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Dylan: Imagine I'm a brand new user or I'm trying to do something new, even in HubSpot. "Hey, how do I do this thing?” And instead of that requiring me to open up a knowledge based article, or poke around the app a bunch, watch some academy content. Instead, what if it was just done for me? And I could just focus on the results, and the outputs, and not learning the CRM. Or figuring out how my property structures are formatted. Instead, just focus on the outcomes and the things you're looking to do to grow.
Connor: Hello and welcome to Go to Market with AI, a podcast for sales, marketing, and customer success leaders using AI to scale their growth operations. I'm your host, Connor Jeffers, and I am joined today by Dylan Sellberg, director of product AI innovation at HubSpot. What's up, Dylan?
Dylan: Hey, how's it going, Connor? Great to be here.
Connor: Likewise. Good to see you. But before we get into the very exciting product AI innovation. I've known you for a bit, and prior to this morning, I was racking my brain to try to remember how we first got introduced.Because I feel like I tell people, with some degree of frequency, of like, "Oh yeah, how I got into the HubSpot universe." And I was like, "Then I met Dylan, and he was doing cool stuff," and I legitimately do not remember how on earth we originally got connected, do you?
Dylan: It was one of two things. It was either when we were working on field level permissions. Locking down properties, before we even had the view level, the field level permissions edit. Or it was in the earliest, earliest days of custom objects. And so that would put us pre COVID just about, yeah.
Connor: Back in like the HubSpot CRM dinosaur ages because things move so fast.
Dylan: It would put us around those days BC before COVID before custom objects.
Connor: Before custom objects. HubSpot BC.
Dylan: Yeah, yeah it was, it was those days for sure.
Connor: For anyone who's here, you have a, in my opinion, like a super interesting HubSpot background and career, because you have worked on most of the things that I love that attract me to HubSpot and got me to work with HubSpot.If you want to start at the beginning, you can, but give me the very, like, we'll get to product and AI innovation, but what's the origin story here?
Dylan: Yeah it's been a while all in, really thinking about it. I started HubSpot in 2016 as a consultant implementation specialist. I spent about a year helping customers get started with the HubSpot CRM, HubSpot product. At the time, that was when actually the CRM didn't even exist. I remember my first day sitting next to my teammate, Joe. And he was like, "Hey, did they teach you about the CRM in new hire training?” And I was like, "No, I don't think so." And they were like, "Oh yeah, cause like we launched it today." And so, I spent about a year doing that and moved over to the product side of the house. Worked on Facebook Messenger, which is a really fun. Associate product manager product took that from zero to one. Helping, this was like in the earliest days of messenger bots. I didn't know that I would like naturally end up spending so much time thinking about like conversational CRM at that point, but built messenger bots. This is before conversations. And so we kind of like wound down that project a bit. Moved over to the HubSpot CRM. And I spent a lot of time helping customers store and structure data inside of HubSpot working on import, properties, CRM setup, a lot of the admin side of house. From there, shifted over to work on the calling product group for a little while. And finally came back around to HubSpot to work on ChatSpot.
Connor: Well, you, so you bounced out a HubSpot and then, and then something really exciting pulled you back in. And so, we don't have to talk too much about what was the temptation out. But you were like, "Hey, I built a bunch of cool stuff at HubSpot. I'm going to go build cool stuff elsewhere." And then you boomeranged back.
Dylan: It was a very short boomerang. But I mean, the same reason out was the same reason back in, I was just, I was hungry. I was looking for something exciting, something new something challenging. And so, when the opportunity arose to come back and work on ChatSpot, which I had been following since the day Dharmesh launched it on March 6th, I was super excited for, the potential that has for, not only HubSpot customers, but CRM and the industry as a whole. When that opportunity came up, I was like, "Yeah, let's do it."
Connor: So hopefully anyone who is listening to this or seeing this is like, "Oh yeah, ChatSpot. I know what that is." Assuming maybe they don't. What is ChatSpot?
Dylan: ChatSpot is the conversational AI assistant for using the HubSpot CRM. And so, it was developed by Dharmesh outside of HubSpot. Basically on the tail of the generative AI boom that ChatGPT began. So under a year old type thing, Dharmesh saw what folks are building over at OpenAI and said, "You know, this, it would be nice to be able to talk to your CRM in such a way.” And we tried this at HubSpot with GrowthBot back in 2015, 2016, and the technology just wasn't there. And so, with LLMs and the power that we have like now been bequeathed through OpenAI, the opportunity for ChatSpot was real and it was here. And so it helps you talk to your CRM, helps you sell better, market better, and use the entire corpus of data that you have using nothing but natural language. It's It's quite literally like talking to a friend. It's that easy.
Connor: So when you think about what you're building with ChatSpot, and you and I had had this really interesting conversation several, several months ago at this point, which feels like an eternity in AI land. But when, when you're thinking about what you guys are building a ChatSpot and who ChatSpot is for, are you thinking about it as primarily, this is something that we're building for HubSpot customers to be able to interact with HubSpot better? Is it something more or different than that?
Dylan: I think we have, we have the opportunity for, two things. We have the opportunity to help HubSpot customers use the HubSpot product in a much more enjoyable, much more powerful way. But we also have this opportunity, just like we had with INBOUND, and any other sales and marketing emerging tech, to teach non-HubSpot customers, to help non-HubSpot customers get value for their business. To help them grow, regardless of whether they're using the HubSpot CRM. So our early passes with ChatSpot were pretty broad, right? The things we built in the areas that we focus, the use cases that we were targeting were both applicable to HubSpot customers and non-HubSpot customers.
Connor: Can you give examples of like, what's the initial stuff that you guys were building for ChatSpot? Where you're like, "What if we did something like this," and how you thought about it?
Dylan: Yeah. You can think about prospecting use case, for example, right? Like we took natural language processing and figured out, "Okay, so we have something like access to Clearbit APIs, and we can take natural language. We can marry a customer's request of, "Help me find companies, help me discover prospects in a given area, or technology use." We can use OpenAI to translate that request and turn that into a Clearbit request. And format that back as if you're having a conversation with Clearbit data." Right? And we, so we did that in a few arenas, right? We did prospecting with Clearbit, image generation. For a small, very short amount of time, we're really excited about multimodality. You could do images and chat inside of ChatSpots before ChatGPT had that. Before Bard supported it. we were super stoked about that.
Connor: The world moves fast.
Dylan: Right. You're right. You're like, "Oh, look how different we are." But things like that, right?
Like really putting that, that business application layer on top of generative AI.
Connor: Not to reduce this because the scale of ChatSpot is big. Like, how many, how many ChatSpot are you at right now?
Dylan: In total, we are over 150,000 signups.
Connor: We're not talking about like, and when I say it's a, the original impetus was, "What if there was sort of a, business and sales and marketing wrapper around ChatGPT?" Before ChatGPT was like, "What if there was an everything wrapper for ChatGPT?" And so, this was the very initial love. We could use this in really cool, exciting, interesting ways.
Dylan: Yes. Yeah, precisely.
Connor: So outset is, "Let's build this thing. Let's try to solve this problem for some of these people." Where have you grown to? And if there's a multi milestone there, feel free to take it.
Dylan: Well, we, saw that working and there's still a lot of opportunity in that space. When you kind of look around at what's happening in the industry, you kind of like see two really strong undercurrents. You have something that users are just gravitating to, naturally, without any business. Which is sharing, and this is all over LinkedIn, it's all over Twitter, it's all over medium and anywhere you go. The business applications for ChatGPT. Here are the 10 prompts you need to do to do X. You know, how's everybody handling X in ChatGPT, right? Like you have that happening and users are like kind of pulling in that direction. So we have that opportunity with ChatSpot to just put that layer on, right? Like you shouldn't need to copy and paste these mega prompts and understand like advanced prompt engineering for really business specific use cases. ChatSpot can use things like templates and these libraries to just take the work away. Instead, you just play Mad Libs and we have like the religion and the prompt engineering for business applications. Helping customers grow and make that really easy. So you have like that trend.
Connor: Can I ask like a philosophical question about what you just said? The distillation and saying, "Oh, well, we're just doing Mad Libs and it's on top." Right? And is that, in your mind, is that a simple framework of like, "Oh, we'll just help these users. And we're like skipping a couple of steps?" Or does that, is that like a UX layer, on top of the database itself? If that comparison makes sense.
Dylan: I think it's, it's a bit of both.
Connor: Okay.
Dylan: At the end of the day, it's, also like a training moment for users to help understand, right? Like how to, how to use an LLM.
Connor: But do you like the comp that I almost make in my head from what you just said, is like most business software is basically spreadsheets with extra steps, like, more or less, right? It's like, we're going to build some UX is we're going to automatically create these tables. We're going to make it easy for you to navigate between these tables. And I think you could almost argue that like, by using a CRM, we're almost teaching you how a relational database works and like why they're cool. Do you think that that's a, like, I guess the parallel for me is on one layer where we're like, "Oh, it's just Mad Libs and like, we're maybe helping the user not have to copy paste something." But it's almost this meta level above that of, that could be said of, all software is, it's just a layer between the machine and the user, and the product is just helping the user use the base machine.
Dylan: That's exactly what it is. Yeah. That's how I think about that. I think over time it becomes less of a layer, less of a force layer, right? Like you, you see a lot of these very similar applications popping up all over now. And they're very heavy into, what I'd call a quick actions, like pre filled responses, like Facebook messengers had those since bots even launched, right? Like handling natural language is hard. Even how far LLMs have come, right? It's still pretty difficult. And you see applications all over kind of leaning into templating, leaning into quick actions, things like that as a way to interact with the system. I think over time you start to like remove those layers and get a little bit better about being able to not only understand requests, but users get a little bit better about making them. But right now it's, it's just an alternative.
Connor: So I interrupted you, but you were saying how the version of today is sort of the two things. One thing is we can create pre-filled prompts, do some of this Mad Libs, make a user and teach a user how to interact with it better.
Dylan: Yeah. So, like, we, we saw that happening and it's still happening. You go on LinkedIn right now, you can almost guarantee your feed has...
Connor: One of the first five posts is a GPT generated GPT post about how to use GPT.
Dylan: And then you saw, it was two, three Mondays ago, OpenAI talked about GPTs. I heard somebody call them GPTS yesterday and I'm not actually sure what, what they are.
Connor: IT's going to be the new GIF, GIF argument.
Dylan: Right.
Connor: I think GPTs is probably right. Right? Like that's, that makes sense. Like the pluralization, there's no S. Yeah.
Dylan: But in any case, you had that, which is like, OpenAI signaling, " Well, gee, we kind of should put more persona specific or application specific use cases around this technology." It benefits both the user and you can actually create a better model in that sense, right? Like you can, you can obfuscate or entirely remove information that is irrelevant to that use case. And what that does is like harden your ability to deliver really meaningful results for a specific use case. And that's exactly what GPTs are doing. They're just taking very specific use cases. Templating them, wrapping them, instructions, prompt language. Like all of that knowledge is based in baked into a use case. And so, that opportunity is like the exact one that ChatSpot is looking to capture. Folks in the industry, coaching one another on how to prompt giant, large language models, and then the industry itself moving towards a more templated way of building these interfaces. Well, ChatSpot kind of sits right in the middle there, with the beautiful added bonus of every HubSpot customer can now interact with their own data and their own system in that way.
Connor: When you think about who your end user is, is it, "I am a salesperson. I am curious about how to use AI to do what I'm trying to do better. And we have already thought about how you might do that. And we are going to give you pre-made functionality and tools to help you achieve that goal, whatever that goal might be?" When you think about who are we building this thing for, what's your avatar for it?
Dylan: Yeah. So right now it's folks who are using the CRM and helping make tasks and the things that they're doing day to day more quick and more effective. Over time you start to see actually like net new opportunities show up, right? Like a proactive assistant, an agent, all running through something like a ChatSpot interface. But right now it's, there's, there's so much opportunity to make, using, learning the CRM not only easier, but like plain quicker. Should you need to know the entire corpus of your accounts properties? And each value? Or should you just be able to like ask a question and kind of figure it out?
Connor: If I want to GTM with AI Dylan. If I want to do that, how does ChatSpot help me? What is the workflow or what is the user journey of, "I'm somebody and I'm trying to do X and how do you help me do that if I'm using ChatSpot?
Dylan: What are you trying to do?
Connor: I mean, I could give you a really hard one, but I don't want to completely curveball you here.
Dylan: Last time I watched Hot Ones on YouTube. Have you ever seen Hot Ones?
Connor: Yeah, yeah, yeah,
Dylan: Yeah, I think we should do that for the next interview. You should bring the hardest question with the hottest chicken wing.
Connor: We like match them in advance and then we come in and we're like, "All right, you got to get this one right. Let's do it."
Dylan: Yeah, now we're really sweating use cases.
Connor: When you think about who the persona you're solving for, is it mostly sales? Is it mostly marketing? Is it anyone who's doing anything?
Dylan: It's doers. It's just, it's folks who are in the CRM and need the CRM to operate in their role.
Connor: But ChatSpot lives, and this is something that like, I think is very interesting as an outsider. And I think I look at and evaluate something like a ChatSpot. And I think at the outset, prior to you and I having a conversation about this, actually, I was like, "Oh man, ChatSpot is this natural language interface for my HubSpot portal.” And what you just described in the way that you're speaking about it is, different than that, which is, "We're going to give this GPT layer, that is, attuned to what folks that are trying to engage in sales marketing and service activities. And we're going to give them a GPT that already knows that they're trying to do that, and it's going to help them attain that goal." Versus them needing to go and prompt their way to get something that's of value. ChatSpot does not live inside of HubSpot, it is separate.
Dylan: Today.
Connor: To me, right? When I think about it from my perspective, I'm like, "That's weird.” And, is that weird? And how is that intentional? Is it, that's a current state and maybe there's a world where it changes? Like, how do you think about it?
Dylan: Purely a current state. I mean, we're bringing ChatSpot inside the product.
Connor: Going to happen at some point. Maybe.
Dylan: Yeah. I mean, how's Monday sound?
Connor: What'd you say?
Dylan: Monday sound?
Connor: Amazing.
Dylan: We'll get you access. No the way we're thinking about ChatSpot and moving inside the HubSpot product is almost exactly the way we thought about building custom objects. From a, like a tactical sequencing perspective.
Connor: Can you actually, what would be super helpful is to, I think a lot of people, if you were not like in the trenches of HubSpot CRM before custom objects existed and before it was there. There was not a world where, and I think this is what you're alluding to, and going through like how you did this, and then maybe creating that parallel is interesting. But I think for anybody seeing this, if you were not in the trenches, there was not no custom objects, and then one day there were. Is not how this went down. And I think it's helpful, because I think it's actually a very good avatar for how HubSpot tends to build and launch stuff.
Dylan: Yeah. That's helpful context. Like it's not a switch that we will flip. It is a progression. It's a continuation. So what we're focused on right now is building something that can scale across the entire product at, I don't know, call it the platform layer. It's an overloaded term, but that's what it is. Something that both from a look and feel and interface perspective, can be used anywhere in HubSpot. But then also from a functionality perspective, I mean just the way that products are built, right? Like you have a team that builds a platform, and you team have teams that apply a use case applications. ChatSpot today has been a single unit trying to do both, right? And the way that we can achieve scale across a very broad HubSpot product now, is to build a platform and enable any team building anything. From blogging, to quoting, to social media, and anywhere in between to build atop that functionality. And so, what we'll see with ChatSpot moving into the product, is core applications sell first, some of the more popular use cases inside of HubSpot, the more visited frequent apps, the more, the bigger problems. And you'll start to see ChatSpot appear, help you with those tasks. And then over time, start to proliferate across the entire product suite, and you'll be able to see ChatSpot anywhere.
Connor: So, current state is ChatSpot is its own domain. ChatSpot is its own thing. It lives outside of HubSpot. You connect it to HubSpot. Very close future state. Tomorrow, maybe Monday, maybe later than that ChatSpot is in product. When you're thinking about how ChatSpot is in product and how people interact with it. Is this a Clippy plus type of experience that there's somebody, there's, there's this agent that's following me around and I can ask it stuff or how am I interacting with it?
Dylan: Yeah, well, I think you're going to interact with it very different than our, than our average user as a very professional. But what I'd like to think about it is like, imagine every HubSpot user had Connor sitting next to them. Somebody who knew HubSpot in and out, right? You know, you'll have to deal with some downsides, but there is upside having that, right? And what that will look like is, imagine I'm a brand new user or I'm trying to do something new, even in HubSpot. Like you can be a tenured user and there's always new arenas to explore. And I can ask an expert like Connor and say, "Hey, how do I do this thing? Or can you help me do this thing?” And instead of that requiring me to open up a knowledge based article, or I'm self discovering, like, poke around the app a bunch, maybe watch some academy content. Instead, what if it was just done for me? And I could just focus on the results and the outputs, and not learning the CRM. Or figuring out how my property structures are formatted. Instead, just focus on the outcomes and the things you're looking to do to grow.
Connor: Extremely interesting here. From what you had just said, because I agree with you. I think that this is the truth. What makes me so interested in what you're doing is I think you're manifesting this more than most of us that are talking about it, which is, my perspective is that all of these AI tools essentially make it so that knowing what buttons to click and knowing how to operate the machine is not a prerequisite to getting value out of the machine. And therefore, by extension, the value of knowing what buttons to click and the value of knowing how to pull the levers, goes significantly down and maybe to zero. And I would argue that, we'll start with like full time people and then i'll probably transition a little bit more like services generally and what that happens. But there are a lot of people, for HubSpot and for lots of other business software, that their core function, their job, the value that they add to an organization is knowing what buttons to click. And I would describe their primary function is they interface with a business user who does not know what buttons to click and does not know what levers to pull. And that business user describes to them, "Here's what I'm trying to achieve, or here's what I want to have happen." And then they go and click the buttons and pull the levers to make that happen. And I think the premise of what you're describing, and I would argue almost every AI tool in every application, and I think this is the right direction, is ultimately how do we make that user who doesn't know what buttons to click, how do we get them to their result faster? There is very real intangible disintermediation there. And what do you think, not so much to those people necessarily. How does that shift how people interact with the product? And what happens to the HubSpot-y world as that occurs?
Dylan: Yeah it's a great question. It's on a lot of folks minds these days. I think a few things will happen there. I mean, the overall sequence and timeline of all of that is probably much longer than most folks are thinking at this time, I would love for that. The nuance of an individual business' data and the way that they think about their operations is so incredible. What we'll see is the ability to use a pure non-customized HubSpot out of the box using nothing but your keyboard or even your voice. Well, that timeline is probably pretty short. But the ability to use a HubSpot account that is, has years of data and customer context, and customization, and external apps, and everything in between, like that timeline is probably like exponentially longer and potentially infinite. And so, like, I, I think the timelines are really interesting there. And like, depending on the level of business there, they're probably stretched out in some sense. And then the other part of your question is like, "What happens to the buttons or the way of using HubSpot?" I think it just becomes different buttons over time that are more creative like applications, right? Like instead of using HubSpot and the expertise is point and click, it's creativity and customer context, and strategy, and less point and click.
Connor: Yeah, I think when I think about it, that's a lot and I think what you're saying is right. And I'm going to talk a little bit about service providers just because I think that this matters. And I think people are either thinking about it a lot and maybe too much or not at all. And maybe not enough. I think there's like a lot of oscillation away from that, like, correct mean. But when I think about sort of what the AI tools do generally. And then my perspective, what products do generally and specific to sort of like digital products and software is, interacting with machines is hard and has been hard. And historically, you had to have many layers of technical engineering, software expertise, and in the original sort of developer days, it's not enough to say, "Oh, I can write a script of code and it does something." Like, how much ram time does that take it? Does the machine have enough processing power? And how is that wired? And all of that has to go into consideration. And I think you go to the example, which I love of how everyone was wowed by Pokemon existing on a single cartridge. And it was just so big for what should should fit there. And the reason is there's a lot of engineering behind reusing assets and making sure that everything actually can function on something so small. And I think most development today, because computer is so inexpensive, no one really cares or thinks about that anymore. And I think that this is kind of a further extrapolation layer of, we're making it easier and easier and easier for people without a degree of technical acumen to use the machine. Which means more people can use the machine, which means net productivity increases across the board. And, to your point on, on sort of that role of disintermediation, how do you think this impacts? I mean, in the HubSpot I was just looking up to try to go and see the number that I say expands because there's so many new HubSpot partners all the time. And there are 6,612 currently in the directory. And so there are 6,600 companies globally who have dedicated some degree of their business towards helping people know what buttons to click, and how to click them, and know what levers to pull, and how to pull them. And I, I would say that historically, and I think a large percentage of those are allocated to, "Let's help someone who's brand new to HubSpot, maybe brand new to CRM, start to use it." And there's like onboarding and the rest of it. How do you think that ChatSpot changes where they focus? Because I imagine what they're going to have to do changes a lot because HubSpot is going to be able to make it a lot easier for a net new customer to use the platform for the first time. And by no means does that mean all these people have nothing to do. It means that what they're doing and where their focus is changes. How do you see it changing?
Dylan: Yeah. I think, I mean, in my ideal, all 6,600 of those, those partners have higher order of problems to solve. And I think that looks a lot like building account specific GPTs using ChatSpot. And now we're, a few moons away from this, right? But like, imagine if your work went from, instead of customizing the way your CRM records work, or like instructing somebody on how to find the right filters, or drawing property diagrams or association maps. What if the higher order problems were like, customizing the AI for your account, customizing ChatSpot for your account. For ideal outcomes, for brand styles, for brand tones, for exactly the way that your business needs to use something like ChatSpot. Like, I think it actually opens a new door for all 6,000 of those partners.
Connor: Okay, so you see a world where there's not just a sales and marketing and HubSpot GPT. There is every organization has their own GPT and agent. That GPT and agent lives on and is inside of HubSpot. And there is a world of consulting and services providers who are doing what to make that agent useful.
Dylan: I mean, it boils down to something like customization. And you can look, you can look at basically if you take the parallels of how somebody would build a GPT today, you kind of like squint at those and see where, the future may lie for somebody doing that within a hub or a CRM instance, right? It's providing it, you know, key knowledge sets, stylistic instructions, core data sets, providing unique data, data unique to the business, connecting external data sources. And things like that, like I think that that becomes the...
Connor: Wild.
Dylan: Yeah, I think that's the exact paradigm.
Connor: Yeah. I mean, it, it makes sense. It flows, right? You think about the, the, if you do a tremendous distillation. The CRM at its core is effectively a bundle of software layers that lives between the business and the database and makes that database more interesting and useful. This is just that again. And, how are we going to not give you a CRM? What if you had a place that had all your customer data and had it all available to you? But what if you had an agent that had all your customer data and had all the context and knew it was happening, and every person in that organization is now 10, 100x more effective and efficient because they're just interacting with that thing and telling them what they want it to do.
Dylan: Now you can't go build that GPT without the access or the data store that HubSpot provides. Right? So if we...
Connor: How do you think that applies to... so I had a really interesting conversation with somebody that's relevant to the show actually. But I don't know what the order will be. So I don't want to reference it, but you'll hear it at some point. And, and they were talking a lot about how they're trying to do this for their organization. Like what you are describing as something that they are trying to do. And they're doing it from more of a product and an engineering perspective. But the big blocker for them is on security and data share, and if they go and connect to this, they're supplying that customer information to OpenAI and it goes into train the models, and they're concerned about all of these pieces. That seems to me from what you are describing to be maybe, an extremely important, if not the most important differentiation, from what you are doing and how you're positioning it.
Dylan: Yeah, yeah. So one thing I was talking to somebody the other day, actually a very knowledgeable HubSpot partner who didn't know that when you're using something like ChatSpot or any AI feature through HubSpot, whether we're using OpenAI or any other vendor that we're using, their models downstream are not trained. And that's like something we need to do a better job at it, screaming from the rooftops because...
Connor: I don't know that anybody really understands what all this means just yet in their defense. Yeah.
Dylan: Right. And so like, that is, that is a benefit to using an API versus the, the native product, right? Like if you're, if you're kind of working through HubSpot through OpenAI or any other vendor we choose to use, their downstream models are not trained on that data. The other long term security lever we have here is like, you can imagine HubSpot going down the route of creating fine tuned LLMs at the portal level and things like that. And at that point, the data is still within one place.
Connor: Do you think that the... Okay, so the future of businesses is everyone has their own GPT. It is connected to your data sources. It's trained on how you do things. And there is a whole universe of consulting and service providers and maybe full time professionals whose job it is to tailor and hone and customize that. And I keep using the word GPT and it's probably reductive. It could be AI agent, it could be a lot of different things, but to make that thing a lot more effective. How does that impact the future of how people, specifically people in these GTM roles, work? Because I think it's really easy for people to think about... Like if you, if you've watched anybody code, and they're using sort of the VS code, or the Copilot, or any of the GitHubby stuff that's going on. And you're like, "Oh, wow, this is suggesting stuff for people who are writing emails." And like, that seems very easily understandable to connect it back through. If an organization as one of these deployed, and it's really tied to the CRM, which things where people work, how does what I'm doing as a salesperson or a marketer or service person, like, what does my day look like?
Dylan: I think your day looks, your day looks similar to as it does now. And if your pie chart is 60 things you love doing and 40 things you need to do, it's probably more like 70 things you love doing and 30 things you need to do, right? Like I want to take the toil work out of using CRM or selling or marketing away using something like ChatSpot. How, how can we allow you to just do the things you love doing about your role? The thinking, the customer conversations and all that. And that looks like a proactive product. You know, finding you in the right moments and auto filling, making suggestions where you need them. But it also looks like a companion. We've been thinking about use cases where folks kind of like set their mental timers to do things of like, "Oh, you know, we should check in on these property definitions, make sure they weren't updated." Or like, you know, "Refresh these workflows," things like that. could you kind of just deploy an assistant like ChatSpot to to take care of all that, for you? I think so. And I, I hope that, the, shift of what somebody does in a given day can be more along the things that they love doing.
Connor: One last thing that I'd love to talk about before we wrap is something... I mean, we just did a bunch of AI research that we're releasing at some point, depending on when this airs. Might may have already come out, maybe coming out tomorrow, maybe Monday, as Dylan says. But I think one thing that we found is, people that have AI tools that are native to the platforms that they're working with, tend to report much higher efficacy and much higher outcomes. I think one of the things that makes, in my opinion, HubSpot very uniquely positioned to leverage this moment, is that HubSpot has a system and a tool that is a CRM layer with a whole bunch of functionality that is layered on top. As opposed to a bunch of independent software suites that solve for a particular use case that are then connected together. And I think that framework and that platform infrastructure allows for the impact of sort of an AI solution to be much, much higher because it's expanding across everything. As opposed to, you have to wire it into each place. If you have well thought out concepts and ideas on that in terms of speaking to why or what you think makes that different I'd be super interested.
Dylan: What specifically?
Connor: The thought for me, right, is like, if, I am another business software company, and I have a lot of different products, and a lot of different tools, and those tools maybe talk to each other, but they're talking to each other via APIs. But their fundamental data architecture is different, the systems are different, and that means if I'm going to go build and add AI to my products, I'm not building an AI for my suite of products. I'm building an AI that has to be tailored to every single tool that I, I sell. Versus, with HubSpot, all of these different hubs are just software functionality on top of the same core CRM. And therefore, my assumption is that the ease with which that, that AI can be deployed is much higher and the impact of what the AI can do is also theoretically higher.
Dylan: Yeah, I agree with that. I think the, the ease of deployment is, is mostly the same. But the impact and the efficacy has much more opportunity. We have much more opportunity there. The way I look at it is like the flywheel just got a lot of torque, the ability to have a unified data store and things that look, feel, and act the same. Our ability to deploy AI features and functionality atop something like that has the potential to deliver vastly, vastly different customer results. Then the need to, to kind of essentially like the story doesn't change to, to connect a bunch of systems, try to unify language there, right? The locus of centralized data, the flywheel of all business functions, GTM, OPS, all related, all living on that same core infrastructure, makes our ability to deliver those features really exciting.
Connor: I have one last unrelated question for you. Which is, I think if I think about what's the what's the most exciting and interesting job to do in in the universe right now? And if you think about if CRM platform is where most people are going to experience, if most people are going to experience AI at work, and the CRM platform is the place where most people work. Then building AI for CRM platforms is one of the highest impact AI things that you could do. And probably one of the most exciting things. And ergo, I think you specifically have one of the most interesting and exciting jobs that exists right now. What do you think is cooler than what you're doing? If you were to do anything else, what would it be?
Dylan: Actually there's a PM on my team. Her name is Rachel. She has a cooler job than me. She uh,
Connor: Dylan wants to be Rachel. That's, that's the answer!
Dylan: Uh, Rachel's a, uh, she's brilliant senior product manager. She's been at HubSpot for a few a few months now. And she leads our specific team called AI Innovation Labs. And they almost have a metric of failure.
Connor: Yeah.
Dylan: Can they experiment and try such cutting edge functionality? Take such big swings, and in hit a few, a few home runs along the way. That is, is the one gig I'd take over.
Connor: All right. That's, that's a good answer. Still at HubSpot, still in product, still in AI, but not necessarily having to build stuff that works and instead just...
Dylan: Just taking huge swings all day and just at some juncture, whether it takes a year, five years, 10 years, there will be an absolute home run out of that, out of that team that'll work. And I'm absolutely thrilled for what it's going to be.
Connor: Incredible. Well, Dylan, thank you so, so much for talking to me about this. I'm being incredibly genuine when I said I was absolutely looking forward to it. I think that you are an incredibly interesting and amazing and intelligent person and I think you are doing incredible things. And so just having the opportunity to talk to you about it is something I, I cherish for real.
Dylan: Thank you. Thank you. I appreciate it. And that feeling's mutual. It's been quite the ride. A couple of years, four or five years now, a couple INBOUNDs. We've gone from talking about, "Can we just please figure out how to change property permissions, to something as big as training custom GPTs for your portal?" So it's cool evolution.
Connor: The future is bright and, and we've had a good, we've had a good, a good track record of delivering on it. So we'll, we'll expect more of the same.
Dylan: Yeah, exactly.
Connor: Awesome. Sounds good, man. I'll see you soon.
Dylan: Thanks, Connor.