Learn. Share. Evolve…

0:00:07 Welcome to the Next Wave Podcast. I’m Matt Wolfe. And if you’ve been keeping an eye on how AI is
0:00:12 changing the way we work, you’re going to love today’s episode. I got to sit down with Nicholas
0:00:18 Holland, the head of AI over at HubSpot, where he spent nearly a decade driving innovation.
0:00:24 In this conversation, we explore the massive shift happening right now from AI as a simple
0:00:31 productivity booster to AI actually doing work on your behalf. We talk about this new frontier
0:00:36 Nicholas calls work as a service, the rise of the super contributor in sales and marketing,
0:00:42 and how agents, not just assistants, are quietly taking over repetitive tasks in real businesses.
0:00:48 You’ll hear a four-step framework for getting your team started with AI, why structuring meeting data
0:00:53 might be your biggest hidden asset, and what it means to become an agent manager in the near future.
0:00:59 It’s practical, it’s forward-looking, and it’s packed with insights. You’re going to love this
0:01:01 one, so let’s dive right in with Nicholas Holland.
0:01:11 Cutting your sales cycle in half sounds pretty impossible, but that’s exactly what Sandler
0:01:17 Training did with HubSpot. They used Breeze, HubSpot’s AI tools, to tailor every customer interaction
0:01:22 without losing their personal touch. And the results were pretty incredible. Click-through rates jumped 25%.
0:01:28 And qualified leads quadrupled, and people spent three times longer on their landing pages.
0:01:33 Go to HubSpot.com to see how Breeze can help your business grow.
0:01:39 Well, thanks, Nicholas, for joining us on the show today. It’s great to have you,
0:01:42 and excited to dig in and nerd out with you about AI. How are you doing today?
0:01:44 Good. Thanks for having me, Matt. Appreciate it.
0:01:47 Yeah, so let’s get into a little bit of backstory. I don’t want to go too deep on this,
0:01:52 but like, how did you get into AI? What’s your relationship with AI? And how did that sort of
0:01:53 evolve into what you’re doing at HubSpot right now?
0:01:59 Yeah, so I’ve been at HubSpot for almost a decade. I started off running the innovation labs out of
0:02:04 Ireland. Then I took over the kind of content and marketing portfolios and eventually became what
0:02:10 they call a GM. So just responsible for product and revenue over the marketing products. And AI has
0:02:14 always been something that, you know, customer platforms like HubSpot have had to do, but it’s
0:02:19 really been like on the machine learning side. Think of it simply as like scoring things. So lead scoring
0:02:26 has always been a big part of that. When OpenAI came out with 3.0, there was rumblings in the marketing
0:02:33 world about Gen AI. And so we started to look at that. ChatGPT comes out. And of course, the world
0:02:39 changes overnight. Everybody’s using it, talking about it. And we had multiple AI leaders. And for whatever
0:02:43 reason, they didn’t work out. There was a point where they were looking for an AI leader and three
0:02:49 years had gone by and marketing is being impacted the most, I think, by our has been. Right. And so
0:02:54 here we are trying to serve the marketer. And we had just put out a ton of value by applying AI on their
0:02:59 behalf. And so there was a point where do we go get an academic leader, someone who, you know, majored
0:03:06 in AI? And our product leader came to me and he said, I think we need somebody who actually knows how to
0:03:11 build things with AI. And we would love to have you do it. And I said, you know, my specialty is in
0:03:18 applying AI. So if we do this, we will become experts at applied AI. And he said, let’s do it.
0:03:23 And so I took over about 10 months ago. And since then, that’s really been how we’ve just began to
0:03:28 drive really deep on how do we apply? We’re model agnostic. We take, you know, a multi-model approach
0:03:32 and then we apply that. You know, we’re at a point now where we don’t even think AI is the limiter
0:03:37 anymore. It’s like, how do you actually bake the cake? Yeah, yeah, yeah. No, it’s interesting because
0:03:42 you mentioned marketing, right? My background was actually in digital marketing, but because AI became
0:03:49 such a sort of integral part of what marketers do now, I started talking about AI as it relates to
0:03:54 marketing. And then over time, AI got more and more popular. And I went, you know what? I’ll talk
0:03:58 about AI and marketing, but also just talk about AI because I just love this stuff. But I feel like
0:04:03 marketing is probably the area where I feel like AI has really sort of impacted things the most.
0:04:09 Huge. For the marketers that are embracing it, I’m seeing like a rebirth of marketing being fun again,
0:04:14 seeing a rebirth of like marketers kind of starting to find, you know, blue ocean again. That was like
0:04:17 the hardest part of like, if you roll back the clock, like two years ago, it was a red ocean for
0:04:22 all marketers. Everything had been figured out. All techniques were basically known. All the software
0:04:28 had started to become homogenized. Now it’s like blue ocean again. You’re seeing wild variances.
0:04:31 And creativity, tactics, all that stuff’s pretty, pretty cool.
0:04:35 Yeah. Yeah. I mean, all of the really cool marketing that you’ve seen over the years that
0:04:39 you’re like, oh, this probably took massive teams and, you know, tens of thousands of dollars to
0:04:43 produce has sort of been, you know, for lack of a better word, democratized. And now anybody could
0:04:49 kind of do the really cool marketing stuff that you’ve seen. And, you know, I love that. That’s what
0:04:51 brought me down the rabbit hole in the first place.
0:04:54 I don’t know how you see it, Matt, when you’re talking with others, but like there’s
0:04:58 like a new role emerging. I don’t know what other companies call it, but we call it like
0:05:04 the super contributor. In just the last week, I’ve seen a marketer who runs a prospecting team now.
0:05:09 So let that take in, like the marketer is now running the prospecting team and it’s because
0:05:14 they’re using agents and different AI things to get that done. So now you’ve got a marketer doing
0:05:18 some sales type roles. I’ve got a sales guy that I was working with. He does all of his own product
0:05:23 marketing work now because he’s using, you know, image creation, he’s doing videos,
0:05:27 he’s doing all that stuff. And so like, it’s just really interesting with all of these like
0:05:30 capabilities coming on. Like, what does it mean to be a marketer? What does it mean to be a sales
0:05:35 person? Like this super contributor is now rising where it’s really like if you have a lot of creativity
0:05:39 and you’ve put in a little bit of elbow grease to learn some of these things, you’ve got a couple
0:05:43 of new superpowers. Yeah. Yeah. I mean, you know, guys like Sam Altman
0:05:48 and Dario Amadai from Anthropic, they’ve made comments about how they think the first
0:05:53 one person billion dollar company is coming within the next couple of years, right? That’s because
0:06:00 one person now could be the builder, the marketer, the salesperson, the, you know, the customer support
0:06:06 person. They can do that all by using various AI tools and agents and things that are available to
0:06:11 them. So yeah, we’re entering a pretty exciting world right now. Yeah. It’s going to be cool.
0:06:18 So I want to talk to you a little bit about the shift of AI productivity to AI actually sort of
0:06:22 doing a lot of the work for you and being able to use AI to build system because in the early days,
0:06:28 AI, like when chat GPT first came out and even before that, when we had access to GPT three and
0:06:32 stuff like that, it was really great at being like a productivity booster in the sense that it can help
0:06:37 you write some emails and, you know, beef up your copywriting a little bit and some little stuff like
0:06:43 that. But now it seems like more and more people are actually using AI to sort of build out their
0:06:48 whole business system. So I’m curious, like, what have you seen in that realm? How are you seeing
0:06:53 this shift from productivity to like, now AI is sort of able to help run the show?
0:06:57 So about a year ago, again, like in the early stint where I was first starting to take over,
0:07:04 we started to notice that there’s like this shift from SaaS, software as a service, to what we think
0:07:08 is going to end up being WASS. People chuckle when we say that internally, because it can seem a little
0:07:16 cheesy, but like work as a service. And work as a service can look like, you know, we’ve even talked
0:07:21 about like, should HubSpot actually do marketing for people or do sales for people? It could go that
0:07:25 far. But what we think right now is that like, that is kind of the key differentiator between
0:07:32 technology of the past, like cloud or internet or mobile, and then AI. Those were all enabling
0:07:37 technologies that let you go basically faster. So it’s like, you heard the old phrase, Henry Ford
0:07:42 said, if I built what people asked for, I’d have given them a faster horse. You know, so we actually had
0:07:47 that internal debate. Are we providing faster horses? Or are we providing cars? And I think that
0:07:53 AI, when you start to shift to it, doing work is a lot more on that car standpoint, because
0:07:59 with cars, what came along, you had new types of professions, you had infrastructure changes with
0:08:05 gas stations and roads, you had kind of knock on effects of cities moving further out, right? I think
0:08:10 that’s the kind of equivalent that we’ll see with AI. And specifically, like everybody’s kind of in
0:08:16 love with the concept of agents. Agents are a type of AI that get closer to that concept of work,
0:08:22 that it’s basically something that’s doing a unit of work. I think for us, what we’re noticing now is
0:08:28 like, we group everything as an agent now internally. So even like assistants, like a chat GPT, they used to
0:08:32 go out of their way to say, like, we’re not an agent, we’re an assistant, right? But as chat GPT has
0:08:36 gotten more powerful, it’s doing more work, it’s doing more things autonomously, it is an agent,
0:08:40 it’s just an agent that interacts with humans. So we’re kind of seeing agents that interact with
0:08:45 humans as assistants. We’re seeing agents now that basically you give it a goal, and it’ll go knock
0:08:51 that out. So that’s more like a task agent or a work agent. And when you look at that, you know, now you
0:08:55 start to break it up into ways that like, one, you need to find parallels as a human to think about it.
0:09:01 So if you had a chief of staff or an executive assistant, they wait for you to tell them what you
0:09:04 need, right? Although some of the best ones anticipated, but you know, you ask them to do
0:09:08 something and they will go do it. You’ll say, Hey, can you take notes at this meeting? Can you
0:09:13 let me know about this next meeting that I have coming up? Will you book this for me? That is a body
0:09:17 of work that’s starting to happen now. And a lot of people are using their AI assistants to do those
0:09:22 type of things. So now we have companies all the time ask, like, how should I approach AI? One of the
0:09:29 first things we say is give every employee an assistant. We have one that’s very good for go-to-market
0:09:34 roles. So marketing, sales, and service. It’s powered by ChatGPT. It’s got all the context of
0:09:39 HubSpot. But like we say, give them an assistant and teach them how to start using that to basically
0:09:44 help themselves. The next thing though, we move into are the agents. And a lot of our customers
0:09:49 are still thinking like, what is an agent? How do I go about this, et cetera. And so we have out of the
0:09:54 box agents that basically are low lift, you know, it’s 15 minutes or less of time to have like a big
0:10:00 body of work. And so tier one support, not just of service, but also tier one support of marketing
0:10:04 inquiries, tier one support of sales inquiries. That’s like one of the agents we build. And then
0:10:08 going from that, once you start to do that, now people will start to get an idea of how it works.
0:10:13 And then they’ll start to build their own kind of special bespoke agents. So we had a customer who
0:10:18 wanted to build something called like an account handoff agent. And the easiest way to think about it,
0:10:22 they just had like this little pain in the rear that was a body of work that they couldn’t get
0:10:29 sales reps to do very well. Right. So I’m the sales rep, Matt, you are the service guy. You’re going to
0:10:34 deliver on what I just sold. What happens in a lot of orgs is I’m like, Hey Matt, I just sold this new
0:10:40 account. Please hook them up. And you’re like, I mean, what did you sell them? What did you promise?
0:10:44 Like, can I get all the details? And they’re like, yeah, yeah, yeah. You know, I’ll get those over to
0:10:48 you. Or they’ll say, can you go check out the CRM? And you’re trying to piecemeal all the calls
0:10:53 and all that stuff. So again, one of our clients was like, can I build an account handoff agent?
0:10:58 That’s brilliant. It’s hours through all the engagements. It understands what it’s trying to
0:11:02 get done. It then puts together an account handoff. And then it’s kind of like an ambient agent upon
0:11:08 deal being closed. It just now does the handoff. Like, so it’s very hard to describe that to like a
0:11:13 regular person without going deep, because you’re like, that’s a body of work that you didn’t have an
0:11:20 account handoff person, but boy, did you need it? And at the end of the day, that’s like why I think
0:11:25 now we’re not seeing like a employment collapse, right? That just makes that business so much better.
0:11:29 That makes that service person not hate that salesperson that makes the system now do this
0:11:34 body of work. And so that will then be like a net ad for that whole company. So as a long answer with
0:11:38 that, that’s like what we’re saying. Yeah. I love that. So basically you have the salesperson,
0:11:44 they’re putting all of their communications into their CRM, you know, HubSpot for all the
0:11:48 communications they’ve had with that customer in the past. Once a sale happens, then the person that
0:11:53 actually has to handle the deliverables, they basically essentially get like a quick report
0:11:58 on essentially all of the past communication. So, I mean, you might even have stuff in there about like
0:12:02 their birthday or like their dog’s name or something. And then the person that takes it over
0:12:04 can actually jump in and already have rapport.
0:12:08 Other thing that’s crazy too, is like, we all tend to think you’re the star of your own movie.
0:12:13 So everything you do is you think about it from your standpoint. We’re seeing organizations now where
0:12:19 on that one account, they had an issue. So they called into support. They then basically were talking to
0:12:26 team A who has product A about this. And then they bought product B right now. That is all very important
0:12:30 for like whenever you hand off stuff, because that continues the cross-sell upsell conversation.
0:12:34 You know what things they’ve been upset with in the past. You might have multiple stakeholders. So
0:12:39 it gets very complex really quickly when you begin to think through all the different possibilities.
0:12:44 But what I love about this is like, it’s a very, very human empathetic problem.
0:12:44 Right.
0:12:50 I’m now in this value chain. I would love somebody to hook me up with knowledge about how we got here.
0:12:54 And this person has already done their job. They don’t want to waste a bunch of time handing it off
0:12:59 to me. And so I love it because it captures the moment that we’re in that there’s real value here.
0:13:03 It doesn’t have to be a proxy for an entire job role.
0:13:08 The business is happier. The customer is happier. Like that’s just a very like empowerment moment.
0:13:13 Yeah. Yeah. You didn’t cut any humans out of the mix. You just made the humans involved a little bit
0:13:15 more efficient at what they’re doing.
0:13:15 Totally.
0:13:22 Real quick. If you’re enjoying my conversation with Nicholas, you’re going to love this.
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0:13:51 link in the description. Now let’s get back to the show.
0:13:56 So I want to jump back to agents for a second. There’s a bit of a debate, right? About like, how do you
0:14:00 actually define an agent? I’ve actually seen people get really fired up about this over on X for some
0:14:05 reason about how you actually define an agent and people getting into arguments about that’s not an
0:14:09 agent. This is an agent. So I’m just curious, like, how do you guys actually define an agent? What’s the
0:14:11 difference between that agent and the assistant?
0:14:17 Boy, we get fired up internally as well. One of the ways that I’ve learned to navigate this is,
0:14:24 you know, inflexible thinking will be a real detriment to people during this AI phase. I think
0:14:30 flexible thinking is going to be a real boon. And so it’s not a, is agent or is not agent. It’s more
0:14:38 like a spectrum on this realm of autonomy. I think with an assistant, we use that term to just
0:14:44 represent the interaction mode. Like its primary job is to interact with a human. When we talk about
0:14:49 these kinds of task agents, their job is to knock out a task. You know, there’s another one we talk
0:14:55 about internally as like a customer agent. Their job is to like work with third parties, not in the
0:14:59 company external. So you have different kinds of rules of engagement with those. All of those are kind
0:15:04 of on different realms of autonomy. But in general, I think the easiest way that like I’ve been able
0:15:13 to put it together in my brain is like the fact that it can have some autonomy agency is really where
0:15:17 this comes from. The fact that it can have some agency and begin to do work is when it starts on
0:15:23 that spectrum of basically being an agent. The debates we have internally is in this, I don’t
0:15:30 want to make anybody’s mind blow. But it’s like, if you go and you ask ChatGPT, Breeze,
0:15:35 Anthropic, whatever, if you say, make me a blog post, there’ll be a lot of people who will die on the
0:15:40 hill saying, that’s not an agent. That’s just a prompt. I say, okay, great. Now rock with me for a
0:15:46 minute. So then I’m like, okay, if I ask only Breeze can do this, but if I say, Breeze, make me a blog
0:15:52 post, and it now does the same thing the first one did, but it also is context aware of all of your
0:15:56 brand guidelines. Is that an agent? There’s still some people who will die on the hill saying, no,
0:16:00 that’s not an agent. But then for the first time, I’m like, well, think about it. It’s now made on
0:16:04 brand content. That’s hard enough to even get like one of your staff members to do that.
0:16:13 So then you get into, if I say, Breeze, make me a blog post on brand and generate some images for it.
0:16:17 This is why I think ChatGPT has become more flexible in their definition of it, because
0:16:23 now it’s done the writing, it’s got now some brand application, and it’s done an image that’s related
0:16:28 to it. That’s like really three jobs that used to be a copywriter, a media slash image, you know,
0:16:33 layout person, and then of course the editor who would put it on brand. So this is what makes it so hard
0:16:38 is because at what point does it become an agent? And so there are a couple of camps.
0:16:44 One is if it’s doing any work on its own, so that would be all the way back to just the prompt. As
0:16:48 the prompts get, models get better, like that will work. Some will basically go down the path of it’s
0:16:54 an agent whenever it effectively has a goal, can make tool selection on its own, and has context.
0:16:58 That’s like our agent leader internally right now. That’s his whole thing is like, it has to have
0:17:03 multiple tools available to it and they can do that. And so I think right now that’s why people get
0:17:07 fired up about it is because the floor is moving up. The models are getting better and better. Like,
0:17:12 I don’t know when ChatGPT5 comes out, but like there’s some rumors that like, it’ll now just be
0:17:17 a super omni model. Like it’ll just be one. You won’t choose a reasoning model. You won’t choose a
0:17:22 writing model. You won’t choose an image model. It’ll just be that. So that model will become more
0:17:27 agentic in nature. And then also over time, you’ll give them more and more tools that they can choose
0:17:32 from. So yeah. Anyways, that’s a hard debate. It’s a trick question for anybody that you get
0:17:36 in this space. But like, that’s just some of the flexible thinking I have along that spectrum.
0:17:42 Yeah. Yeah. I mean, it’s very similar to the concept of AGI, right? Like, are we at AGI already? If we do
0:17:47 hit it, will we know if we do, will the goalposts move? And also depends on like, like you said,
0:17:52 the threshold, like there are some people out there that are smarter than others. We probably got some
0:17:58 AGI based on those standards. Right. Exactly. Exactly. I mean, if you go back to like,
0:18:03 you know, Alan Turing or someone like that, back from like the 1940s, I think he would probably say
0:18:07 like, you guys have AGI already. If you were to ask somebody back then, right? Yeah. If you were to look
0:18:12 at me in high school, how smart I was then now, we had AGI. Like I was, that was definitely, uh,
0:18:17 AI is definitely smarter than I was back then. Yeah. Yeah. Yeah. I mean, when it comes to agents,
0:18:21 the definition that I’ve heard quite frequently is that it needs to work through multiple tasks,
0:18:26 but then also it should have the ability to kind of like double check its work. So if it
0:18:29 notices that it made a mistake, it should be able to realize it made a mistake and then go back and
0:18:34 double check it. And one thing that I found really interesting is that you can actually get models
0:18:40 like O3 and O3 Pro, some of ChatGBT’s models to sort of act like agents right now with a single prompt.
0:18:44 Absolutely. I was giving it prompts yesterday where I was telling it to, you know, come up with five
0:18:50 solutions for this problem and then analyze all five and then pick the best result from the five
0:18:55 solutions that you came up with. I mean, that is going through and really thinking through all sorts
0:19:01 of avenues and then deciding on the best avenue. You can also tell it things like, you know, solve this
0:19:07 problem for me. And then once you’re done solving it, review everything you just wrote, find any logical
0:19:12 fallacies with what you just wrote, and then rewrite what you just wrote to fix any of the logical
0:19:17 fallacies that you just found. That one prompt is acting like an agent for you because it’s double
0:19:22 checking its work and then fixing the double checking of it. So it’s like, yeah, I mean, like you
0:19:28 mentioned, it’s just a broad spectrum. I’d say like some people would consider what O3 does, where it will
0:19:34 work through and pick tools to use to help you get to a solution. Pretty agentic already. Totally. Yeah.
0:19:39 And then that’s at the model layer. You know, now what people don’t know is like in one chat thread,
0:19:45 you might ask it a reasoning question. You might ask it a basic knowledge retrieval question, some
0:19:49 knowledge already in the LLM, a fast answer. You might ask it to do an image. You know, that’s why
0:19:53 they call them the O models because they’re omnimodels. In my opinion, we’re already way down
0:19:59 the agentic path. So I spend less time haggling over what’s an agent nowadays. And now more to your
0:20:05 point, I’m much more fascinated by the work that it can do. And so for our AI now, and we’re still early
0:20:11 days in this, but like, I want the assistant, the breeze assistant, its mission is to be like your
0:20:17 executive assistant, your chief of staff. That’s it. And so that’s all we obsess over. And so we’re doing
0:20:21 like really cool things like with that, where like it knows who you are, memory and all that stuff.
0:20:25 It basically knows your role, but it’s also working really hard to like understand your calendar.
0:20:28 It’s working really hard to understand what tasks have been assigned to you. And so
0:20:33 that to me is like, when we start to get to work, there’s that. And then on the agents, you know,
0:20:37 that we just talked about a minute ago, like the account handoff or the sales prospecting agent,
0:20:42 like it’s something where there’s a body of work that it is particularly good at. And that’s what
0:20:45 we’re doing. So we’re trying to just really relentlessly find those bodies of work,
0:20:49 those use cases, hammer those out with agents. And then I think what’s coming next
0:20:53 is I think everybody’s going to be overloaded with agents. That’s my next theory.
0:20:53 Yeah. Yeah.
0:20:58 You know, a hundred agents working for you. Sounds cool. You’re that billion dollar one person company.
0:21:02 I’ve been on the flip side, like, man, what does it look like to manage a hundred agents? Who knows?
0:21:07 Yeah, that’ll be interesting. I mean, I’ve always liked the analogy of like a conductor,
0:21:10 right? Like you’ve got like your symphony and you’ve got your flute player and you’ve got your
0:21:14 violinist and you’ve got that. And then you’ve got the conductor sort of conducting the whole thing.
0:21:19 And I feel like we’re slowly moving to the role where most people get to be that conductor and
0:21:23 they get to conduct all sorts of different instruments and the different instruments know
0:21:26 what they’re supposed to be doing. And they do that thing. Well, that’s right.
0:21:31 I think what really excites me about the future of AI is when AI gets a little bit more proactive.
0:21:36 I think there’s a lot of people right now that they sort of avoid AI. They don’t want to use AI that,
0:21:40 you know, the ethics behind how it was trained and, you know, all sorts of reasons. Right.
0:21:45 But I think once AI starts to get like more proactive, where it’s looking at your emails,
0:21:49 looking at your calendars, and then like maybe one day you just get like a message on your phone
0:21:55 saying, Hey, Nicholas is trying to schedule a call with you. I see that you have this date open on
0:22:01 your calendar. Would you like me to respond to him and book it in for that time? Right? Like once agents
0:22:05 start doing that, I think the world is on board. I think it’s like, okay, I literally have a personal
0:22:10 assistant in my pocket. Now we’re close. Yeah. We’re closer than people think on that. We’re spending a
0:22:15 lot of time thinking about like the primitives or like the low level things. So there’s this concept
0:22:19 that’s come up of these things called ambient agents. They are proactive. Like you said,
0:22:23 the concept there is like, they’re listening to all signals at all times and then they know what to do.
0:22:29 So we have a pretty robust automation platform at HubSpot that kind of is baked into the whole
0:22:33 platform. So everything that we do at HubSpot, it’s like one unified code base, everything we do
0:22:37 translates to all of our product lines. So we’ve got this automation layer that touches everything.
0:22:43 And so when we were building agents, we originally started off with like, give it a goal. So just think
0:22:47 of like a master prompt. Then people wanted to give it a lot of data and context. So this was like,
0:22:53 how do you feed it a bunch of information? Then they wanted tool selection. So can it email,
0:22:56 can it web search, can it do that? So we did all that. So that was working really good. But then
0:23:00 people were like, how do I run this thing? And you’re like, what do you mean? It’s like right here,
0:23:04 you click run and they’re like, man, I’m not going to go into your agent page and click run every
0:23:08 time I need it. Like this is supposed to do a job and it should do it at this moment.
0:23:11 Right. This was like a really good epiphany for us. We’re like, oh my God,
0:23:16 think about it. Like this, it does a job, but all of us have a signal, do it at this moment.
0:23:23 And so we basically pulled the automation platform into the agent builder so that for an example,
0:23:28 you know, again, I go back to something simple, like account research. The first time a form is
0:23:33 filled out, you can go do account research. You know, the account handoff, the next time it’s marked as
0:23:38 sold, it can go do the account handoff. And then there are other events that we listen to. So it’s not
0:23:43 just on objects changing status, like events. So like you can actually say when person visits this
0:23:48 page and we know with our customers, like when they visit. So now whenever Matt visits this page,
0:23:52 it can actually kick off a whole stream of like, write an email, Matt, so good. Where you been?
0:23:58 Haven’t talked to you in forever. And like, so those ambient agents are here now. Again, I’ll go back to
0:24:03 how good these are, is going to be less and less about the AI and more and more about like,
0:24:09 who is the chef cooking the meal right now. Right, right. So I want to chat a little bit about real world
0:24:15 use cases. Like what are some things that like business owners listening, obviously HubSpot has a
0:24:19 very large audience of small and medium sized businesses. So that’s primarily who’s probably
0:24:24 listening to this podcast. What are some like real world use cases that people can do like right now
0:24:30 today using AI to help their business, whether it be, you know, other AI tools or HubSpot’s
0:24:33 internal tools, just like what would be some of your best advice right now?
0:24:38 Well, first I do think leadership needs to make a decision. What you’ll find in a lot of companies
0:24:43 is that they’ll have, you know, some forward thinker innovator on their staff who’s dabbling
0:24:48 and playing with this. But until leadership makes a decision that they’re going to embrace this kind
0:24:53 of future of work, it’s very hard for a company to move forward. So that’s first, I think you should
0:24:57 have top down buy-in. Once you do that, the very first step that needs is that I think every employee
0:25:02 needs an assistant. It can be a Gemini if it comes with your Google account. It can be
0:25:08 ChatGPT if you want or Claude Anthropics good. And then of course, we have Breeze for people who have
0:25:13 HubSpot and Breeze again is just ChatGPT powered plus all the contacts. But everybody needs that
0:25:18 assistant. And then to your point, from that point forward, they should start learning how to interact
0:25:21 with those assistants. So that’s just like your regular old prompts, like what’s the weather?
0:25:26 Tell me I want to plan my trip to Bermuda. You know, can you help me brainstorm on this marketing
0:25:30 idea? I’m about to contact this customer. You know, they have these objections. How would you
0:25:34 handle it? It’s like your thought partner. Once you do that, the next thing I encourage companies to do
0:25:40 is a little strange, but it’s like get all of your unstructured data into a central system. So
0:25:47 if you’re using a Fathom, a Firefly, HubSpot has a note taker, get everything, all the meetings,
0:25:53 all that stuff recorded, get it into a system where now the assistant has access to it,
0:25:58 future agents have access to it. That’s really critical because there’s like 80% of a company’s
0:26:02 knowledge is locked up in phone calls and meetings. So now you’ve got every employee has an assistant.
0:26:07 You’ve got your data now unified with unstructured. And that’s the first time that you should now start
0:26:12 to choose an agent. I think you should start with an out of the box one first. The one that I think is
0:26:17 going to be most ubiquitous, kind of like websites, kind of came up in the like 90s, late 90s. And it was
0:26:22 weird when you first had a website and then became weird if you didn’t have a website. I think right
0:26:27 now it’s like in that early phases, it’s kind of past weird, but it’s like people are starting to have
0:26:32 agents that man the frontline support. And it’s because they never sleep. You know, they’re helpful.
0:26:37 They’re not trying to rush you off the phone. They’re not trying to really like turn you into a sales lead.
0:26:41 You know, there’s all these kind of perverse incentives whenever you’re having a tier one support.
0:26:46 But hiring a customer agent, like for HubSpot, all you do is like point it to your website.
0:26:51 If you have a knowledge base, you point it to your knowledge base. If you have some files you want
0:26:55 to give it, you do that. And then that’s it. It’s literally those three things, like upload a few
0:26:58 files, point it to your website, point it to your knowledge base. And then we’re seeing that it can
0:27:04 resolve, you know, roughly 55 plus percent of resolutions. That saves a ton of time for your
0:27:08 employees to now work on other stuff, escalations, all that.
0:27:10 And it still has like a handoff kind of thing as well.
0:27:17 It has a handoff. So if you say, I’m not happy with you, you know, escalate me, it’ll do that.
0:27:21 The other thing that it can do is like start to find out if you’re like, want to talk to a salesperson
0:27:26 and you want to route it to sales. So you can have a very like informal conversation about products and
0:27:31 pricing and packaging, all that stuff without feeling rushed. And then of course, when you’re ready,
0:27:35 it can do that. So that’s the kind of the first agent that I recommend people do.
0:27:40 And then after that, I recommend that they then begin to bring their people along on what I kind
0:27:45 of call like AI fluency. This goes in a bunch of different directions, but like carve out time,
0:27:49 like one day a month for them to mess around with this stuff. There’s like educational stuff that
0:27:54 HubSpot provides, like help them come along and you’ll turn around twice. And then in a year,
0:28:00 your people are fluent. All of them have an assistant. You’ve got an agent or more working for you.
0:28:05 And then the software that you use day in and day out, if it has AI features, like HubSpot has like
0:28:09 over a hundred AI features just baked in, they’re not scared to click it. We noticed a bunch of people
0:28:14 like AI features are coming into all these software tools. And a lot of people were like, I don’t know,
0:28:19 I don’t know what it does. I don’t, I don’t know how much it costs, you know, like I’m busy. And so
0:28:24 that’s what we think like an AI leader looks like their data game’s on point. Their employees are on
0:28:27 point. Their agents are working. I think that’s it.
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0:29:10 Yeah, I like that a lot. I think one of the things you mentioned that it’s been sort of a game changer
0:29:16 for me is I’ve started recording a lot of my meetings. And if you actually have the chat GPT desktop app,
0:29:20 they actually have like a record button now too, where you don’t record your whole meeting. And then at the
0:29:24 end of it, give you a summary, give you any to do items. So I just actually hit the little record
0:29:29 button on my chat GPT and I don’t take handwritten notes. I’m just like very focused, engaged on the
0:29:35 conversation. When the meeting’s over, I go, all right, what did I say I was going to do? What did they tell me I
0:29:36 needed to do? What are they going to do for me?
0:29:43 What are my action items? And, and those threads, a lot of people, again, this is like, we’re all pretty like new to this
0:29:50 game. But like those threads in chat GPT are kind of the same. Like we learned in like a breeze. It’s not
0:29:54 just that moment. People chat with it right now. But like, let’s say that like you and I are in a sales
0:29:59 engagement. We’re hanging out, we’re talking, you did that recording thing. And then let’s say I go cold.
0:30:02 It happens all the time. Like we don’t talk for two or three weeks. And then I’m like, Matt, can we get
0:30:07 back together and chat again? You’re like, what the hell? Like, what do we talk about? And you can go back
0:30:13 and ask questions of that conversation. Yeah. This is like a new muscle that like tools like
0:30:18 granola. If you haven’t seen granola.ai or, you know, like these are new muscles that we’ve never
0:30:21 even had before, which is wait a minute, wait a minute, wait a minute. It’s not just a transcript.
0:30:26 It’s not even just like some structured follow-up or task outlet. It’s like, I can go back and be like,
0:30:29 what did Matt say about this? How did he feel about that? Like, that’s crazy.
0:30:34 Yeah, it’s wild. I mean, you know, I think back to, you know, whatever, 15, 20 years ago, right?
0:30:37 Everybody knew everybody’s phone number, right? Like I knew all of my neighbors,
0:30:41 phone numbers, my parents’ phone numbers, like all of my best friends’ phone numbers. I had them
0:30:45 all memorized today. I don’t even know my mom’s phone number. I couldn’t tell you what it is.
0:30:51 It’s all saved in my phone. Now. I don’t need to remember that. I’ve sort of outsourced that piece
0:30:55 of my brain a little bit. I know. I’m not saying people should like outsource like all of the
0:31:00 conversations they have, but it’s almost like that same thing of like, now we have the ability to
0:31:05 remember like every conversation we’ve ever had in the same way that we have the ability to
0:31:10 basically have storage for every phone number, every contact, every connection we’ve ever learned.
0:31:13 And I mean, you could do it in the real world. You’ve got things like the limitless pendant and
0:31:17 the B that just got acquired by Amazon and, you know, tools like that, where you could just be
0:31:22 walking around in the world world, keeping track of all your conversations as well. And so it’s really
0:31:28 an interesting world to be able to sort of like outsource our memory. Like I do have concerns that
0:31:32 maybe people will outsource too much of their memory. So I still think it’s important to be very
0:31:36 engaged in the conversations, but it is very cool to be like, I had a conversation with him two months
0:31:41 ago. What did we talk about again? And then, you know, jump on two months later and be like, Oh,
0:31:45 how was that birthday party? You were going to the, you know, the day after we talked or whatever.
0:31:49 It’s really cool that we have that sort of superpower now that we didn’t have before.
0:31:52 You know, it’s funny. The other thing I’ll tell you is like, I think there’s a lot of fascinating
0:31:57 anthropology type things going on here. So two months later, you asked me how the birthday party is
0:32:01 in today’s world. If I’m not super into AI, I’m blown away. I’m like, Oh my God,
0:32:08 it’s amazing. He remembered like, what a thoughtful, incredible person. And like over in our lifetime,
0:32:12 our younger kids may not be like in our lifetime, there were people like that who were just like, so
0:32:19 like thoughtful. I think now, if you said that I would probably know that you had some, you know,
0:32:24 what’s funny. This is where I think is awesome. I wouldn’t care. The fact that you asked me about it
0:32:30 is meaningful. We talk about it. We build a little rapport. And I go back to, again, the Delta that
0:32:35 only a few extraordinary people would have remembered the birthday. Now it’s something that we all have.
0:32:39 And so now you and I can have that moment. Let’s say that if that’s not your superpower to remember
0:32:43 that stuff. And so there’s just a lot of like things where I think that like the threshold for
0:32:47 humanity or the bar is going to move up in a good way, which I think is pretty exciting.
0:32:52 Yeah, for sure. There’s something that you mentioned before we hit record. You mentioned context
0:32:56 engineering and I hate to admit it, but that was the first time I’ve heard that term.
0:32:59 Can you explain what that means? What is context engineering?
0:33:05 Yeah. So the AI journey that a lot of, you know, us who are like in the AI industry, maybe you too,
0:33:10 Matt, it’s like you learn about a prompt. Then you learn that like the human language has a massive,
0:33:14 you know, capacity for like communicating information. So everybody was really learning
0:33:21 about prompt engineering. And then there were some prompt styles that came out. There’s a great YouTube
0:33:29 video on a prompt structure called craft. C stands for context. R stands for role. A stands for the kind
0:33:35 of action. F is the format that you want it in. And T is like the tone. And A can be for the action
0:33:40 address for the audience. Anyways, point being, you used to be like a master at prompting, you would fill
0:33:47 all that out. But what has happened is that to really get good AI results, it takes a lot of work to give
0:33:53 the right amount of context. So we started to see a body of customers that naturally on their own would
0:34:01 have like the mega PDF file. The mega PDF file would be about Matt Wolf, about his podcast, about what he
0:34:07 cares about. It’s like this mega PDF file. And then you would bring that into every AI interaction saying like,
0:34:12 hey, I’m looking for an idea. I’m looking for a script. I’m looking for this. And here’s all the context.
0:34:18 Well, where things started to get wild is that there’s a huge variance in the context that you bring to all of these
0:34:24 AI things. And so there’s a lot of effort right now in this world of context engineering, which is, you know, you’re
0:34:29 trying to make a podcast on HubSpot. You can make a lot of content on HubSpot. A lot of the content right now, if you
0:34:33 don’t give it the right context, even with great prompting, it’s not you. It doesn’t sound like you
0:34:38 right. It doesn’t know your history. It doesn’t understand all these things that you’ve done in
0:34:45 terms of previous guests. And so we started to see that like context is a layer above data. So everybody
0:34:51 used to think data was this moat. Data is getting easier and easier to get very hard, especially like
0:34:56 with customers wanting to own their data. It’s very hard to treat data as a moat. But context is a way that
0:35:01 you basically pull signals out of that data. And the way we first stumbled across the first
0:35:06 kind of context thing was companies using HubSpot kept saying like, I don’t want to have to keep
0:35:12 telling it about the products I sell. I don’t want to have to keep telling it about who our competitors
0:35:17 are. I don’t want to have to keep saying something like who’s our ideal customer profile. These are all
0:35:23 reasonable things that a business would want a new employee to know. This is what we do. This is who we do it
0:35:28 for. This is why we’re unique. This is who we sell to. And so we were like, dang, so people were now
0:35:32 having to constantly copy and paste or upload that mega PDF file with that. And we were like, why don’t
0:35:38 we start to create all that context in a layer that they can do that? And so we first came across like
0:35:43 a brand context. Then people were like, I wanted to know a lot about me. I’m Nicholas. I’m an SVP and
0:35:51 product. I’m over AI. These people report to me. These are my peers. This is like what I do memories
0:35:57 about me. So then we built out like a user context model, you know, now I want to sell to Matt. Matt’s
0:36:02 a prospect. And so, you know, you go look at his LinkedIn page, you go searches his blog, you know,
0:36:07 you go find them on Reddit. And so you then build out like a whole context profile on map. That’s a buyer
0:36:13 context. And on and on and on and on. And so we started to find out all these contexts that really were
0:36:17 better than people carrying these mega PDFs. And you now bring those to the table. So what’s
0:36:22 happening in a lot of the AI leadership stuff is that anybody who’s working on AI solutions,
0:36:27 including, I think, OpenAI and Anthropica, I think this next year is going to be all about
0:36:31 context and memory. I think people are going to obsess over that because it will ultimately make
0:36:35 all of the agency, all the agents and things like that just so much better.
0:36:39 Yeah, I totally agree with that. I mean, I think the thing that excites me the most about these new
0:36:46 models that keep coming out, it’s less about, you know, it got a 99.3% on this benchmark instead of
0:36:51 a 99.1%. Isn’t that amazing? Like those kinds of like benchmark leaps, like they’re, they’re
0:36:55 impressive. Don’t get me wrong. Like, cool that you did that. But from like a real world consumer
0:37:00 that’s using it all the time, I love seeing the increasing token count. Like I love seeing that
0:37:06 Gemini now has 1 million tokens that I can use. And supposedly they’ll be making 2 million tokens
0:37:10 available soon. Like that’s what really, really excites me because then you get these opportunities
0:37:15 to really bake in even more and more and more context. And, you know, before we hit record,
0:37:20 one of the things you mentioned is like, what is defensible? Well, like, like you mentioned,
0:37:25 a lot of the data isn’t defensible. Almost anybody can acquire data at this point, but like the context
0:37:31 that you bake into the models that you’re using is, I guess, what is a lot more defensible these days.
0:37:36 I know for me, like I have the YouTube channel and I do a lot of like ad integrations. I’ve actually
0:37:43 over many, many, many, many months dialed in the exact way I want my integrations to sound and how
0:37:47 they want to be worded and the exact sort of like structure of them and the beats that I want to hit
0:37:53 on them. And how do I get them under 90 seconds? And I’ve sort of trained over months, exactly how
0:37:58 those integrations should go. Now it’s gotten to a point where a sponsor will give me an ad brief and
0:38:02 say, here’s what we want you to talk about. I literally copy and paste that brief into my sort
0:38:09 of pre-built. I use ChatGPT projects for this. So I throw it into that project and it just like spits
0:38:15 out the perfect integration pretty much 99% of the time where I barely have to do any tweaking and nobody
0:38:21 else has access to that specific context that I built up over time. Listen, if somebody were to ask you
0:38:27 to change now, like you’ll be like, you’ll pry it from my cold dead hand. And so I think that pick
0:38:33 that, maybe you do a particular outro, maybe over time you have some sort of quick hits that are a
0:38:38 summary of this. Like you’ll build up three, four or five of these type of, you know, they’re like
0:38:43 sub assistants or kind of, you know, personal assistants, you know, custom assistants. You’ll build
0:38:48 those. They will have the context that you’re looking for. And it really does then become
0:38:53 a virtuous loop. The more you use it, the more context it gets, the more you dial it in.
0:38:57 And then it’s very, very hard to then switch. That’s, that’s why it’s great for the vendor.
0:39:02 And it’s very, very hard for you to switch. And so, you know, I was laughing, um, if you were back,
0:39:05 you know, starting a product, if you ever started a company, like one of the things you hear is like
0:39:08 something you build has to be 10 times better to get people to switch. I don’t know if you’ve ever
0:39:12 heard that, but like, yeah, I’ve heard that. Yeah. The thing is, is like in the world of AI right now,
0:39:16 a lot of people are switching without just like whatever’s the latest, the newest, you know,
0:39:20 those swap, they’re like very, very easy come easy go. But I am seeing as people get more and
0:39:27 more of that context engineering down, that that bar is now moving up to five times, 10 times. And
0:39:30 there is a world where it knows you so well that it might be harder than 10 times better to get you to
0:39:34 switch. No, absolutely. I think another thing that’s really interesting about the world we’re
0:39:40 going into now is that even software is kind of gotten to this point now where anybody can create
0:39:46 like little tools that they need for their business. I think we’ve entered this place now where if I
0:39:51 have a little like bottleneck in my business that I just find myself, this is something I do every
0:39:56 day. It’s repetitive. Why do I keep doing this? I can literally go to one of these tools, have it
0:40:01 generate a little script for me and just take that off my plate. And I even feel like software and like
0:40:06 writing little apps is no longer really that defensible because I can go and find an app that I really
0:40:10 like. I’ve done this on past videos where I went and made a Spotify clone just to see if I could.
0:40:16 Right. I went and made a Feedly clone just to see if I could of some of these tools that I use in my
0:40:24 home life. And now I don’t even feel like having a software is even that defensible. So just really
0:40:29 interesting. I have a controversial take on that. Let’s hear it. I do think you’re right. For some
0:40:35 people, it will be hard to be defensible. However, this is like an interesting take the other way.
0:40:44 I think you have the same access as I do as others to go by paintbrushes, a canvas.
0:40:51 But what you paint, what I paint and what a master painter paints is wildly different. And so I think
0:40:56 that the part that that I’m also, you know, somewhat attuned to is that there’s no accounting
0:41:02 for taste. So I think there’s another thing that’s kind of fascinating happening in the world of AI tied to
0:41:07 what you said, which is you have a little problem. There’s no solution out there. This is when I talk
0:41:12 about the bar for humanity gets better. Like you solved it. It’s awesome. No harm, no foul. There’s
0:41:18 another one where there’s a tool out there. There’s not a lot of competition. Maybe the price is too high
0:41:22 for the value you get. This is going to be one where you will want to build something. And that’s going to
0:41:27 be a fascinating world for those types of things. There is another world where it’s just so easy.
0:41:32 You don’t even look for an alternative. You build it. And I’m seeing this where people now love the
0:41:38 tools they build. Like there’s a psychological thing called the Ikea effect, which is you built this table
0:41:45 from Ikea. You love this table, Matt. This is your table born of your own two hands. Your friend comes
0:41:51 over and he sees a table with a crooked leg and a chip in the side of it, but you love this table. And so I think
0:41:55 there’s going to be a lot of Ikea effect happening right now where a lot of people are building these
0:42:01 apps that they’re very proud of that do the job, by the way. That is key. It’s like not a project. It
0:42:08 does the job, but it’s also nowhere near commercial software. It’s not high taste. It’s not great. And so
0:42:13 there’ll be a whole world of these like personal projects that have been enabled, but I still think
0:42:18 there’s no accounting for taste. There will be room for some of these high quality, high craft type tools.
0:42:21 So again, the competition field out there is going to change quite a bit.
0:42:26 Yeah, no, I totally see that. I mean, most of the little apps that I’ve built are ugly as hell,
0:42:31 but they solve a problem for me and I would never try to sell access to them. They just help me with
0:42:37 one thing, you know? Well, I know we’re running out of time here, but I do want to ask about like HubSpot
0:42:41 and like, what are some of the like really cool AI features of HubSpot that you’re excited about right
0:42:46 now? And are there any sort of upcoming AI features that maybe you can tease a little bit,
0:42:48 anything exciting to talk about there?
0:42:54 I think I’m excited. We re-architected the Breeze Assistant to go a lot deeper with OpenAI
0:43:00 and to bring a lot more of the HubSpot context. So for a salesperson, a marketer, a service person,
0:43:04 I’m just pretty excited because I think, I don’t know how you feel, Matt, but like there’s a huge
0:43:08 capacity for using LLMs out there. Like a lot of people I know, they first start with one,
0:43:13 but then over time they’re using Perplexity for search. They’re using, you know, Google for search.
0:43:18 They’re using ChetGPT for analysis, Claude for writing, Grok for current events. Like there’s
0:43:23 like this ability to handle multiple LLMs in your life. And so I think there is going to be a world
0:43:28 where there’s going to be work LLMs now. Some of the ones I just mentioned, we’ll try to go into work
0:43:33 as well. I’m excited for that. Like we’ll have Breeze Assistant and I think it’ll be great for the go-to-market
0:43:37 professional. A lot of the agents we put out were kind of dog water when we first put them out,
0:43:42 meaning like they just were like very, very immature. The models weren’t there yet when we
0:43:47 first put them out, the orchestration around it. But a couple of the ones that we put out are pretty
0:43:51 darn good now. So the customer assistant, that tier one one is pretty good now and it is having
0:43:55 meaningful impact to businesses. And then the prospecting agent, we just came out with the V2,
0:44:00 it’s still in private beta. But what’s really good about it is that it does account research and it’s
0:44:05 really good using deep research. So does account research. It then goes through and it’s, this is
0:44:12 crazy. It’s actually deal aware. So let me tell you what I mean by that. You put a contact in there
0:44:16 that you’ve worked with, it’s watching the interactions and it is ready with the next
0:44:22 communication that either you can send or it can send on your behalf. And so you send it out and
0:44:26 let’s say the person replies, let’s say they get on the phone and you call them. Let’s say that you have a
0:44:32 zoom meeting in the middle of all this. And then you then hang up. It’s ready for the next
0:44:37 communication. That’s aware of everything that just happened. So it’s not like an automated sequence.
0:44:41 It’s not that it’s like, it’s crazy. So it’s like, it’s all, you know, I call you up, we hang out,
0:44:45 we talk about three or four different things. It’s now ready for the next steps and those are planned.
0:44:49 So I think that’s pretty good. And then the last part is I think this next year, a lot of people are
0:44:55 going to want to curate their context. So we’re going to expose a lot of that context for customers to look at.
0:45:00 And I think there will be a rise of a new worker. Actually, I think there will be an agent manager
0:45:06 and an agent trainer. Sometimes they’ll be the same smaller companies, but in a larger company,
0:45:12 you can see an agent manager being somebody like the sales leader who says, we’re going to use these
0:45:18 agents for our sales org. And then the trainer might be either the BDR themselves, the SDR themselves,
0:45:22 or maybe a team manager. So I think that’s going to be really wild because you’ll start to see people
0:45:25 go, yeah, I’ve managed agents in my last job. And you’ll be like, oh my God, you’re hired.
0:45:26 So like, I think that’s good.
0:45:31 Awesome. Well, I mean, this has been an amazing conversation. I’m really excited to see what
0:45:36 HubSpot rolls out in the AI realm and the agent realm coming up. And, you know, thank you so much
0:45:40 for hanging out and sort of nerding out about this stuff with me today. It’s always a blast.
0:45:42 Yeah. Thanks for having me, man. Appreciate it.