So on the website, Scott, what's happening with the web technology, the content management system today?

Well, I mean, the main thing here is this idea of people now starting to experiment with much more dynamic web experiences.

The, the version of it that I think is, is most intriguing is it's not just about...

Like, we've always had a certain degree of, you know, personalization of websites, but it was mostly like, okay, we've got fixed spots, you know, on a relatively well-structured page, and we might substitute, you know, one or two things in those fixed spots, or, you know, even that page often existed within a very fixed and rigid information architecture that like, okay, well, here's your menu choices and your navigation and, you know, you, you can, you can have your car in any color you want as long as it's black.

You can, like, get anywhere you want on the website as long as it's one of the choices that we've given you.

You know, and, you know, sort of seeing this shift where people are now like, okay, on both levels, they're like, listen, if we start to componentize, you know, the content, not just the raw information, but content, visual content, to a certain extent even services that we might, you know, expose, you know, from an interactive perspective, and then we're combining that with this, you know, the AI engine's, you know, ability to, like, think of these things k- as a conversation, even if it's not an explicit, you know, text conversation back and forth, but sort of treating, you know, that flow as a conversation and then being able to make reasonable guesses as to like, okay, well, based on where we're at in this conversation, this is what to show people.

It's interesting, you know, like the, I've had a number of web companies that the place where they, they, they sort of made the first leap into this was honestly with the search bar.

Like, what is, what is a composable canvas in this chaotic time, in this front end, where some amazing things are happening? What does that look like?

It wasn't that we were going out there looking for like a particular tool.

Well, one of the things, too, is that.

Marketing is so challenging today.

I really have just such tremendous respect for marketers.

I don't consider myself a marketer.

I consider myself adjacent to marketers.

But I put marketers on a pedestal because I'd be hard-pressed to think of any other profession that both has seen such a wide and dramatic set of changes.

I think the problem is, well, let me ask you, where do you think you're genuinely kind of seeing change with AI? Have you got any kind of stories rather than just kind of technology vendors that are just sort of slapping a bit of AI on their logo?

or

solved is probably over, the way we've... made progress to address it to date is like, okay, well, you have these major platforms, you know, like a Salesforce, like a HubSpot, you know, like a Marquee or whatever it is that's like, okay, we've got enough momentum around us that we will, you know, gravitationally pull other companies to build bespoke integrations to our particular set of APIs.

Right.

And so

kind of,

again, we'll hang and wave you for a moment, but any MCP client being able to like interact with any MCP server without anyone having to like build, you know, custom bespoke integrations, I think it's going to be a massive unlock.

And again, I'm sure a lot of vendors individually, if I could say, like, for the love of God, don't do this.

But they can.

How do people get to what they don't know?

Yeah.

I mean, my take on the situation is...

I know this is probably ironic coming from the MarTech landscape guy, but boy, if there's one regret I have over that landscape, it's that I think, for better or worse, it got people a little too focused on the tech and the tool side of it.

And it's not to say that the tech and the tools don't have amazing things that they can bring to the world, but they are not the dominant factor in the success here.

I'm a recovering engineer, computer science nerd, so there's this thing in computer science of the big O. What's the dominant variable that's sort of shaping these outcomes? And the dominant variable is not tech.

The dominant variable is org, and in particular, I would say is a key part of that is even like the culture for it.

This two-tiered internet, where are we headed with this? Like, Just rap as Scott Brinker would.

Oh, sugarcoating

... keep, keep track of all your content and keep it updated and you sp- Yeah.

There's a lot of content out there that just isn't that.

And so, you know, getting the data, interpreting the data is...

I mean, that alone feels like a, okay, you, you just aren't gonna fully automate that.

But the level above that of the context, what isn't in the data that would actually, as a human, really matter to you in which experience you would choose? I mean, again, I know we're talking about UI isn't necessarily the same battlefield it once was, but it's, it's a nontrivial piece.

So, like, how does an agent quantifiably verify is this a delightful user experience or not?I, I would just propose at the moment that's not where AI is at.

Let's go

[laughs] Speak your mind.

My point about architecting ad tech and martech as part of the same system, I don't mean the same system like, oh, the same piece of software or even necessarily the same vendor, or necessarily even the same team.

I'm just saying at, there is some level you go up, we don't have a chief marketing officer separate from a chief advertising officer separate from a chief s- You know, there's marketing, and this whole spectrum of how we engage with people across own channels, paid channels, earned channels.

Like, at some level, marketing has to be thinking about how are we orchestrating across these things and really, like, getting the synergy and the alignment and the consistency for them.

And that's just, this is, yeah, again, this is the thing that just gets me in trouble every single time.

I'm just like, these debates that happen at the level down of like, oh, well, this is a different system and it works on different algorithms-

Um, first of all, uh, getting that data layer in order.

order.I mean, again, for a lot of companies, this is still a mess.

You'd be vibe coding it right now.

(laughs) Um, you know, I think one of the challenges that is happening now in the environment is it used to just be like, okay, if I have a better mouse trap, if I have a way to build a better, you know, solution than the incumbents, you know, I can do that.

I find my unique strategy, my niche, you know, I go after it.

Um, and it was very much about a battle, a competitive battle with commercial solutions.

Oh, is my commercial solution better than yours? Where it's getting very tricky right now is because AI has made it so easy for people to build software.

I mean, professional software developers, you know, leveraging these coding assistants.

I mean, you talk to people who are really good software engineers and the way they're leveraging these AI assistants that like 10X their productivity, it's- it's pretty impressive.

Am I putting words in your mouth, or is that where you're going?

How important is context, and how should marketers think about understanding context as we go into 2026?

You know, there's probably, like, a, um, a parallel here of what AEO is to SEO, is a little bit what context engineering is to prompt engineering.

Which is to say, you know, most context engineering, uh, sort of originated with this idea of prompt engineering, of like, "Okay, if I'm going to ask the AI to do something on my behalf, I need to feed into it as clear information instructions as possible to make sure I, you know, it doesn't go off and, and mend its own thing, it gives me what I want." Well, you know, it's sort of just like AEO-expanded SEO, context engineering expands on prompt engineering to say, like, "Okay, well, it's not just what we wanna describe as the instructions to the LLM, it's also that we wanna point it at access to data that it might then be able to use in its processing, uh, because these AI engines are getting agentic, which means they can take actions." You know, we might also wanna provide it with access to certain tools, you know, that it can use to like, "Oh, okay, actually you can go out and you can query, you know, for this information, or you can book this appointment." Um, and so it's that art, that practice of saying, "Okay, now when I ask the AI to do something, I wanna bundle up instructions, I wanna bundle up access to the right data for what it might wanna need to do.

I wanna, like, point it at the tools it can use in executing that." Um, and that's what context engineering is all about.

How do you think about creating the right context when you're building some sort of an agentic process? What's enough information? What's too much? How do you figure out the right balance of here's the, the boundaries of the information an LLM is looking at, and here's what I'm asking it to do?

You know, I, I so love the way you frame this question, because most of the challenges that people have with AI aren't really challenges with the AI.

You know, if you took the AI out and you say, "I'm gonna have a human who's going to do something for me.

Not the LLM, I'm gonna have a human do this," and you're like, "What instructions do I need to give them? What data do I need to give them access to? What tools, you know, are they gonna need to use to execute it?" It turns out, for a bunch of things that we're asking AI to do, we don't, like, have the answers to that.

Right.

It thrives on content.

Um, you know, but because of the way these LLMs think, you know, it turns out things like, for instance, FAQs, like sort of very structured question-and-answer content seems to resonate very well, uh, with the models that these AI engines are, you know, uh, creating and then how they're gonna be able to answer accordingly.

Versus like with Google, again, like they would have taken FAQs, but you know, it was ag- again sort of like a different set of algorithms of like what they were looking for in content and how they use it.

Maybe one third thing here is like Google used to really complain and dissuade you from showing one thing to Google and another thing to a human.It seems actually with these AI engines, they don't really care about that.

Uh, and actually one of the AEO recommendations these days is, like, you know, actually if you identify something coming in as one of these AI agents, you know, you can actually serve up a simplified version of your page, doesn't have all the user interface crap.

It's, like, just a markdown version of the content.

Are people going away from content marketing as a, a, a channel? Are they thinking about expanding it? What's the balance look like there thinking about content?

Is anything actually working?

Well, I actually, I find in the content production pipeline, this is one of the use cases where people are getting pretty good at actually harnessing AI effectively.

Now, this doesn't mean, like, (laughs) you turn it over to an AI agent and it does the complete end-to-end, like, oh, concept-concept and production and distribution and analysis and yeah, I went off and I got an espresso and I came back and it was all done.

No, but, uh, like, each step along those ways, uh, we are seeing people productively use AI to, like, "Okay, well help me brainstorm when I wanna do this or help me evaluate this.

Okay, all right, now I want to create this thing.

Can you take this piece of content and help break it up into different components for, um, uh, you know, different channels where I may deploy it? Can you help me with the analysis of, you know, customer engagement around it?" You know, again, these are all things that marketers could do before.

They were generally very time-consuming and we're seeing a fair number of content production use cases where the human still has to be the one driving each step of that, you know, but the timeframe in which they can get those things accomplished has shrunk considerably.

Mm-hmm.

You'd be vibe coding it right now.

(laughs) Um, you know, I think one of the challenges that is happening now in the environment is it used to just be like, okay, if I have a better mouse trap, if I have a way to build a better, you know, solution than the incumbents, you know, I can do that.

I find my unique strategy, my niche, you know, I go after it.

Um, and it was very much about a battle, a competitive battle with commercial solutions.

Oh, is my commercial solution better than yours? Where it's getting very tricky right now is because AI has made it so easy for people to build software.

I mean, professional software developers, you know, leveraging these coding assistants.

I mean, you talk to people who are really good software engineers and the way they're leveraging these AI assistants, like, 10X their productivity.

Am I putting words in your mouth or is that where you're going?

Mm-hmm.

And that wasn't just in MarTech.

That was pretty much in every software category.

Like, that's just how we treated software working.

And if you were a startup, the only reason you were a startup was because someday you hoped to become a multi-billion dollar company or get acquired by a multi-billion dollar company.

With the proliferation of software that's happening, one of the things that's changed is that's not the only business model.

You've got now a ton of people who-- Let's just take, for instance, service providers, who again, classically, oh, you're either a software company or you're a services company.

Since you could see sort of from the top down, what are some of the AI technologies that are most commonly being adopted in this new wave of MarTech?