WEBVTT NOTE Sentence-level transcript of https://www.youtube.com/watch?v=6pbQgnJ9Voc NOTE One cue per sentence. Cue ids are the line anchors on /transcripts/6pbQgnJ9Voc.html. A cue ends where the next begins, or 2 s after its last word. s1 00:00:01.309 --> 00:00:03.309 [music] s2 00:00:12.640 --> 00:00:13.480 Hey everybody, I'm Jeff. s3 00:00:13.480 --> 00:00:22.760 I guess I was introduced, but I'm the co-founder of Exa, and today going to give a talk on turning go-to-market into an AI engineering problem in the spirit of this s4 00:00:22.760 --> 00:00:24.000 AI engineering fair. s5 00:00:24.000 --> 00:00:30.160 And just a quick show of hands just to like understand the audience, like raise your hand if you're a technical. s6 00:00:30.480 --> 00:00:31.040 Okay, great. s7 00:00:31.040 --> 00:00:38.120 Okay, so I kind of oriented this talk around like go-to-market as presented to to engineers. s8 00:00:38.120 --> 00:00:40.840 So, happy that I did that. s9 00:00:40.840 --> 00:00:41.080 Cool. s10 00:00:41.080 --> 00:00:47.000 So, first just to like ground the ground like what what Exa is cuz it's sort of relevant inside of this presentation. s11 00:00:47.000 --> 00:00:49.880 Exa is a Exa is a search engine for agents. s12 00:00:49.880 --> 00:00:53.120 Think like agents are really smart, but they don't have access to the web. s13 00:00:53.120 --> 00:00:55.680 We're like this web MCP web tool that agents can access. s14 00:00:55.680 --> 00:01:00.440 We power Cursor, we power Cognition, we power a lot of the AI ecosystem at this point. s15 00:01:00.440 --> 00:01:06.400 And before we start, I also just want to like talk about, you know, especially to the technical audience, like why should you even care? s16 00:01:06.400 --> 00:01:07.680 Like why should you care about go-to-market? s17 00:01:07.680 --> 00:01:17.480 I guess this audience cares about go-to-market cuz you chose to go to this go-to-market talk, but 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." s18 00:01:17.480 --> 00:01:19.760 Or "Distribution is the only thing that matters." s19 00:01:19.760 --> 00:01:27.560 And there's all sort of like all sorts of like Twitter flame wars like like oh, is Glean going to succeed because they're really good at distribution, but they're like what what the heck is their product? s20 00:01:27.560 --> 00:01:35.160 And then and other people are like, "Oh, the like the the product needs to be super good cuz agents you know, agents shop for the product, so they'll shop the for the best product." s21 00:01:35.160 --> 00:01:40.720 And so, my view and my experience in the last few years is that you just kind of have to do both. s22 00:01:40.720 --> 00:01:44.320 Like I think you have to get product right and you have to get go-to-market right. s23 00:01:44.320 --> 00:01:48.280 Like you got to build this thing, it's got to be good, and then you got to get it into people's hands. s24 00:01:48.280 --> 00:01:51.440 If you don't do both things, then you don't have a company. s25 00:01:51.440 --> 00:01:54.440 So, that's kind of my view on the matter. s26 00:01:54.440 --> 00:02:06.560 And I I say like a really funny thing also is like as a technical person when you start a company or you start some some sort of project, like very much so the bias is like, "Hey, I'm going to just build the thing. s27 00:02:06.560 --> 00:02:08.520 I'm going to make it really really freaking good, right?" s28 00:02:08.520 --> 00:02:10.800 Like that's kind of like the bias you have as like an engineer. s29 00:02:10.800 --> 00:02:15.160 That's the bias we had when we started X.ai and we were like honestly pretty bad at go-to-market. s30 00:02:15.160 --> 00:02:15.586 Like we s31 00:02:15.586 --> 00:02:16.240 [laughter] s32 00:02:16.240 --> 00:02:18.840 we were not doing enough marketing, we were not doing enough sales. s33 00:02:18.840 --> 00:02:29.880 Um but I think the cool thing about 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. s34 00:02:29.880 --> 00:02:31.600 And so I think that's like a super exciting thing. s35 00:02:31.600 --> 00:02:37.600 Like it's like more fun for engineers than ever to do go-to-market cuz you can automate things. s36 00:02:37.600 --> 00:02:44.720 You can you can do so much as one person and uh etc. Also um I want to make this interactive. s37 00:02:44.720 --> 00:02:51.600 If if anybody has questions at any point, please please ask cuz I'm aware there's a lot of talks and I don't want to bore you. s38 00:02:51.600 --> 00:02:51.959 Cool. s39 00:02:51.959 --> 00:02:53.120 So um cool. s40 00:02:53.120 --> 00:02:58.720 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. s41 00:02:58.720 --> 00:03:01.480 So first, I guess like what does what do go-to-market teams do? s42 00:03:01.480 --> 00:03:06.040 So I have like a laundry list of things here of things that go-to-market teams do, but here are a few. s43 00:03:06.040 --> 00:03:09.080 Like one is you got to research like your customer, right? s44 00:03:09.080 --> 00:03:10.680 You got to research your targets. s45 00:03:10.680 --> 00:03:13.760 You have to find out information about your about targets. s46 00:03:13.760 --> 00:03:16.880 You have to find the right people at particular companies. s47 00:03:16.880 --> 00:03:18.880 You have to build POCs. s48 00:03:18.880 --> 00:03:21.800 Um there's like a just a ton of stuff you have to do, right? s49 00:03:21.800 --> 00:03:26.959 Um so you know, I'm not going to I'm not going to list everything here, but like what is the grand unifying theme? s50 00:03:26.959 --> 00:03:30.000 Well, go-to-market is a data problem, right? s51 00:03:30.000 --> 00:03:39.200 So you have all you have this like entire world of uh of of what your product does and then and this entire world of like all your potential customers s52 00:03:39.200 --> 00:03:45.040 and you're just going to like learn and figure out what your world looks like. s53 00:03:45.040 --> 00:03:47.480 And so this is my this is my uh proposal. s54 00:03:47.480 --> 00:03:51.480 It's a data problem and we have to solve from a data perspective. s55 00:03:51.519 --> 00:03:51.840 Cool. s56 00:03:51.840 --> 00:03:54.320 So okay, so what is data that is relevant? s57 00:03:54.320 --> 00:04:00.520 Uh I propose that you need basically a live model of your world that agents can act on. s58 00:04:00.520 --> 00:04:01.920 And so, what does that mean? s59 00:04:01.920 --> 00:04:04.320 Okay, well, one is you have a ton of internal data, right? s60 00:04:04.320 --> 00:04:14.120 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. s61 00:04:14.120 --> 00:04:16.600 That's like internal data that you know. s62 00:04:16.600 --> 00:04:18.280 And then there's all sorts of external data, right? s63 00:04:18.280 --> 00:04:28.800 Like 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? s64 00:04:28.800 --> 00:04:30.520 Like there's all this news. s65 00:04:30.520 --> 00:04:42.800 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. s66 00:04:42.960 --> 00:04:43.320 And cool. s67 00:04:43.320 --> 00:04:51.080 So, I'm going to like go through, hopefully pretty fast, just all the different components of what we've built at Exa. s68 00:04:51.080 --> 00:04:54.840 And just for like context, uh I've been really passionate about this for a long time. s69 00:04:54.840 --> 00:05:01.520 So, like Exa was launched in uh the middle of 2023, and so we were post-GPT-4. s70 00:05:01.520 --> 00:05:12.040 And GPT-4 was really incredible, cuz 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. s71 00:05:12.040 --> 00:05:17.600 And so, from the beginning, I've been thinking about our go-to-market from from a very, very uh AI agent-first perspective. s72 00:05:17.600 --> 00:05:22.520 And so, we're going to go over two interfaces that we have that help us, and then two agents. s73 00:05:22.520 --> 00:05:24.520 Cool. s74 00:05:26.919 --> 00:05:27.160 Cool. s75 00:05:27.160 --> 00:05:30.200 Okay, the first is what we call our ICP dashboard. s76 00:05:30.200 --> 00:05:36.240 And the ICP dashboard is a product that we have internally that answers the question, like what is our world? s77 00:05:36.240 --> 00:05:41.480 Like what is the world of customers and use cases that we care about? s78 00:05:41.480 --> 00:05:51.720 And what we actually do is we go ahead and use Exa, and again, Exa is this like uh arbitrarily powerful search engine for AIs essentially. s79 00:05:51.720 --> 00:05:57.400 And we just classify basically like every possible company that is inside of our total addressable market. s80 00:05:57.400 --> 00:06:03.480 And I kind of blurred out some of the details on like how much money we make from each category and stuff like that. s81 00:06:03.480 --> 00:06:09.600 But yeah, we have like categories like model providers, AI coding platforms like say Cursor, go-to-market intelligence tools. s82 00:06:09.600 --> 00:06:16.600 And this makes up our TAM and we have an understanding of literally like almost every company within those segments. s83 00:06:16.600 --> 00:06:18.840 And then for each of those companies we can deep dive, right? s84 00:06:18.840 --> 00:06:20.520 So here's the example of SpaceX. s85 00:06:20.520 --> 00:06:25.760 We can see how much annual spend we could anticipate them to have then all this like metadata about the company. s86 00:06:25.760 --> 00:06:29.720 So we have a list of all the companies and then a ton of data about each company. s87 00:06:29.720 --> 00:06:31.000 How do we do this? s88 00:06:31.000 --> 00:06:34.160 Again, we're able to do this because Exa is this search engine. s89 00:06:34.160 --> 00:06:39.440 We take the internet, we crawl it, we train we train embeddings to do web search really well. s90 00:06:39.440 --> 00:06:44.480 And so basically from a technical perspective you can think about Exa as like embeddings over the internet. s91 00:06:44.480 --> 00:06:52.680 And when you have embeddings over the internet you have this like arbitrarily powerful semantic filtering and slicing and dicing of any type of data that you want. s92 00:06:52.680 --> 00:06:58.120 And so we use that to generate this this like gigantic list of potential ICPs. s93 00:06:59.680 --> 00:07:00.000 Cool. s94 00:07:00.000 --> 00:07:03.600 Next, we have a tool we call Request Lens. s95 00:07:03.600 --> 00:07:04.960 Request Lens, what is Request Lens? s96 00:07:04.960 --> 00:07:10.880 Well, it's basically a system where anytime something significant happens with any of our customers, we're alerted. s97 00:07:10.880 --> 00:07:17.680 Someone signed up, someone used a ton of searches, someone stopped using searches, someone showed up that we really really care about. s98 00:07:17.680 --> 00:07:24.040 All these things are signals that we are notified about and that our team can act on. s99 00:07:28.760 --> 00:07:29.800 Cool. s100 00:07:29.800 --> 00:07:33.720 So those are the two interfaces that we have and then I'll go over two types of agents that we have. s101 00:07:33.720 --> 00:07:36.000 So one is coding agents. s102 00:07:36.000 --> 00:07:42.040 So our go-to-market team is crazy crazy crazy deep on agents. s103 00:07:42.040 --> 00:07:49.520 So like like our our our engineering team uses a lot of agents, but our go-to-market team is like like you could look you could look at some of their like devin spend and like other agent spend. s104 00:07:49.520 --> 00:07:50.640 It's really freaking high. s105 00:07:50.640 --> 00:07:57.600 And that's because everybody on our go-to-market team is constantly asking agents about our customers. s106 00:07:57.600 --> 00:08:02.560 Uh we have like like account executives that build demos for our customers. s107 00:08:02.560 --> 00:08:09.080 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. s108 00:08:09.080 --> 00:08:12.000 They all have access to tons and tons of our internal data. s109 00:08:12.000 --> 00:08:13.600 And uh yeah. s110 00:08:13.600 --> 00:08:20.080 Anytime we want to dig deeper on account, anytime we want to make a demo, etc., we depend heavily on agents. s111 00:08:23.000 --> 00:08:23.240 Cool. s112 00:08:23.240 --> 00:08:25.680 And then I want to talk about another really cool agent that I'm pretty proud of. s113 00:08:25.680 --> 00:08:27.080 We call it Jeff Bots. s114 00:08:27.080 --> 00:08:28.520 Uh or I call it Jeff Bots. s115 00:08:28.520 --> 00:08:32.000 Uh Jeff Bots is an AI clone of myself. s116 00:08:32.000 --> 00:08:33.280 Uh as much as possible. s117 00:08:33.280 --> 00:08:33.919 So, what is it? s118 00:08:33.919 --> 00:08:42.640 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. s119 00:08:42.640 --> 00:08:45.240 So, I was in Puerto No, I was in Mexico. s120 00:08:45.240 --> 00:08:48.000 I was in Mexico and I would I had a week off. s121 00:08:48.000 --> 00:08:53.720 And so, my goal with that week and with Opus 4.5 was to uh just try to make a digital clone of myself. s122 00:08:53.720 --> 00:09:00.200 And so, I did things like analyze like 760 of my emails to figure out what my email voice is. s123 00:09:00.200 --> 00:09:05.440 Like, oh, I use 18 words on average per email and I like to end emails with best and not sincerely. s124 00:09:05.440 --> 00:09:06.960 Like all all that type of stuff, right? s125 00:09:06.960 --> 00:09:10.320 So, I made like a like a voice for myself. s126 00:09:10.320 --> 00:09:13.440 And then I also made a decision-making framework. s127 00:09:13.440 --> 00:09:19.160 So, I made like a decision-making framework where I analyzed hundreds of decisions I've made in the past. s128 00:09:19.160 --> 00:09:21.960 And I I analyzed them and I created evals. s129 00:09:21.960 --> 00:09:29.080 So, I actually created evals from those decisions and calibrated this agent system to behave like myself. s130 00:09:29.080 --> 00:09:33.400 And then finally I gave it like read and write access to all the data that I personally have. s131 00:09:33.400 --> 00:09:44.120 And there's a cool advantage to this because like I basically have access to every single system at the company uh cuz I'm in the the the nice seat of of having that and so like s132 00:09:44.120 --> 00:09:47.320 um yeah, this thing has access to like everything. s133 00:09:47.320 --> 00:09:56.040 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. s134 00:09:56.040 --> 00:09:57.680 And this is a huge great thing. s135 00:09:57.680 --> 00:10:01.480 Like our go-to-market team uses it to like draft emails, for example. s136 00:10:03.760 --> 00:10:04.720 Cool. s137 00:10:04.720 --> 00:10:10.040 Um all right, so those those are the systems that uh that we have at at Exa. s138 00:10:10.040 --> 00:10:10.960 It works pretty well. s139 00:10:10.960 --> 00:10:14.080 Our go-to-market team is very lean, but very productive. s140 00:10:14.080 --> 00:10:25.840 Um and so yeah, I just want to cover like lastly just a few principles um principles I have around what it means to be an agent-first company. s141 00:10:25.840 --> 00:10:28.520 So firstly, to be agent-first you must be API-first, right? s142 00:10:28.520 --> 00:10:40.680 So like all these systems that we built, whether they were those whether it was those agents or whether it was those GUIs that we have, like if there did not exist really good APIs on top of any internal and external data s143 00:10:40.680 --> 00:10:42.120 we'd be we'd be out of luck, right? s144 00:10:42.120 --> 00:10:44.040 Like you need to create really good APIs. s145 00:10:44.040 --> 00:10:49.720 If you don't have really good APIs your agents are not going to be able to have data access. s146 00:10:49.720 --> 00:10:52.040 So you can think about this as MCP, CLI, whatever, right? s147 00:10:52.040 --> 00:10:53.320 Like it doesn't really matter. s148 00:10:53.320 --> 00:10:56.480 Uh you just need some interface that's programmatic. s149 00:10:57.120 --> 00:11:06.400 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? s150 00:11:06.400 --> 00:11:16.600 Uh I think the answer is no. Like I think I think both GUIs and chatbots are are both super useful and have their own benefits. s151 00:11:16.600 --> 00:11:24.920 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. s152 00:11:24.920 --> 00:11:29.560 Like yes, technically AI can just produce a new UI for any use case that you have. s153 00:11:29.560 --> 00:11:33.360 Like just to answer a question, it could produce like an HTML markdown file, right? s154 00:11:33.360 --> 00:11:42.760 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. s155 00:11:42.760 --> 00:11:53.360 Um so yeah, I think like having crystallized UIs and then also arbitrarily powerful flexible chat agents are both important components of being agent first. s156 00:11:53.520 --> 00:12:04.520 And then finally, uh you know, there's this question like, "Hey, should you like shop for like Salesforce or should you like build your own CRM or something, right?" s157 00:12:04.520 --> 00:12:06.480 I actually think this is like a false dichotomy. s158 00:12:06.480 --> 00:12:10.240 It's like like there it's not a choice. s159 00:12:10.240 --> 00:12:14.560 Like we don't live in a world where the choice is between purchasing SaaS and building things yourself. s160 00:12:14.560 --> 00:12:22.360 Like the way I like to think about it is like you should just be using something that is arbitrarily customizable, right? s161 00:12:22.360 --> 00:12:29.800 Like whether you like obviously if you build something yourself, then it's arbitrarily customizable cuz you can write code and make it better at any given point. s162 00:12:29.800 --> 00:12:39.320 But also if you procure SaaS, um if you can make that SaaS work on your behalf and be arbitrarily customizable, then that works too, right? s163 00:12:39.320 --> 00:12:47.160 Like you don't need to build this like GUI and like have a proactive roadmap as to like what features would make really great sense inside of some system. s164 00:12:47.160 --> 00:12:52.960 Like if you can arbitrarily customize the system, even if it's a system you've purchased, then you're like pretty good, right? s165 00:12:52.960 --> 00:12:54.480 So like for example, we use Salesforce. s166 00:12:54.480 --> 00:12:59.920 Like we use Salesforce at X.ai and uh it's great because uh it's a really good good database. s167 00:12:59.920 --> 00:13:05.960 It's made a lot of amazing choices around what sales should look like, choices that we don't want to make ourselves. s168 00:13:05.960 --> 00:13:07.200 And then it exposes MCP. s169 00:13:07.200 --> 00:13:09.560 So all of our agents have access to Salesforce MCP. s170 00:13:09.560 --> 00:13:10.240 Works really well. s171 00:13:10.240 --> 00:13:11.920 Our team uses it every day. s172 00:13:11.920 --> 00:13:18.240 And so yeah, I think infinite customizability um is is really the highest order bit. s173 00:13:19.760 --> 00:13:20.600 Cool. s174 00:13:20.600 --> 00:13:23.839 Um that's that's all I had. s175 00:13:23.839 --> 00:13:26.080 Uh Yeah, does anyone have any questions? s176 00:13:26.080 --> 00:13:28.960 Oh, we have time for a few questions. s177 00:13:28.960 --> 00:13:31.280 Okay, coming. s178 00:13:36.280 --> 00:13:40.920 Hey, um so you said you uh took all your past decisions. s179 00:13:40.920 --> 00:13:43.320 Can you elaborate a bit about that? s180 00:13:43.320 --> 00:13:45.880 What What artifacts are those? s181 00:13:45.880 --> 00:13:47.920 Usually people don't save like their decisions. s182 00:13:47.920 --> 00:13:48.640 Is it Slack? s183 00:13:48.640 --> 00:13:49.280 Is it email? s184 00:13:49.280 --> 00:13:52.000 Is it other other artifacts? s185 00:13:52.000 --> 00:13:52.880 Yeah, good question. s186 00:13:52.880 --> 00:13:56.720 I looked at decisions I made within Slack and email. s187 00:13:56.720 --> 00:14:02.560 I mean, a surprisingly large amount of everything that goes on a company is is is on Slack, right? s188 00:14:02.560 --> 00:14:09.720 So like if you just read like a ton of Slack history, like you can definitely find hundreds of decisions that you made in the past. s189 00:14:09.720 --> 00:14:10.360 Awesome. s190 00:14:10.360 --> 00:14:11.760 Quick question uh over here. s191 00:14:11.760 --> 00:14:21.080 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? s192 00:14:21.080 --> 00:14:24.120 What's the split between technical and non-technical? s193 00:14:24.120 --> 00:14:31.720 Because obviously you need them to like know how to do marketing, sales, etc. But then do they also are they also upskilling in terms of using AI systems? s194 00:14:31.720 --> 00:14:35.320 Are you handing them tools or they building their own? s195 00:14:35.320 --> 00:14:36.560 That's a very good question. s196 00:14:36.560 --> 00:14:45.800 So our go-to-market team is comprised of like there's there's account executives which like run the deals. s197 00:14:45.800 --> 00:14:51.920 There are like sales like SDRs that help with uh demand generation. s198 00:14:51.920 --> 00:14:55.640 And then there are separately they're separate like a FDE org. s199 00:14:55.640 --> 00:14:58.080 So forward deployed engineering organization. s200 00:14:58.080 --> 00:15:11.000 And what I'll say is that like everyone that's Okay, everyone that's uh not not in FDE is like has learned how to use AI really well. s201 00:15:11.000 --> 00:15:14.480 So like the answer is like they're not vibe coding. s202 00:15:14.480 --> 00:15:17.120 They're not generally with you know, in some there's some exceptions. s203 00:15:17.120 --> 00:15:20.400 They're not generally vibe coding these interfaces that we have. s204 00:15:20.400 --> 00:15:22.560 Um but they're using the tools really really well. s205 00:15:22.560 --> 00:15:28.600 And like we make sure that we have training sessions and like just make sure that people really understand how to use these tools. s206 00:15:28.600 --> 00:15:37.440 And then this funny we have this funny thing which is like our four deployed engineering organization is actually the one that like does a lot of the maintenance and feature building s207 00:15:37.440 --> 00:15:38.960 uh of these AI systems. s208 00:15:38.960 --> 00:15:53.240 And so they're both running deals and like like supporting deals um but then also making everything smoother by like doing sales but then also building the sales system. s209 00:15:53.240 --> 00:15:55.800 Like it's it's kind of it's kind of a funky thing we have going on. s210 00:15:55.800 --> 00:15:57.800 Yeah. s211 00:15:58.440 --> 00:16:02.080 How do you think about uh different security? s212 00:16:02.080 --> 00:16:02.640 Oh. s213 00:16:02.640 --> 00:16:03.160 Hey. s214 00:16:03.160 --> 00:16:06.560 How do you think about different uh security boundaries within your enterprise? s215 00:16:06.560 --> 00:16:17.800 What do you What you said suggested that you've got Jeffbot which had 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. s216 00:16:17.800 --> 00:16:22.760 Is that what you're going with or is there some uh other guardrails in place? s217 00:16:22.760 --> 00:16:23.600 Yeah, that's a good question. s218 00:16:23.600 --> 00:16:27.400 We we pay pretty special we we pay pretty careful attention to guardrails. s219 00:16:27.400 --> 00:16:39.680 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. s220 00:16:39.680 --> 00:16:51.920 However, when anybody else calls Jeffbot all can do is draft messages and also I don't give Jeffbot permissions to all of our MCPs and tools in the case where other people call it. s221 00:16:51.920 --> 00:16:58.720 And so in short it's like pretty it's pretty well defined or we we we do pay some care to the security. s222 00:16:58.720 --> 00:16:59.080 Yeah. s223 00:16:59.080 --> 00:17:01.480 Okay, last question. s224 00:17:04.560 --> 00:17:09.480 Um, can you can you share the origin story of the FDE team? s225 00:17:09.480 --> 00:17:12.560 Did that just happen organically or did you intentionally do it? s226 00:17:12.560 --> 00:17:16.480 I'm just really curious like how that came to exist. s227 00:17:16.480 --> 00:17:17.079 Yeah, for sure. s228 00:17:17.079 --> 00:17:25.360 I mean, uh my my philos- my my hypothesis on this is like, once upon a time the FDE role didn't really exist. s229 00:17:25.360 --> 00:17:29.800 Like, Palantir started calling some people FDEs, but it that was really it. s230 00:17:29.800 --> 00:17:34.520 And what tech companies had was like solutions and sales engineers. s231 00:17:34.520 --> 00:17:37.080 And then, like account executives. s232 00:17:37.080 --> 00:17:38.240 I was a solutions engineer. s233 00:17:38.240 --> 00:17:39.160 Got it. s234 00:17:39.160 --> 00:17:39.640 Yeah, yeah. s235 00:17:39.640 --> 00:17:55.960 The thing The thing that I think has changed is that um because of AI, as like en- as a technical person that is supporting revenue generation, you can actually not only support the revenue generation, but then very easily build the tooling s236 00:17:55.960 --> 00:17:58.040 to smooth everything over. s237 00:17:58.040 --> 00:18:01.400 And make your own life easier, make the lives of AEs easier. s238 00:18:01.400 --> 00:18:03.000 Like, because of AI, this is just possible now. s239 00:18:03.000 --> 00:18:07.360 Like, that's like two Before that was like two jobs, and now it's like one job. s240 00:18:07.360 --> 00:18:08.840 Um in theory. s241 00:18:08.840 --> 00:18:15.200 Like, now when our team grows, like right now it's about eight or nine FDEs, like what will will it scale such that everyone does everything? s242 00:18:15.200 --> 00:18:15.800 Probably not. s243 00:18:15.800 --> 00:18:20.240 But, at least right now that's what we have, and I think that's a really good working model to get pretty far. s244 00:18:20.240 --> 00:18:22.160 Eight out of how many? s245 00:18:22.160 --> 00:18:24.560 Uh eight Oh, eight of like how big is our go-to-market org? s246 00:18:24.560 --> 00:18:28.680 Or eight You have eight FDEs, and the size of the company right now is how many? s247 00:18:28.680 --> 00:18:31.440 Oh, the We're about 115 people. s248 00:18:31.440 --> 00:18:31.840 Okay. s249 00:18:31.840 --> 00:18:33.840 Yeah.