do reads and writes.

16:39However when anybody else calls Jeffbot all it can do is draft messages and also I don't give Jeffbot permissions to all of our MCPs and tools in the case of what other people call it. And so in short it's like pretty it's pretty well defined or we we we do pay some care to the security.

16:58Yeah. Okay. Last question. Um can you can you share the uh origin story of the FTE team? Did that just happen organically or did you intentionally do it? I'm just really curious like how that came to exist. Yeah, for sure. I mean, uh, my my philos my my hypothesis on this is like once upon a time the FD role didn't really exist.

17:24Like Palunteer started calling some people FTEEs, but that was really it. And what tech companies had was like solutions and sales engineers and then like account executives. I was a solution. Got it. Yeah. Yeah. The thing the thing that I think has changed is that um because of AI as like a engine as a technical person that is supporting revenue generation you can actually not only support that revenue generation but then very easily build the tooling to smooth

17:56everything over and make your own life easier make the lives of AES easier like because of AI this is just possible now like that's like two before that was like two jobs and now it's like one job um in theory like Now when our team grows like right now it's about eight or nine FTEEs like will will it scale such that everyone does everything probably not but at least right now that's what we have and I think that's a really good working model to get pretty far.

18:20Eight out of many uh eight oh eight of like how big is our go to market or eight you have eight and right now oh the uh we're about 115 people. Okay. Yeah.