Shownotes
Earlier this month, Scott Brinker and co-author Frans Riemersma released their latest report: Martech for 2025.
It’s 108 pages of dense insights on where Martech is headed—and as you might imagine, it’s largely focused on the core ways AI is re-shaping our discipline.
For nearly 15 years, Scott has chronicled the rise of martech as one of its foremost thought leaders, and it was my pleasure to sit down with him to dig into the conclusions.
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About Today's Guest
Scott Brinker is VP Platform Ecosystem at HubSpot and previously the co-founder and CTO of ion interactive, a SaaS company that pioneered interactive content for global enterprises and was acquired in 2017.
Since 2008, he’s also run the Chief Marketing Technologist blog, chiefmartec.com, with over 50,000 readers, and creator of the Marketing Technology Landscape, mapping the growth of the marketing technology industry from a few hundred vendors to over 14,000.
He wrote the best-selling book "Hacking Marketing," published by Wiley in 2016, and co-authored of the article "The Rise of the Chief Marketing Technologist" published in Harvard Business Review. He is a frequent keynote speaker at conferences around the world on topics of marketing technology and agile marketing.
https://www.linkedin.com/in/sjbrinker/
Key Topics
- [01:26] - Main take-aways from the report
- [03:31] - How AI can lead to more differentiated marketing
- [06:07] - Efficiency vs. effectiveness from using AI
- [11:33] - AI and the Hype Cycle
- [15:28] - Innovator’s Dilemma and compressed innovation
- [17:16] - Segments of AI innovation
- [21:06] - Innovation challenges fo r legacy incumbents
- [22:56] - Last-mile issues with AI feature quality
- [29:10] - AI agents
- [40:54] - Orchestration layer
Resource Links
Learn More
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Transcripts
welcome to rev ops FM, everyone.
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Big day on the show today.
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As we chat with someone who
really needs no introduction.
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It is Scott Brinker, VP of
platform ecosystem at HubSpot
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editor at chief martech.
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com and creator of the famous marketing
technology landscape, super graphic.
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Now Scott has just released a new
report called Martek for 2025.
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And as you might expect, it's
all about how AI is reshaping the
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marketing and Martek environment.
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And it's just under a year ago
that we've done our first episode
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on the impact of AI and marketing.
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So I thought this is a great time to
check in, see how things have evolved in
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the past 12 months, and how AI is going
to change your job in the years to come.
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And Scott is gonna be our guide.
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Scott, it is a pleasure
to have you on the show.
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Scott Brinker: Well, thank you
so much for having me, uh, fun
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topics looking forward to this
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Justin: It is now Scott, I understand
your cocktail party trick is
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that you can recite all 14,000.
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Vendors from the Martech
landscape graphic by heart.
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Is this true?
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Scott Brinker: and I can even
draw their logos from memory.
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Justin: It is, it is, it is an
amazing talent and my, my hat goes
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off to you for maintaining that now
for how many years is it now that
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you've been, producing that graphic?
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Oh my
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Scott Brinker: so it's
over a 14 year period.
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We took one year off,
back in 2012, I think.
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Yeah, but yeah, it's just
been growing crazy ever since.
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Justin: Well, it is become an amazing sort
of pillar of our, uh, of our ecosystem.
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So thank you for the
work that you do there.
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on this year's report on Martech
in 2025, maybe let's just start
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with the bottom line up front.
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Like what are top one to two
messages you would like mops pros
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to take away from this report?
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Scott Brinker: Wow.
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Well, I think at the end of the day,
we all know marketing is changing.
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both at a technical level of like
the capabilities, you know, uh, the
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products we use, what AI is making
possible for us as marketers to do.
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but it's also very clearly shifting
the dynamics for how buyers are
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going to want to engage with us.
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you know, it's one of the things.
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I mean, just to like pick some
random thing we could look at that
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in previous years would have been
an entire year's worth of content.
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Uh, and then this year we're like, Oh
yeah, by the way, it turns out that maybe
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Google search, you know, is going to be
disrupted by these other channels in which
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people are going to go and like, you know,
just directly get answers to questions.
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We're like, yeah, that's one of
the like 15 or 20 major disruptions
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that I'm looking at at the moment.
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Thank you very much.
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But I mean, you know,
again, I know this is.
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I don't want to sound flippant about it
because I know it's a stressful thing
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for people when you've got all these
changes and you're trying to keep up,
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uh, and if there's any consolation,
it's that everyone's in the same boat.
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Everyone is trying to keep
up with these changes.
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Um, but I, but I always take this
optimistic view that these sorts of
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disruptions are actually a good thing.
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Gift to in particular marketers,
um, you know, marketing is
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always about differentiation.
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It's always about standing out from the
crowd, you know, and when you've ended
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up in a market or an environment where
sort of everything is sort of converged
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down to like a common playbook and common
channels and common approaches, it's
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actually really, really hard to stand out.
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But when you're in these moments
of disruption where things are
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changing, I think those sorts of
marketers who are willing to be.
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bit bold and like push out on the
frontier and experiment with it.
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It becomes a really great opportunity
for them to differentiate.
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So yes.
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There's a whole bunch of ways in
which, you know, marketing and
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MarTech is changing, but it's
actually a great opportunity.
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Justin: One of the, risks concerns, I
don't know how we want to phrase it,
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but one of the, one of the things that
I've felt and seen other people express.
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Is, you know, if everyone's using
chat GPT to write their copy,
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everyone kind of sounds the same.
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so it's interesting that you,
you poke on it as like an
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opportunity for differentiation.
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How do you think companies can use AI,
to actually be different to stand out?
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Scott Brinker: Yeah, well, I mean, I would
argue the, uh, yes, just using chat GPT
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out of the box to write my blog articles
the same way everyone else is, that is
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that same sort of pattern we've seen of
like, oh, well, I guess if you publish
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a top 10 listicle on keyword X, you
know, I mean, there's always a formulaic
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approach to these things that, you know,
sadly, yeah, a lot of, you know, sort of
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Companies a lot of marketers like fall
into the trap of but very few of those
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things like started out That way there was
someone who actually invented the listicle
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who like hey Wow, this is actually a way
to like people this resonates with folks.
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Oh, I could do a series
of these Andrew Chen who?
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uh, has had a number of different roles,
you know, as a VC up to growth for Uber.
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Uh, he had a phrase he had invented.
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The Law of Shitty Clickthroughs.
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Uh, you know, which is basically
this idea that, you know, marketing,
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particularly in the digital space.
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Is this continuous sequence
of, okay, a market or coming up
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with some sort of novel way of
breaking through and engaging.
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it works, like it's like this
incredible, like return because
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it's not been done before.
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And the word starts to leak out and
then other people start to do it.
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And basically eventually over time, the
efficacy drops and it reverts to the mean
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and you have to come up with another one.
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And so, yeah, there's definitely
a lot of that with AI.
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I think, you know, the folks
who are going to be creative.
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With generative AI aren't just
saying like, Oh yeah, chat GPT,
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write my blog article for me because.
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Nobody's going to be
reading, those blog articles.
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I mean, like if we have, if we
produce like a hundred times more,
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you know, blog articles, uh, we have
not invented a hundred times more
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human attention, you know, for people
to actually consume these things.
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In fact, if anything, there's sort of
the indication now that, you know, the
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recipients on the other side here, they're
kind of savvy on this, you know, they're
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leaning into like, Hey, you know, this
huge stream of emails that you sent me.
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Can you just summarize that in
a few bullet points for me and
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let me know if there's anything
particular I should pay attention to.
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So I think the creative marketers are
looking at generative AI or AI more
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broadly, not about just how do we churn
out more of the same kind of articles,
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but how do we do something different?
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Justin: on that note, your
survey suggested like at
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least 80 percent of marketers.
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If I interpreted the results correctly
at least 80 percent of marketers are
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using AI in some fashion, and it looked
like at least half of them were using AI.
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Daily or weekly, which I think is huge,
like compared to where we were a year
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ago, where it felt like people were
still like, what do we do with this?
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And what is this thing?
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but drilling down on on that creativity?
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do you have a sense?
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Could be database could be
intuition based, like the
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impact of this AI adoption?
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Is it mainly efficiency right now?
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Or are there marketers out there that
you've seen who are actually being able
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to produce better marketing as a result
of doing this than they otherwise could?
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Scott Brinker: there's a little
bit of a blending between the
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efficiency and the innovation side.
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for instance, like, when I think
about it this way, okay, so like the
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efficiency would just be like, okay,
well, we can do this thing faster
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or we can do it less expensive.
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Not bad.
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but here's where it's sort of like plays
into the degree to which you're able to
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turn that from an efficiency into a true
innovation thing is it starts with like.
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The questions, the hypotheses, what
are the experiments that I want to run,
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you know, well, historically, you know,
for marketers who weren't necessarily
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data scientists or data engineers,
like being able to go deeper into their
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data, you know, to answer some of these
questions, to get data, to feed some
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of their hypotheses, very often involve
the cycle of filing tickets and grab
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an analyst, track that down, you know,
and so there's a whole set of some
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of these, uh, you know, new AI tools.
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many of them are early, but
they're developing very quickly.
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Uh, that is democratizing the ability
for marketers to be able to ask more
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questions of their data and have it
come back with answers immediately.
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So we're taking out, you know, that
whole like multi day or in some case,
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multi week, you know, analyst cycle.
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Now you could say, that's
the thing about efficiency.
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It is, you know, but to me,
marketers, like one of their.
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is their imagination.
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That's just like constant, like coming up
with questions and ideas, you know, the
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vast majority of which we've kind of been
trained to just like, let go of, because
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we're like, oh yeah, no, I just can't
take too much work to get that answer.
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So, you know, I don't care, you know,
but as we sort of, you know, uh, reduce
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the number of things that feel out
of reach to be able to like ask more
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and more questions and get answers
and like chase those ideas down, I
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think that actually helps unlock.
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New creativity and new ideas.
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Same thing when we talk about the actual
implementation, when we compress the time
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and costs to be able to produce things,
This isn't just about saying like, Oh, I
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was going to produce 10 things, you know,
and I was going to spend a week on it.
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Now I can produce 10 things
and I can do it in two days.
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Um, Oh, okay, great.
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I'm more efficient.
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You know, it's really like, okay, now for
that extra three days, like what do I do?
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Can I produce more things?
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And it's that produce more things,
which is where you get that.
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inflection from like, okay, wait a second.
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This isn't necessarily just about
efficiency because as long as you're
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not saying, well, I'm just going to
produce more of the same sort of thing,
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but you're gonna be like, oh, well,
let me try some other experiments.
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Let me like, you I've got a little
bit of time, I've got this wow idea.
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let me create this and put that out there.
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So harnessing that time is another way.
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, And a third one is, and we've seen
this direction headed for a while with
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these sort of no-code tools, , but
now generative AI is bringing.
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a sort of no-code capability
to so many disciplines.
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You know, I mean, you know,
with, uh, you know, images.
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We're starting to see some really
cool stuff emerge here in video
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creation and things like this.
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it ex it democratizes ability
for people who have an idea?
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To actually like,
instantiate it, make it real.
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you know, and even if you want to be
modest about it and say like, Oh, well,
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you know, maybe some of this stuff isn't
ready to go from idea to, you know,
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production ready final version, if it's
to go from idea to something, that's
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a pretty darn good representation of
what it could be, you know, to be able
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to get reactions to that, you know,
and use that as a guide for developing
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the production ready version one.
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Again, is this an efficiency thing?
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Yes, because I didn't necessarily need
the full team to even like put together
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a prototype concept, but it's that
empowerment to create these prototype
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ideas, you know, just pretty much at
the speed of thought, that is a huge
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unlock from an innovation perspective.
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Oh my goodness.
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It's like fuel for the imagination.
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Justin: And you've multiplied your
ability to iterate to your point,
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to try more ideas or to spend that
time doing deep human work that maybe
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you usually would not have time for
in a typically overloaded calendar.
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Scott Brinker: Yeah.
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You know what I mean?
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You talk about like what you could
do with the additional hours in the
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00:11:13,516 --> 00:11:17,536
day and certainly more experiments
and more ideas, you know, but for the
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human work, how about spending more
time just talking with customers, you
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know, I mean, we know as marketers,
like that's a big part of our job.
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I.
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You know, a lot of marketers who
don't get a lot of time actually being
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able to talk with customers to be
able to have that sort of like human
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connection and human insight that
they feed in to their imagination.
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So I think you're right, like that
sort of human work, boy, if we
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could get 10, 20 percent more of
our week, uh, you know, allocated
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to that, that would be a huge gift.
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Justin: You know, one of the
first things in your report is
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a graphic of the hype cycle.
215
00:11:53,573 --> 00:11:56,693
I will include a link to the report so
people can can look on this, but you know,
216
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it has that peak of inflated expectations,
that trough of disillusionment
217
00:12:01,313 --> 00:12:05,120
and the, gradually increasing up
to the plateau of productivity.
218
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And I can feel riding that over
the past year from like, whoa, AI,
219
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like AI, like AI SDRs, they suck.
220
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And then gradually I find myself now
you mentioned about disrupting Google.
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I've started to ask chat GPT
things instead of Googling them.
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Like it's actually, I
don't do it on purpose.
223
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It's just, this is a more efficient
way to get my information or, I have
224
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grok on my phone and yesterday there
was a battery with some words in
225
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French and I didn't understand it
and I just took a picture and said,
226
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can you translate this text for me?
227
00:12:34,950 --> 00:12:38,350
Translated the text like it's
starting to work its way in.
228
00:12:38,350 --> 00:12:41,790
So like, where do you think
we are on that on that curve?
229
00:12:41,790 --> 00:12:42,660
Where would you peg us?
230
00:12:42,670 --> 00:12:45,270
You know, obviously everyone's at
different places personally, but
231
00:12:45,270 --> 00:12:50,975
as a collective, what do you think?
232
00:12:51,038 --> 00:12:51,928
Scott Brinker: two reasons.
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00:12:51,938 --> 00:12:57,138
Um, one is I'm convinced there isn't
actually one hype curve here, but
234
00:12:57,138 --> 00:13:01,208
like there's a multitude of different
hype curves all at different stages.
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I mean, again, like with, um.
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I mean, just take some of the stuff here
with like open AI, you know, where are
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we at as far as our comfort level and
using chat GPT, to help us brainstorm or
238
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help us like edit some of our writing.
239
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I think we're actually now pretty far into
the, you know, plateau of productivity.
240
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Most people are pretty
comfortable with it.
241
00:13:20,736 --> 00:13:21,816
It's pretty good at that.
242
00:13:22,306 --> 00:13:24,616
Where are we on using
it as a search engine?
243
00:13:25,076 --> 00:13:25,756
I don't know.
244
00:13:25,776 --> 00:13:28,976
Probably at this exact moment, uh,
given the announcements, you know,
245
00:13:28,976 --> 00:13:31,836
uh, happening, you know, right
now, uh, we're probably at the peak
246
00:13:31,836 --> 00:13:34,356
of hype of like, Oh my goodness,
it's going to disrupt everything.
247
00:13:35,496 --> 00:13:39,086
There'll be a trough, you know, where
we had on the, you know, ability from
248
00:13:39,086 --> 00:13:42,706
open AI with things like soar and, you
know, there's video creation stuff.
249
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Probably very early on in
the technology triggers.
250
00:13:46,001 --> 00:13:49,531
And so it's, it's interesting because
for one thing, there's just multiple
251
00:13:49,531 --> 00:13:51,641
hype curves at different stages.
252
00:13:51,943 --> 00:13:54,759
but the other thing is, this is
the thing about the hype curve.
253
00:13:54,759 --> 00:13:56,489
I've, I've just noticed over the years.
254
00:13:56,509 --> 00:13:57,529
And again, kudos to.
255
00:13:57,904 --> 00:14:02,461
Gartner for like identifying this pattern
and like articulating it, yeah, in such
256
00:14:02,461 --> 00:14:06,321
a excellent way, but it used to be,
I mean, I've got some gray hair here.
257
00:14:06,781 --> 00:14:10,321
It used to be that these hype curves
were something that played out over.
258
00:14:11,111 --> 00:14:14,717
Between a 5 to 10 year period, you
know, in fact, this is how like
259
00:14:14,717 --> 00:14:16,157
the tech industry was structured.
260
00:14:16,157 --> 00:14:20,387
This is how venture capital
was built to do this, slowly,
261
00:14:23,337 --> 00:14:23,887
You
262
00:14:25,897 --> 00:14:26,497
know,
263
00:14:30,567 --> 00:14:35,144
well, maybe it's more like, you know,
4 to 6 years, things like in marketing,
264
00:14:35,144 --> 00:14:38,124
uh, the customer data platform,
you know, You know, revolution.
265
00:14:38,124 --> 00:14:43,504
I feel like that thing like Barely got
started in like 2015, 2016, you know,
266
00:14:43,504 --> 00:14:47,094
and now we're already at the thing
of like, well, CDPs, are they, they
267
00:14:47,094 --> 00:14:48,584
a thing now, or is this all the day?
268
00:14:48,604 --> 00:14:49,874
And you're like, wait a second.
269
00:14:49,874 --> 00:14:51,674
I, this thing was like
barely getting started.
270
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Like what, um, now we're on to some
next new, uh, you know, architecture.
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00:14:56,054 --> 00:14:59,904
It feels like an AI compressed that
hype curve down to something that
272
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really is more about months than years.
273
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I kept feeling like, you know, even in
the first year or so of chat GPT, you'd
274
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be like, okay, well, yeah, this thing is
really great, but it's data is, uh, you
275
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know, two years old and then they connect
it up to the web and you're like, okay,
276
00:15:14,882 --> 00:15:15,842
all right, well, I guess that's all.
277
00:15:15,982 --> 00:15:16,242
Yeah.
278
00:15:16,242 --> 00:15:19,132
It's really good, but it
can't do math problems.
279
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Uh, you know, oh, well now we connected
and it can run little Python programs and.
280
00:15:23,682 --> 00:15:27,322
Calculate that and you're like, okay,
well, like everything I like thought was
281
00:15:27,352 --> 00:15:31,052
this barrier that would be this multi
year thing kind of vanishes after a couple
282
00:15:31,052 --> 00:15:35,862
months, you know, and so I think it just
makes it really hard to say like, yeah,
283
00:15:35,862 --> 00:15:41,612
the hype curve is still a thing, but where
are we and how quickly will we move along?
284
00:15:41,662 --> 00:15:42,962
Boy, I don't know.
285
00:15:43,122 --> 00:15:45,480
Justin: you mentioned the
book, the innovators dilemma, a
286
00:15:45,480 --> 00:15:47,180
number of times in your report.
287
00:15:47,280 --> 00:15:51,280
And in that book, uh, the author
talks about how he studied the
288
00:15:51,280 --> 00:15:55,415
disk drive industry and Because of
this like fruit fly effect where
289
00:15:55,415 --> 00:15:56,735
it was a very fast moving industry.
290
00:15:56,735 --> 00:15:58,285
So you could observe the life cycle.
291
00:15:58,725 --> 00:16:02,165
And I just, he's passed away
obviously, but he was here looking
292
00:16:02,165 --> 00:16:06,185
at, at these very compressed cycles
that we're going through now, like
293
00:16:06,185 --> 00:16:09,825
it is like watching life almost on 1.
294
00:16:09,825 --> 00:16:09,995
5 X.
295
00:16:09,995 --> 00:16:12,842
You know, the way you can with a
YouTube video where you see things
296
00:16:12,842 --> 00:16:16,282
moving very quickly, businesses
starting trends coming and going.
297
00:16:16,386 --> 00:16:17,386
Scott Brinker: Yeah, no.
298
00:16:17,386 --> 00:16:20,110
And such a brilliant, Clay
Christianson, uh, you know,
299
00:16:20,110 --> 00:16:21,500
the whole innovative dilemma.
300
00:16:21,625 --> 00:16:24,588
but this thing about disruptive
innovation, boy, you just
301
00:16:24,588 --> 00:16:25,798
see it this perfectly thing.
302
00:16:25,798 --> 00:16:28,532
Like, his big, insight there
was, you know, so often these
303
00:16:28,532 --> 00:16:29,742
disruptions would happen.
304
00:16:30,017 --> 00:16:35,007
From below, where like it would end up
empowering a set of people to do things
305
00:16:35,017 --> 00:16:39,169
who quite frankly, wasn't cost effective
for them to do things the old way.
306
00:16:39,169 --> 00:16:41,519
Like the great example would
be like, you know, coding.
307
00:16:41,799 --> 00:16:45,069
Okay, well, if I needed to build
a software program, you know.
308
00:16:45,394 --> 00:16:46,644
Not too long ago.
309
00:16:46,674 --> 00:16:47,084
All right.
310
00:16:47,084 --> 00:16:49,034
Well, I've got to hire multiple engineers.
311
00:16:49,034 --> 00:16:49,734
I'm going to need this.
312
00:16:49,914 --> 00:16:51,414
You know, it's like, oh, okay.
313
00:16:51,414 --> 00:16:55,304
So if I want to do a simple little
program to like, you know, go fetch me
314
00:16:55,304 --> 00:16:59,564
some stock market data and I'm like,
no, it's just not worth doing that, you
315
00:16:59,564 --> 00:17:02,954
know, and then you have these disruptive
innovations that come along that.
316
00:17:03,364 --> 00:17:06,594
They're maybe not yet at the
level where they can do the sorts
317
00:17:07,184 --> 00:17:11,800
use Silence.
318
00:17:12,243 --> 00:17:17,654
cases, they work friggin beautifully, you
know, and they unlock that capability for
319
00:17:17,654 --> 00:17:22,559
all these people who before, like, didn't
even have the option, to get things done.
320
00:17:22,569 --> 00:17:24,469
So Big fan of, Christiansen.
321
00:17:24,997 --> 00:17:26,097
This is his time.
322
00:17:26,878 --> 00:17:27,944
Justin: Yeah, no, it's.
323
00:17:28,007 --> 00:17:29,547
Scott Brinker: above, he's like, yep.
324
00:17:31,044 --> 00:17:34,574
, Justin: but part of that dynamic that
he describes about the innovation coming
325
00:17:34,574 --> 00:17:39,319
from above you, you start to address it
a little bit in, the different segments
326
00:17:39,379 --> 00:17:42,802
and, maybe you can walk us through them
a little bit, but the, the segments
327
00:17:42,822 --> 00:17:45,966
that you kind of spell out, are these
indie tools, you know, bubbling up
328
00:17:46,386 --> 00:17:48,596
Using AI innovating very rapidly.
329
00:17:48,606 --> 00:17:51,099
These challenger platforms that
are kind of, threatening the big
330
00:17:51,099 --> 00:17:54,689
incumbents and consolidating a number
of different abilities, the incumbents.
331
00:17:54,910 --> 00:17:58,260
who are, you know, quickly
trying to add a eyes as fast as
332
00:17:58,260 --> 00:17:59,920
they can onto their platforms.
333
00:18:00,047 --> 00:18:02,557
and then, uh, won't even touch
services as a software yet.
334
00:18:02,557 --> 00:18:05,507
But then you also talk as you just
address this, this custom apps idea
335
00:18:05,507 --> 00:18:09,434
that actually I can just build it
myself now with chat GPT, help maybe
336
00:18:10,084 --> 00:18:14,174
amend or add anything you'd like to
to my explanation of that landscape.
337
00:18:14,444 --> 00:18:17,444
And then perhaps where are you
seeing the most fruitful innovation
338
00:18:17,444 --> 00:18:18,834
right now in all of those segments?
339
00:18:19,133 --> 00:18:21,608
Scott Brinker: well, I would start by
saying, you know, in any, in any, uh, in
340
00:18:21,608 --> 00:18:23,618
any, uh, in any, uh, Period of disruption.
341
00:18:23,888 --> 00:18:27,268
Uh, we're used to thinking of sort of the
battle between startups and incumbents.
342
00:18:27,548 --> 00:18:31,108
you know, and one of the reasons why
we made a distinction and sort of the
343
00:18:31,118 --> 00:18:34,978
startup segment between this idea of
these indie tools versus the challenger
344
00:18:34,978 --> 00:18:41,308
platforms is because so many of these
new AI tools that have popped up.
345
00:18:42,398 --> 00:18:45,871
actually looking to displace,
the major incumbent platforms.
346
00:18:45,871 --> 00:18:48,831
In many cases, they now
integrate with those platforms.
347
00:18:48,831 --> 00:18:51,041
They're complimentary to those platforms.
348
00:18:51,265 --> 00:18:55,105
and so it's a little bit like, oh, I
mean, again, not to say there won't be
349
00:18:55,105 --> 00:18:58,869
overlap and exchange back and forth,
you know, in many ways, there will be.
350
00:18:58,869 --> 00:19:02,899
The complimentary nature there seems to
exceed the competitive nature, at least
351
00:19:02,899 --> 00:19:07,289
at this stage of the game versus there
are a set of challenger platforms that are
352
00:19:07,289 --> 00:19:10,899
very much looking at this inflection point
of like, Oh yeah, we're gonna, we're,
353
00:19:11,069 --> 00:19:13,099
we're bringing down Salesforce, you know?
354
00:19:13,309 --> 00:19:14,419
and that's a hard.
355
00:19:14,734 --> 00:19:16,274
That's a hard hill to climb, but,
356
00:19:16,340 --> 00:19:16,370
Justin: Yep.
357
00:19:16,474 --> 00:19:18,974
Scott Brinker: know, this is
the nature of the tech industry.
358
00:19:19,014 --> 00:19:22,914
Um, some, some of those folks are
actually going to climb that hill and,
359
00:19:22,914 --> 00:19:26,854
you know, disrupt the incumbents they're
going after, you know, so that's the,
360
00:19:26,864 --> 00:19:31,696
you know, that's what a central, the
commercial, uh, MarTech landscape, uh,
361
00:19:31,696 --> 00:19:33,476
you know, where we're seeing those groups.
362
00:19:33,916 --> 00:19:40,251
The thing about the custom software
is people Because AI is really, I
363
00:19:40,251 --> 00:19:44,121
mean, already the barriers to entry
in creating software, the cost of
364
00:19:44,121 --> 00:19:47,901
creating software was already like
plummeting, you know, given the cloud,
365
00:19:47,911 --> 00:19:52,511
given frameworks, given, you know,
AWS services of every flavor you can.
366
00:19:52,511 --> 00:19:55,831
I mean, it was already like headed
down, like more people could do it than
367
00:19:55,831 --> 00:19:58,461
ever, but now with these AI co pilots.
368
00:19:58,786 --> 00:20:03,106
And increasingly, you might talk a
bit about how AI agents are able to
369
00:20:03,106 --> 00:20:06,586
even create code without you knowing
that they were creating code for you.
370
00:20:07,006 --> 00:20:10,526
Um, you know, it's just further
accelerated this ability for like
371
00:20:10,526 --> 00:20:13,796
companies to be able to create
more and more software themselves.
372
00:20:15,166 --> 00:20:19,676
will that be a total replacement
to those commercial products?
373
00:20:19,907 --> 00:20:22,597
my sense is in the, Foreseeable future.
374
00:20:22,607 --> 00:20:23,757
Probably not.
375
00:20:23,957 --> 00:20:26,737
Um, you know, with due
credit, uh, what was it?
376
00:20:26,787 --> 00:20:30,787
Klarna, you know, ripping out Salesforce
and Workday and all these other ones,
377
00:20:30,787 --> 00:20:34,827
but you know, they're, they're in a
league of their own, you know, but what
378
00:20:34,827 --> 00:20:39,207
I think of it is, is a more of a, again,
another one of these complimentary
379
00:20:39,207 --> 00:20:42,855
components, where like, okay, yeah,
there are certain capabilities on the
380
00:20:42,855 --> 00:20:46,045
market that it wouldn't make sense for
me to like reinvent the wheel, right?
381
00:20:46,045 --> 00:20:48,310
Like, you know, I don't need
to write my own email server.
382
00:20:48,670 --> 00:20:52,530
At this point, that's not a comparative
advantage, uh, you know, for most
383
00:20:52,530 --> 00:20:57,340
companies, um, but when it comes to how
I want to design a particular customer
384
00:20:57,340 --> 00:21:02,070
experience or the way in which I want to
optimize a particular workflow, from quote
385
00:21:02,090 --> 00:21:06,980
to cash, you know, that actually might
be something that I would want to custom
386
00:21:07,390 --> 00:21:11,630
develop the way in which that works, the
way in which that experience is delivered.
387
00:21:11,910 --> 00:21:15,280
Now I can do that on top of
those commercial platforms,
388
00:21:15,280 --> 00:21:16,790
but I'm now leveraging AI.
389
00:21:16,810 --> 00:21:20,580
To be able to accelerate the
customization that I build on top of it.
390
00:21:20,666 --> 00:21:27,324
Justin: you know, incumbents have often in
my experience With adding that innovation
391
00:21:27,424 --> 00:21:29,942
and, mean, there's many potential reasons.
392
00:21:29,962 --> 00:21:32,692
Part of it is maybe they just are
not as laser focused on that one
393
00:21:32,692 --> 00:21:35,712
problem, but it's always seemed
counterintuitive to me that like.
394
00:21:36,072 --> 00:21:41,092
You know, two developers in a bedroom
somewhere can produce something that is
395
00:21:41,092 --> 00:21:44,782
a bit closer to the pulse of the market
than some of these big legacy platforms
396
00:21:44,782 --> 00:21:49,602
that I've owned and used with all these
resources who just seem to miss the mark.
397
00:21:49,602 --> 00:21:52,362
And some of that, you know, you've
mentioned, you know, they have
398
00:21:52,542 --> 00:21:56,142
backwards compatibility, dependence
on existing revenue structures.
399
00:21:56,142 --> 00:21:58,052
There's a lot of the
innovators dilemma stuff.
400
00:21:58,155 --> 00:22:02,394
And then part of it, to me, it almost
feels like a mindset thing where somehow
401
00:22:02,924 --> 00:22:07,985
a dysfunction creeps in at larger
scale where they seem unable of, of
402
00:22:07,995 --> 00:22:12,873
the focus or of the creative leaps that
are required, to innovate at the level
403
00:22:12,873 --> 00:22:17,766
that sort of the young blood can, I
wonder, do you share that experience?
404
00:22:17,776 --> 00:22:19,896
I'll say as a notable exception,
I'm not just saying, cause you're
405
00:22:19,896 --> 00:22:22,896
on, you're on the show, but, but,
but HubSpot almost feels to me it
406
00:22:22,906 --> 00:22:24,006
to be an exception to that rule.
407
00:22:24,006 --> 00:22:24,783
And I don't know how they've.
408
00:22:24,784 --> 00:22:29,015
continue to innovate the way that they
seem to have been able to do, at the
409
00:22:29,015 --> 00:22:32,405
scale that they're at, but a lot of other
companies, and I come from the Marketo
410
00:22:32,405 --> 00:22:36,095
ecosystem, they have not been able to do
that, like Eloqua has not been able to,
411
00:22:36,095 --> 00:22:39,185
you know, a lot of these players, um,
what is your take on this issue about
412
00:22:39,205 --> 00:22:43,245
innovation within legacy incumbents
and how can it flourish as a possible?
413
00:22:43,437 --> 00:22:45,837
Scott Brinker: there's a quote and
I wish I could remember who said it.
414
00:22:45,867 --> 00:22:50,177
Um, I believe it was a
at Andreessen Horowitz.
415
00:22:50,366 --> 00:22:55,265
that it's like, uh, it's a race between,
will the startups get distribution
416
00:22:55,375 --> 00:22:57,755
before the incumbents get innovation?
417
00:22:57,922 --> 00:23:00,602
I think this time is a
little bit different.
418
00:23:00,724 --> 00:23:03,019
you know, for a couple, of reasons.
419
00:23:03,019 --> 00:23:05,799
First of all, is this pattern of.
420
00:23:06,394 --> 00:23:08,559
dilemma like disruption from below.
421
00:23:08,649 --> 00:23:10,749
Everyone's now sort of read those books.
422
00:23:10,789 --> 00:23:15,329
Um, you have a whole generation,
you know, of, uh, executives at
423
00:23:15,329 --> 00:23:18,739
these incumbent companies that
perhaps a number of them actually
424
00:23:19,069 --> 00:23:23,523
were the disruptors, you know, Okay.
425
00:23:23,779 --> 00:23:24,209
uh,
426
00:23:30,939 --> 00:23:31,969
A
427
00:23:34,129 --> 00:23:36,529
little
428
00:23:47,159 --> 00:23:50,919
bit slower, just, you know, it'd be
sort of easier to say like, yeah, yeah,
429
00:23:50,919 --> 00:23:52,799
all right, we'll get to that next year.
430
00:23:52,799 --> 00:23:54,849
We've got, you know, the main
thing we need to ship this
431
00:23:54,849 --> 00:23:56,589
year, you know, there's just.
432
00:23:56,654 --> 00:23:58,064
There's just no illusion of that.
433
00:23:58,094 --> 00:24:01,094
You know, everyone basically looks
at that and says like, yeah, this
434
00:24:01,094 --> 00:24:02,344
is, this is changing the game.
435
00:24:02,344 --> 00:24:06,694
This is going to change everything, you
know, about how software actually works.
436
00:24:07,054 --> 00:24:12,264
And so given that, you know, forceful
momentum, you know, and then, yeah,
437
00:24:12,264 --> 00:24:16,064
the fact that a lot of the leaders
that these companies do recognize that.
438
00:24:16,457 --> 00:24:20,757
Yeah, you know, if, if we lose this,
we're going to lose it because we let, you
439
00:24:20,757 --> 00:24:25,677
know, someone disrupt us, uh, you know,
from below on this I think companies are
440
00:24:25,697 --> 00:24:30,513
like attacking this with much greater
ferocity, than historically they have.
441
00:24:30,553 --> 00:24:34,763
Now, that being said, there are still
like, there are structural advantages
442
00:24:34,763 --> 00:24:38,423
and structural disadvantages, you
know, that large companies have.
443
00:24:38,778 --> 00:24:40,565
and I don't think there's
a silver bullet to it.
444
00:24:40,700 --> 00:24:44,220
But I think we're going to see through
this next cycle of the next five
445
00:24:44,220 --> 00:24:48,710
years, which ones of the incumbents
actually successfully made the
446
00:24:48,710 --> 00:24:50,400
transitions and which ones didn't.
447
00:24:50,640 --> 00:24:53,860
And they're going to become the, you
know, business school case studies of
448
00:24:53,860 --> 00:24:56,930
like, okay, here's how you fend this off.
449
00:24:57,246 --> 00:25:00,986
I mean, at the end of the day, it's, you
know, we started with Christiansen, we
450
00:25:00,986 --> 00:25:04,932
end with Christiansen, it's like, you
have to be willing to disrupt yourself.
451
00:25:05,038 --> 00:25:10,368
you know, and I mean, I'll, uh, you know,
give a shout out here to, um, competitor.
452
00:25:10,445 --> 00:25:14,326
you know, I think, Benioff, you
know, and basically taking all of
453
00:25:14,336 --> 00:25:18,666
Dreamforce, being like, nah, it's
not Salesforce, it's AgentForce, you
454
00:25:18,666 --> 00:25:22,256
know, my God, was that a ballsy move.
455
00:25:22,497 --> 00:25:25,056
now, you know, how that plays
out, how it delivers on it.
456
00:25:25,126 --> 00:25:25,476
All right.
457
00:25:25,496 --> 00:25:29,576
You know, I'm not, not for me to comment
at this point, you know, but I mean,
458
00:25:29,586 --> 00:25:34,546
that's sort of like willingness to
like disrupt, you know, all of your
459
00:25:34,546 --> 00:25:38,256
existing plans, all of your existing
narrative, you know, go to market.
460
00:25:38,311 --> 00:25:41,761
That's the sort of thing that I think
Christensen would be like impressed and
461
00:25:41,761 --> 00:25:48,201
be like, okay, yeah, no, not shy about
disrupting the previous model, uh, you
462
00:25:48,201 --> 00:25:50,121
know, to like make it to the next wave.
463
00:25:50,121 --> 00:25:50,171
Yeah.
464
00:25:50,652 --> 00:25:55,362
Justin: And just to close the loop on your
point, the latest edition of Innovators
465
00:25:55,362 --> 00:25:57,162
Dilemma has a forward by Benioff.
466
00:25:57,232 --> 00:25:58,502
So, um,
467
00:25:59,081 --> 00:25:59,371
Scott Brinker: See?
468
00:25:59,391 --> 00:26:00,111
Okay, yeah.
469
00:26:00,522 --> 00:26:04,032
Justin: So, just to underscore your
point about the latest, you know, the
470
00:26:04,062 --> 00:26:07,362
disruptors who have internalized the
message and, like, maybe we've seen
471
00:26:07,362 --> 00:26:10,265
sort of the end of history then of
the, um, Of that pattern repeating,
472
00:26:10,275 --> 00:26:14,605
and in terms of the actual implementation,
like I think we all saw in 2024 Oh, we
473
00:26:14,605 --> 00:26:16,455
need to get our AI features out the door.
474
00:26:16,455 --> 00:26:17,465
Like, what are we going to do?
475
00:26:17,795 --> 00:26:20,469
You know, almost like a, solution
in search of a problem in a way.
476
00:26:20,469 --> 00:26:22,909
Like we, we have AI, we need to
figure out what we're going to do
477
00:26:22,909 --> 00:26:23,809
with it and how we're going to.
478
00:26:24,634 --> 00:26:25,572
we want and be able to
deliver that to users.
479
00:26:25,572 --> 00:26:26,791
Um, and so, and so, but we have
some, we have some questions
480
00:26:26,791 --> 00:26:27,166
that we haven't answered.
481
00:26:27,166 --> 00:26:28,197
So Mike, you're welcome to
jump in and get to them.
482
00:26:28,197 --> 00:26:28,572
and some of them.
483
00:26:29,092 --> 00:26:30,282
I've been really interesting.
484
00:26:30,282 --> 00:26:31,792
Some of them, a few of
them have been good.
485
00:26:31,792 --> 00:26:34,002
Some of them have been kind of
like, Oh, I see what you're trying
486
00:26:34,002 --> 00:26:35,762
to do, but, but not so much.
487
00:26:35,962 --> 00:26:40,062
One of the things I've observed just
personally is a bit of a last mile effect.
488
00:26:40,102 --> 00:26:42,822
Like, uh, we are recording this,
uh, using the tool to script.
489
00:26:42,832 --> 00:26:43,382
It's a great tool.
490
00:26:43,382 --> 00:26:44,842
I use it to, to record my podcast.
491
00:26:46,052 --> 00:26:50,292
They have a feature just as an example,
like remove umza nos, like, Oh, amazing.
492
00:26:50,292 --> 00:26:51,272
This takes me hours.
493
00:26:51,312 --> 00:26:52,922
I just pushed this
button, remove umza nos.
494
00:26:53,402 --> 00:26:56,362
And it does a pretty good job,
but then I don't know, 5 percent
495
00:26:56,362 --> 00:26:58,292
of the time, It clips a word.
496
00:26:58,482 --> 00:26:59,572
It makes it weird.
497
00:27:00,362 --> 00:27:02,602
So either like you're okay with
that and you're willing to tolerate
498
00:27:02,602 --> 00:27:05,172
that or you still have to review
the whole thing anyways and so it's
499
00:27:05,172 --> 00:27:09,052
almost that last mile is enough to
like, it's just not quite there.
500
00:27:09,182 --> 00:27:12,462
Do you think is this, a fly in the
ointment and we will outgrow it or do
501
00:27:12,462 --> 00:27:15,952
you think there is something deeper
there that companies need to address to
502
00:27:15,952 --> 00:27:18,692
be successful as AI software companies?
503
00:27:18,928 --> 00:27:21,437
Scott Brinker: Yeah, and I think
this is one where it's probably
504
00:27:21,437 --> 00:27:27,577
incredibly use case dependent,
uh, you know, on what the both.
505
00:27:27,577 --> 00:27:28,927
Um, what is it?
506
00:27:28,927 --> 00:27:32,817
It's a question of both like how
much tolerance is there for error.
507
00:27:33,297 --> 00:27:35,297
Um, and then.
508
00:27:35,667 --> 00:27:40,907
You know, what is the, uh, speed
by which asymptotically you're like
509
00:27:40,907 --> 00:27:46,097
reducing the error down to where
it falls below that threshold, you
510
00:27:46,097 --> 00:27:47,507
know, that you're concerned about.
511
00:27:47,767 --> 00:27:54,017
Um, I do think video, video,
audio, any of these things that
512
00:27:54,017 --> 00:27:55,697
are like human representations.
513
00:27:55,707 --> 00:27:58,607
I mean, they have the phrase for
this, like the uncanny valley.
514
00:27:58,977 --> 00:28:01,112
Um, You know, we see
515
00:28:01,154 --> 00:28:16,199
Silence.
516
00:28:16,662 --> 00:28:21,682
I mean, a technological perspective,
it's a frigging wonder, it's still
517
00:28:21,682 --> 00:28:25,172
pretty clear, like, nah, nah, this
is, this is not a real person.
518
00:28:25,172 --> 00:28:27,652
And even if you weren't super
paying attention to that, just
519
00:28:27,652 --> 00:28:29,502
something would seem off, you know?
520
00:28:30,597 --> 00:28:32,967
Will we get past that?
521
00:28:34,197 --> 00:28:35,687
Kind of suspect we will.
522
00:28:35,821 --> 00:28:39,703
and I think in the meantime, you know,
a lot of those things, yeah, like
523
00:28:39,890 --> 00:28:41,740
again, everyone has their own choices.
524
00:28:41,740 --> 00:28:44,631
Like I wouldn't use them, you
know, for production purposes.
525
00:28:44,687 --> 00:28:51,698
you know, but am I comfortable using it to
like, you know, like say clean up certain
526
00:28:51,698 --> 00:28:54,288
amounts of like audio background noise.
527
00:28:54,398 --> 00:28:54,698
Yeah.
528
00:28:54,698 --> 00:28:57,268
It seems to do like in
general, like a phenomenal job.
529
00:28:57,378 --> 00:28:58,868
Justin: I think, I think you're right.
530
00:28:58,908 --> 00:29:00,078
I think, no, no, I think you're right.
531
00:29:00,078 --> 00:29:03,398
And it's, it's like, I can't
remember if this was in your report
532
00:29:03,398 --> 00:29:05,508
or something else I was listening
to, but it's like, it doesn't work.
533
00:29:05,508 --> 00:29:05,998
It doesn't work.
534
00:29:05,998 --> 00:29:06,498
It doesn't work.
535
00:29:06,498 --> 00:29:07,948
And all of a sudden it
works and it's normal.
536
00:29:08,098 --> 00:29:10,048
It's just like, well, yes, of
course this would be the way.
537
00:29:10,068 --> 00:29:13,518
And I think that'll, like you
said, with the, the ums and ahs
538
00:29:13,518 --> 00:29:18,168
or something like that, it'll,
Eventually go below that threshold of
539
00:29:18,178 --> 00:29:20,478
acceptability of, of error tolerance.
540
00:29:21,088 --> 00:29:24,808
And, and then life will, you
know, will never be the same.
541
00:29:24,918 --> 00:29:27,381
Let's talk a little bit about, agents.
542
00:29:27,411 --> 00:29:30,261
This was a concept that I, I kind
of thought that I understood.
543
00:29:30,261 --> 00:29:33,648
And then as I read this section,
was the area where I realized my, my
544
00:29:33,648 --> 00:29:36,438
understanding was quite superficial.
545
00:29:36,838 --> 00:29:40,728
In terms of like, Oh, I actually didn't
really understand what we meant by that.
546
00:29:40,768 --> 00:29:43,768
And about this idea of a large
action model and the real
547
00:29:43,768 --> 00:29:44,998
implications of an agent.
548
00:29:45,008 --> 00:29:48,918
So maybe assuming there's probably other
listeners that are in that position as
549
00:29:48,918 --> 00:29:52,908
well, do you walk us through this and
what it means and what the implications
550
00:29:52,908 --> 00:29:54,318
are maybe for how we will use them?
551
00:29:54,456 --> 00:29:57,716
Scott Brinker: Yeah, happy to do that
with the caveat that, um, you know,
552
00:29:57,726 --> 00:30:02,412
if you ask 100 experts for their
definition of an agent, probably get
553
00:30:02,412 --> 00:30:04,821
at least 120 different, explanations.
554
00:30:04,894 --> 00:30:10,145
I think the oversimplified version
is almost like an automation.
555
00:30:10,212 --> 00:30:13,043
this ability to simply say like,
I'm going to make a request,
556
00:30:13,043 --> 00:30:15,874
of this app, software agent.
557
00:30:16,114 --> 00:30:16,944
It's going to go off.
558
00:30:16,944 --> 00:30:19,564
It's going to perform that request,
come back and deliver it to me.
559
00:30:19,643 --> 00:30:23,385
I, I think one of the examples I
pointed to here was, Dharma Shaw,
560
00:30:23,495 --> 00:30:25,115
uh, you know, co founder of HubSpot.
561
00:30:25,395 --> 00:30:29,535
He's, uh, created as one of his many
projects that he goes off and does.
562
00:30:29,595 --> 00:30:33,187
Uh, I've been the custom wonder of
this fellow, agent AI, which is a
563
00:30:33,187 --> 00:30:34,497
whole bunch of these, like very.
564
00:30:34,852 --> 00:30:37,582
Purpose built, you know, agents
that they go off and they
565
00:30:37,582 --> 00:30:38,622
do one thing and they do it.
566
00:30:39,068 --> 00:30:44,628
I think if you ask others, you know,
what makes something an agent is the
567
00:30:44,628 --> 00:30:50,028
ability for it to have almost a little
bit more of an internalization of how it
568
00:30:50,028 --> 00:30:54,728
goes about making decisions, you know,
so you give it a higher level goal.
569
00:30:55,678 --> 00:30:59,728
starts to reason about, okay, well,
how would I accomplish that goal?
570
00:30:59,728 --> 00:31:01,628
I would break it down into
these different steps.
571
00:31:01,628 --> 00:31:04,008
I would take this first step
based on the results of that.
572
00:31:04,008 --> 00:31:06,358
I would adjust, move to the second step.
573
00:31:06,585 --> 00:31:12,445
you know, and I think this is where
people get most excited because as you
574
00:31:12,445 --> 00:31:16,545
start looking at agents through that
lens, you know, it's not just about
575
00:31:16,545 --> 00:31:18,085
things that are almost like these.
576
00:31:18,420 --> 00:31:23,106
You know, uh, very purpose specific tools,
but they start to become things that I'm
577
00:31:23,106 --> 00:31:27,456
not quite sure I'm ready to call them
digital coworkers, you know, uh, but they
578
00:31:27,456 --> 00:31:33,762
become like, yeah, these more autonomous,
support capabilities, for what we do, But
579
00:31:33,762 --> 00:31:38,212
I think this is where things get really
interesting is because, okay, we're, we're
580
00:31:38,212 --> 00:31:43,092
used to starting to have this interaction
with chat GPT, just from a conversational
581
00:31:43,092 --> 00:31:46,922
perspective, you know, and particularly
with some of the latest frontier models
582
00:31:46,922 --> 00:31:50,922
on these LLMs, you can actually see them
do the reasoning and you can ask them
583
00:31:50,922 --> 00:31:55,112
and you're like, yeah, I could kind of
figure out a plan for this, the, the
584
00:31:55,112 --> 00:32:01,102
leap from that conversational AI to truly
being an agent is like, okay, well, now
585
00:32:01,102 --> 00:32:02,902
I've empowered this thing to take action.
586
00:32:02,992 --> 00:32:04,772
Action out in the world.
587
00:32:04,868 --> 00:32:08,954
and some of the most recent, announcements
from folks like Anthropic and OpenAI are
588
00:32:08,954 --> 00:32:13,973
about, you know, giving these, LLMs, the
ability to, uh, I think what Anthropic
589
00:32:13,973 --> 00:32:18,253
calls it like computer use, it's this
idea that you can start to try and like
590
00:32:18,273 --> 00:32:20,493
operate some of your software for you.
591
00:32:20,853 --> 00:32:22,843
I think that stuff's really interesting.
592
00:32:23,958 --> 00:32:29,918
to me, when I look at the greatest
opportunity for agents to be put into
593
00:32:29,918 --> 00:32:35,698
production, it's less about having them
work with software the way we humans
594
00:32:36,178 --> 00:32:41,458
would work with software of like, Oh
yes, uh, navigate to this menu choice
595
00:32:41,458 --> 00:32:45,048
and drop down here and pick this and
fill out these three form fields.
596
00:32:46,118 --> 00:32:50,663
Not that it is impossible, but it's
frankly, Boy, a really circuitous
597
00:32:50,663 --> 00:32:55,223
way, you know, for a computer to
interact with other computer systems
598
00:32:55,223 --> 00:32:56,313
to be able to get things done.
599
00:32:56,313 --> 00:33:00,963
The way in which do that is we
do it through APIs, you know, and
600
00:33:00,963 --> 00:33:05,073
this is the thing that is very
exciting for me is this rise of the
601
00:33:05,093 --> 00:33:09,093
AI intelligence to create agents.
602
00:33:09,563 --> 00:33:14,313
Is happening at the same time that
we've been on a good streak for
603
00:33:14,313 --> 00:33:19,583
the past decade of more and more
software is now built with open APIs.
604
00:33:19,903 --> 00:33:24,243
You know, this was a function of we ended
up with these heterogeneous tech stacks.
605
00:33:24,573 --> 00:33:26,343
People needed to
integrate things together.
606
00:33:26,343 --> 00:33:27,473
How do you integrate it together?
607
00:33:27,483 --> 00:33:29,643
Well, you have APIs that
can talk to each other.
608
00:33:29,653 --> 00:33:33,903
We have all software categories
integration platform as a service, you
609
00:33:33,903 --> 00:33:35,643
know, build around that capability.
610
00:33:35,913 --> 00:33:41,143
Um, And without going out on my soapbox
of why APIs are such an amazing thing
611
00:33:41,153 --> 00:33:44,553
for, you know, I wish more software
vendors did a better job with more APIs.
612
00:33:45,953 --> 00:33:47,603
They're at least headed
in the right direction.
613
00:33:47,623 --> 00:33:52,283
I think having that converge with this
AI intelligence for agents to then
614
00:33:52,283 --> 00:33:57,843
be able to take actions through those
APIs is that combination is going to
615
00:33:57,843 --> 00:34:00,403
just unlock a tremendous amount of.
616
00:34:01,808 --> 00:34:05,839
Justin: Is the agent, an AI of a kind
of different nature than what we're
617
00:34:05,839 --> 00:34:09,819
getting when we're chatting with chat
GPT, or is it more just it's like
618
00:34:09,819 --> 00:34:14,319
chat GPT with lots of extra context
empowered to take action through
619
00:34:14,329 --> 00:34:16,539
APIs or through access to interfaces?
620
00:34:16,817 --> 00:34:20,827
Scott Brinker: I think the thing
that takes it one step further is the
621
00:34:20,827 --> 00:34:22,456
ability for it to be a feedback loop.
622
00:34:22,951 --> 00:34:26,551
I mean, like, if you have an agent,
you know, it's not just about
623
00:34:26,551 --> 00:34:28,210
like, oh, it comes to a plan.
624
00:34:28,556 --> 00:34:30,206
It can kind of have done that already.
625
00:34:30,255 --> 00:34:32,556
Alright, now the next step
was, okay, it can take action.
626
00:34:32,710 --> 00:34:33,801
Action on that plan.
627
00:34:33,801 --> 00:34:38,271
It can call, you know, these
APIs or primitive sense can
628
00:34:38,281 --> 00:34:39,741
like manipulate the computer.
629
00:34:39,931 --> 00:34:40,969
UI for you.
630
00:34:41,271 --> 00:34:45,071
but then the step beyond that, that
gets really interesting is it can
631
00:34:45,071 --> 00:34:49,221
then feedback into its decisioning.
632
00:34:49,346 --> 00:34:50,956
What happened as a result?
633
00:34:51,306 --> 00:34:53,406
Oh, this is the result I got.
634
00:34:53,406 --> 00:34:55,016
That was not what I expected.
635
00:34:55,065 --> 00:34:58,936
Okay, let me adjust my plan and I'm
going to try this other thing or, Hey,
636
00:34:58,936 --> 00:35:00,736
wow, this looks like this is a problem.
637
00:35:00,736 --> 00:35:03,856
I want to flag this and create an
exception, you know, for someone,
638
00:35:04,176 --> 00:35:06,476
you know, that sort of feedback loop.
639
00:35:06,526 --> 00:35:10,016
And again, this is all very early
with people experimenting with stuff,
640
00:35:10,016 --> 00:35:13,636
but I think that's what starts to
make this really powerful because.
641
00:35:14,536 --> 00:35:20,126
You know, we've, again, over the past
10 years, automation has been one
642
00:35:20,126 --> 00:35:24,756
of those themes that's made its way
through pretty much every facet of
643
00:35:24,756 --> 00:35:29,146
the tech stack, but certainly in rev
ops, marketing ops, sales ops, right?
644
00:35:29,146 --> 00:35:33,476
I mean, marketing automation platforms,
the very name of these products, you know,
645
00:35:33,476 --> 00:35:35,316
are about automation at the center of it.
646
00:35:35,426 --> 00:35:39,311
but a lot of it's been this very
deterministic rules based automation.
647
00:35:39,682 --> 00:35:43,392
where things get dicey is when
something goes wrong or it
648
00:35:43,392 --> 00:35:45,482
doesn't work quite as expected.
649
00:35:45,794 --> 00:35:48,994
Usually then we've had to sort of
like surface that up to then the
650
00:35:48,994 --> 00:35:50,922
human administrator to figure it out.
651
00:35:51,351 --> 00:35:55,561
to allow these AI agents to be
in a position where they can kind
652
00:35:55,561 --> 00:35:57,548
of figure it out for themselves.
653
00:35:57,754 --> 00:35:59,154
Boy, that's a huge breakthrough.
654
00:35:59,481 --> 00:36:02,291
Justin: Maybe if you'll indulge me in
a thought experiment, because this is,
655
00:36:02,302 --> 00:36:03,691
it's just a really interesting thing.
656
00:36:03,717 --> 00:36:06,936
something in marketing operations
that is A common task, you know,
657
00:36:06,946 --> 00:36:10,756
building out some kind of campaign or
program or email within a marketing
658
00:36:10,756 --> 00:36:13,466
automation platform, whether that's
Marketo or HubSpot or something else,
659
00:36:13,564 --> 00:36:18,186
quite often we have internal service
resources or teams that do that.
660
00:36:18,186 --> 00:36:19,636
So a marketer says, Hey, I want an email.
661
00:36:19,636 --> 00:36:23,180
They do a ticket, someone on the marketing
ops team will go and build that for them.
662
00:36:23,355 --> 00:36:26,540
what would be involved if we wanted to
make an agent to replace that, which
663
00:36:26,540 --> 00:36:29,780
it sounds like we could, whether the
agent was using APIs, which many of
664
00:36:29,780 --> 00:36:33,158
these tasks are possible through APIs
or had to do it through the interface.
665
00:36:33,638 --> 00:36:37,068
Like, how do you even get started
if someone wanted to do that?
666
00:36:44,032 --> 00:36:47,892
Scott Brinker: Like, is that a
set of steps that an agent could
667
00:36:47,892 --> 00:36:49,342
actually, you know, take over?
668
00:36:49,362 --> 00:36:49,622
All right.
669
00:36:49,622 --> 00:36:53,632
Well, so I've got to like, you know,
let, is it getting full copy or is
670
00:36:53,632 --> 00:36:55,072
it only getting sort of a framework?
671
00:36:55,072 --> 00:36:55,872
Does it adjust it?
672
00:36:55,882 --> 00:36:58,682
Does it check it up against,
uh, you know, brand voice?
673
00:36:58,712 --> 00:36:59,442
Okay.
674
00:36:59,762 --> 00:37:02,202
Now, like, do I think about
like, okay, who am I doing
675
00:37:02,202 --> 00:37:03,882
for like audience selection?
676
00:37:04,197 --> 00:37:05,307
You know, who's the list?
677
00:37:05,317 --> 00:37:07,167
What should be the criteria for that?
678
00:37:07,187 --> 00:37:08,327
Um, okay.
679
00:37:08,327 --> 00:37:11,687
Now I've, you know, got my email,
like, you know, what's the right
680
00:37:11,687 --> 00:37:14,687
delivery schedule, you know, for
these folks, or there are other
681
00:37:14,687 --> 00:37:16,717
competing, you know, messages.
682
00:37:16,717 --> 00:37:19,407
How do we make sure, you know, we've
got the right throttling on that.
683
00:37:19,737 --> 00:37:20,027
I don't know.
684
00:37:20,027 --> 00:37:23,127
Maybe there's things of like a check
of like the accessibility, you know,
685
00:37:23,127 --> 00:37:27,325
there's like a whole series of things
that you could imagine us doing that
686
00:37:27,375 --> 00:37:30,125
with more traditional automation.
687
00:37:30,185 --> 00:37:30,805
It's just.
688
00:37:30,957 --> 00:37:35,567
Some of these things were so qualitative
in how they needed to be evaluated
689
00:37:35,577 --> 00:37:39,306
and like traditional, you know,
machine learning, automation AI just
690
00:37:39,796 --> 00:37:44,116
wasn't quite able to capture that as
well as, you know, these LLMs have
691
00:37:44,116 --> 00:37:46,086
almost like the mirror opposite.
692
00:37:46,291 --> 00:37:48,651
You know, they're particularly
good at some of those like
693
00:37:48,661 --> 00:37:50,171
squishy, qualitative things.
694
00:37:50,171 --> 00:37:54,931
I think if you mirror those two things,
it wouldn't all surprise me, uh, you know,
695
00:37:54,931 --> 00:37:58,291
to have more and more of these agents
show up that can, yeah, kind of like take
696
00:37:58,291 --> 00:38:02,951
a ticket from a marketer, basically roll
together, you know, the whole campaign.
697
00:38:03,456 --> 00:38:08,016
Presumably for at least some time
here, then present it to a human who
698
00:38:08,016 --> 00:38:11,276
will review it, make sure, like, okay,
yeah, this is, you know, what I wanted,
699
00:38:11,766 --> 00:38:15,787
but if that reduces, the work and the
time, involved in putting one of those
700
00:38:15,787 --> 00:38:22,727
campaigns together from again, like three
days to three hours, a huge, huge leap.
701
00:38:23,753 --> 00:38:29,113
Justin: the fascinating thing about
that for me is like every tool
702
00:38:29,113 --> 00:38:31,043
is like putting their own little.
703
00:38:31,398 --> 00:38:32,508
AI features in place.
704
00:38:32,518 --> 00:38:35,528
Like I use Zapier and you can go and
build his app the traditional way,
705
00:38:35,528 --> 00:38:39,768
like drag your little things in and
configure them where you can type
706
00:38:39,768 --> 00:38:41,558
something in and be like, and do it.
707
00:38:41,578 --> 00:38:43,438
And it's doing an okay job of doing that.
708
00:38:43,438 --> 00:38:47,109
But quite often as a professional
in that I'm maybe more inclined to
709
00:38:47,109 --> 00:38:48,869
just do it myself a lot of the time.
710
00:38:49,389 --> 00:38:52,889
But if every tool has those little
things, you're still needing a human
711
00:38:52,889 --> 00:38:54,059
there to stitch it all together.
712
00:38:54,089 --> 00:38:57,099
But now you have this agent that is
kind of outside of all those systems,
713
00:38:57,099 --> 00:38:58,499
but can interact with all of them.
714
00:38:58,934 --> 00:39:04,244
It's trained on your brand and company
specific policies and procedures and
715
00:39:04,244 --> 00:39:06,984
can chat with the marketer on your
behalf and then go out and execute
716
00:39:07,014 --> 00:39:11,333
and take feedback and make changes
like that's fairly revolutionary.
717
00:39:11,333 --> 00:39:13,733
Like, are we, are we, do you
think that all the systems
718
00:39:13,733 --> 00:39:15,522
are in place to do that today?
719
00:39:15,522 --> 00:39:18,912
Or are we still like a step or two away
from an agent being that autonomous?
720
00:39:19,651 --> 00:39:22,152
Scott Brinker: Yeah, I think a
lot of the ingredients are there.
721
00:39:22,529 --> 00:39:25,429
, again, this is one of these things
where, just like we were talking earlier
722
00:39:25,429 --> 00:39:29,461
here about, you know, the uncanny
valley of, you know, uh, AI video
723
00:39:29,501 --> 00:39:31,301
editing and generation and whatnot.
724
00:39:31,329 --> 00:39:33,705
you know, it's particularly when
you're talking about like actually
725
00:39:33,705 --> 00:39:37,565
running these operations, you know,
for multi billion or multi billion
726
00:39:37,565 --> 00:39:43,430
dollar, marketing organization,
your tolerance for error in there.
727
00:39:43,474 --> 00:39:46,354
it's probably such that we're probably
a little bit of ways, you know, from
728
00:39:46,354 --> 00:39:49,234
wanting to let, you know, the AI
agents run that stuff autonomously.
729
00:39:49,284 --> 00:39:52,847
They've got some work to do to prove that,
the reliability is going to be there.
730
00:39:52,857 --> 00:39:56,557
But I think directionally,
yeah, it makes a ton of sense.
731
00:39:56,675 --> 00:40:01,635
Yeah, I mean, you know, it's an
interesting question as to will this
732
00:40:01,665 --> 00:40:05,825
be a different layer of the tech stack?
733
00:40:06,069 --> 00:40:10,474
I think one of the things that's
really Interesting right now is there's
734
00:40:10,484 --> 00:40:14,127
not one agent platform out there.
735
00:40:14,127 --> 00:40:16,447
There's not like there's
one home for agents.
736
00:40:17,006 --> 00:40:20,566
pretty much that every
tool seems to be developing
737
00:40:20,576 --> 00:40:22,296
capabilities for its own agents.
738
00:40:22,296 --> 00:40:28,236
There's a standalone agents, the LLMs
themselves are creating agents, you know?
739
00:40:28,286 --> 00:40:31,446
And so I think one of the most interesting
things is going to be like, okay, yeah,
740
00:40:31,446 --> 00:40:32,846
we've got all these like different.
741
00:40:33,141 --> 00:40:39,896
Agents out there, what's the, what's the
orchestration, will that be the existing,
742
00:40:40,086 --> 00:40:43,746
uh, in common platforms, you know, that
have served as the orchestrators and
743
00:40:43,746 --> 00:40:49,014
sort of current go to market automation,
orchestration, uh, Uh, I'm certain
744
00:40:49,224 --> 00:40:53,164
everyone who is an incumbent there,
uh, wants that to be the case, you
745
00:40:53,255 --> 00:40:53,365
Justin: Yeah.
746
00:40:53,444 --> 00:40:55,554
Scott Brinker: but I think it
would also be again to, you
747
00:40:55,554 --> 00:40:57,834
know, disruptive innovation.
748
00:40:57,834 --> 00:41:01,594
Like, yeah, are there other places
where people might be trying to, like,
749
00:41:01,594 --> 00:41:05,268
serve as the ultimate orchestrator,
of these different agent capabilities?
750
00:41:05,426 --> 00:41:06,926
Personally, I would keep an eye on that.
751
00:41:08,452 --> 00:41:11,612
Justin: I mean, so to an extent, you've
just asked yourself the question that I
752
00:41:11,612 --> 00:41:15,722
wanted to ask next, which is, you know,
you, you referred to the importance of
753
00:41:15,722 --> 00:41:20,028
this orchestration and you kind of called
it this coordinating centers of gravity.
754
00:41:20,028 --> 00:41:21,677
I thought that was a really good phrase.
755
00:41:21,840 --> 00:41:26,200
In this big ops environment that provides
cohesion and governance to the plethora
756
00:41:26,200 --> 00:41:27,890
of apps and agents and automations.
757
00:41:28,410 --> 00:41:30,260
And what does that look like?
758
00:41:30,270 --> 00:41:35,283
is it just a workado on steroids that
kind of is able to provide the API
759
00:41:35,283 --> 00:41:40,693
pathways for agents and other apps
that are on the periphery to interact
760
00:41:40,693 --> 00:41:42,143
with each other and to take action.
761
00:41:42,226 --> 00:41:44,580
Is it something else that we
don't really understand yet
762
00:41:44,593 --> 00:41:45,777
or haven't conceived of yet?
763
00:41:45,797 --> 00:41:46,447
What will it be?
764
00:41:46,639 --> 00:41:47,289
Scott Brinker: Yeah.
765
00:41:47,359 --> 00:41:53,109
Uh, and this would be one of the things
I will hesitate to make a prediction.
766
00:41:53,186 --> 00:41:56,455
because I think there's old model,
you know, the way in which we would
767
00:41:56,455 --> 00:42:00,325
historically have thought of this
is something that is a very clear
768
00:42:00,335 --> 00:42:04,353
conductor, to the orchestra, uh, your
example of like, you know, workado,
769
00:42:04,367 --> 00:42:08,420
you know, as a version of that, I
think what's interesting as a parallel
770
00:42:08,460 --> 00:42:14,670
is to consider what has happened
if the data layer inside companies.
771
00:42:14,963 --> 00:42:18,363
so for a while, what we had was we had
all these little mini databases attached
772
00:42:18,363 --> 00:42:21,653
to every single individual different app
that lived in their own little silos.
773
00:42:21,983 --> 00:42:26,058
you know, now with the, More and
more of those silos opened up.
774
00:42:26,308 --> 00:42:29,908
We started to, in particular with things
like cloud data warehouses and lighthouses
775
00:42:29,908 --> 00:42:34,048
and things like that, be able to get, you
know, data, you know, flowing more freely.
776
00:42:34,221 --> 00:42:38,229
but then the question became like,
okay, we've now got all this data being
777
00:42:38,229 --> 00:42:41,239
generated or being used by different
things throughout our organization,
778
00:42:41,239 --> 00:42:43,709
but how is this being orchestrated?
779
00:42:44,634 --> 00:42:49,219
you've now seen a multiple different
architectures, of how people
780
00:42:49,229 --> 00:42:50,429
think about dealing with that.
781
00:42:50,449 --> 00:42:53,589
You know, do we do it entirely
through one central authority?
782
00:42:53,949 --> 00:42:57,569
Do we do it through this concept of
data products, you know, where different
783
00:42:57,569 --> 00:43:01,229
teams, like, you know, manage things
as a product, you know, interfacing to
784
00:43:01,229 --> 00:43:05,469
others, how much do you allow things
to be federated, you know, uh, where
785
00:43:05,469 --> 00:43:07,199
do you allow there to actually be.
786
00:43:07,323 --> 00:43:11,314
redundancies or, desyncs, because
in the grand scheme of things,
787
00:43:11,694 --> 00:43:15,514
what you're losing, you know, from
that redundancy is more than gain
788
00:43:15,514 --> 00:43:18,784
for, you know, by the efficiency
or the speed on some other level.
789
00:43:18,946 --> 00:43:24,986
I suspect it's going to be, if I had to
take a guess, it would be something more
790
00:43:25,076 --> 00:43:29,246
like that where, boy, this ability at the.
791
00:43:29,716 --> 00:43:33,506
Services layer, not the data
layer, but the services layer.
792
00:43:33,831 --> 00:43:38,341
it is going to be a free flowing mechanism
where like, hey, it's all in the cloud.
793
00:43:38,371 --> 00:43:40,141
Anything can call anything else.
794
00:43:40,391 --> 00:43:43,401
So it's going to be then about the
architectures we put around that.
795
00:43:43,411 --> 00:43:45,081
Will some of them be centralized?
796
00:43:45,966 --> 00:43:48,696
Yeah, I think those will
be the simplest models.
797
00:43:49,146 --> 00:43:53,056
But could you imagine one where
people start to actually design
798
00:43:53,056 --> 00:43:55,980
them as decentralized, systems?
799
00:43:56,720 --> 00:43:57,570
Yeah.
800
00:43:57,660 --> 00:43:59,120
Uh, could you could see that?
801
00:43:59,130 --> 00:44:02,530
In fact, actually, again, like the
larger, larger you get as a company,
802
00:44:02,530 --> 00:44:05,710
it's almost like, I mean, we see this
in the data products, you know, side,
803
00:44:05,710 --> 00:44:09,500
it's just at some level, you know, a
hundred percent centralized solution
804
00:44:09,850 --> 00:44:14,890
just becomes incredibly unwieldy, you
know, at this massive enterprise scale.
805
00:44:15,171 --> 00:44:16,011
Justin: un, unmaintainable.
806
00:44:16,061 --> 00:44:19,651
And to your point about like digital
coworker, I had this, this chat
807
00:44:19,651 --> 00:44:24,881
with another founder who's working
on an AI product, like an AI that
808
00:44:24,891 --> 00:44:28,411
has all the same context as you,
you know, that reads every email.
809
00:44:28,806 --> 00:44:33,526
That is in your project management system
that's getting all the tickets that's
810
00:44:33,536 --> 00:44:36,935
getting the meeting notes or is attending
the meeting could become could take
811
00:44:36,935 --> 00:44:42,615
your job or could become this incredible
resource to sort of dialogue with, like,
812
00:44:42,655 --> 00:44:43,575
all right, what are we going to do?
813
00:44:43,585 --> 00:44:46,445
You know, like this, this kind of
always on partner thought partner
814
00:44:46,745 --> 00:44:50,095
working through problems with you,
like every person could have one.
815
00:44:50,140 --> 00:44:52,320
how much does that actually
amplify the ability?
816
00:44:52,685 --> 00:44:56,375
Of a person and it's, I mean, it's hard to
think of that being centrally coordinated.
817
00:44:56,375 --> 00:44:59,735
That's kind of like your, your AI
mini me in a way, sort of following
818
00:44:59,735 --> 00:45:01,214
you around and partnering with you.
819
00:45:01,436 --> 00:45:05,014
but that, sort of surrealistic kind
of futuristic idea doesn't actually
820
00:45:05,014 --> 00:45:07,954
seem that far fetched now, given
everything we've talked about.
821
00:45:08,284 --> 00:45:11,784
Scott Brinker: No, in fact, actually, this
is, I would argue, one of the use cases
822
00:45:12,206 --> 00:45:17,978
I talk to who are now doing a lot with
AI, like it's one of the most reliable
823
00:45:18,041 --> 00:45:23,881
use cases is having these AI again, like
whether it's feeding it all of your own
824
00:45:23,881 --> 00:45:26,292
stuff, or quite frankly, having it feed.
825
00:45:26,459 --> 00:45:29,349
Things of like, oh, well, this is
everything I've ever received from
826
00:45:29,349 --> 00:45:33,140
my boss, you know, you know, and
being able to, like, have these
827
00:45:33,140 --> 00:45:36,824
sorts of dialogues, you know, it's
like a Socratic method, almost
828
00:45:36,861 --> 00:45:37,031
Justin: Yes.
829
00:45:37,184 --> 00:45:41,874
Scott Brinker: uh, you know, just how
it changes our ability to look at things
830
00:45:41,874 --> 00:45:43,484
from, like, a different perspective.
831
00:45:44,169 --> 00:45:49,019
because it's both got that combination
of, uh, we won't say it's omniscient,
832
00:45:49,349 --> 00:45:53,229
uh, you know, yet, but it's like,
boy, it, it, it does have more data
833
00:45:53,229 --> 00:45:56,419
than pretty much any other human you
would, you know, talk to about that.
834
00:45:56,677 --> 00:45:59,826
but also again, in an environment
here where there's, there's no
835
00:45:59,826 --> 00:46:03,216
emotion on the other side, there's no
judgment, there's no, you know, like.
836
00:46:03,773 --> 00:46:07,453
Privacy thing of like, yeah, now the AI
is going to go like, you know, whisper
837
00:46:07,453 --> 00:46:09,273
behind my back about that conversation.
838
00:46:09,323 --> 00:46:12,503
I mean, it just, it's this amazing
thing, I think, to give people
839
00:46:12,503 --> 00:46:16,473
a lot of freedom to just like
creatively dialogue and explore.
840
00:46:16,890 --> 00:46:19,478
Different ways of looking at
things, coming up with new ideas.
841
00:46:19,715 --> 00:46:22,245
Justin: It really does feel to me that
we were creeping closer and closer to
842
00:46:22,245 --> 00:46:27,715
having like the onboard ships, computer
of Star Trek, just available to us
843
00:46:27,745 --> 00:46:30,815
where it's like, you're talking to
them, like computer, do this, do that.
844
00:46:31,075 --> 00:46:35,021
Help me solve this problem,
become this, and an assistant.
845
00:46:35,247 --> 00:46:37,764
do you ever sort of take a step like
sometimes when I think about these
846
00:46:37,764 --> 00:46:41,013
things, I'm like, this is like, yes,
it could be weird and dystopian and
847
00:46:41,013 --> 00:46:43,923
there could be bad outcomes, but
it's also just incredible like that.
848
00:46:43,923 --> 00:46:45,083
This stuff exists right now.
849
00:46:45,093 --> 00:46:45,443
Like what?
850
00:46:46,333 --> 00:46:48,073
What timeline did we wander into that?
851
00:46:48,073 --> 00:46:49,153
This is happening.
852
00:46:49,163 --> 00:46:49,903
It is so weird.
853
00:46:50,146 --> 00:46:50,656
Scott Brinker: Yeah.
854
00:46:50,757 --> 00:46:55,507
you know, again, probably, probably
reaching too far back into the archives,
855
00:46:55,507 --> 00:47:00,657
but you know, a very early Star Trek
movie was a Star Trek four where they
856
00:47:00,657 --> 00:47:05,457
travel in time to like what at the time
was, you know, 1980s, you know, earth.
857
00:47:05,536 --> 00:47:08,237
and was it Scotty, you know, he
has to interact with one of the
858
00:47:08,237 --> 00:47:10,607
computers and he's like trying
to talk to it at the computer.
859
00:47:10,607 --> 00:47:11,947
You know, the guy hands him the mouse.
860
00:47:11,947 --> 00:47:15,809
He's like talking to the mouse,
like, this is a huge, big laugh line.
861
00:47:15,819 --> 00:47:16,489
Like, well, of course.
862
00:47:16,919 --> 00:47:19,283
Justin: I think that's probably all
that we have time for today, but Scott,
863
00:47:19,293 --> 00:47:21,463
thank you so much for chatting with me.
864
00:47:22,168 --> 00:47:23,048
Super interesting.
865
00:47:23,058 --> 00:47:23,838
Super exciting.
866
00:47:23,848 --> 00:47:26,768
Again, I encourage everyone to read
the full report, which is obviously
867
00:47:26,768 --> 00:47:29,938
a lot of detail that we didn't get
to cover, but thank you for, uh, for
868
00:47:29,938 --> 00:47:32,978
putting that out and being such a great
resource to the marketing ops community.
