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Brian: to the rebooty show.

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I am Brian Morrissey.

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Today I'm speaking with Pete Pachal, a
veteran news editor who runs the Reliably
Excellent Media Co Pilot newsletter.

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Pete is deep in the weeds about the
intersection of AI and news publishers.

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it's Topic that I is very near and
dear to my heart and I always rely on
a media copilot to keep me up to date.

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Pete, is, an expert in this area.

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And we discussed the recent news of
deep seeks apparent AI Sputnik moment.

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and what it means downstream for
publishers beyond, The fact that
everyone's 401k took a hit today.

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we also talk about actual use cases
of AI improving the news product.

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spoiler, there really
aren't that many to date.

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And the implications of agentic AI,
which is where, basically AI agents go
out and accomplish tasks, and whether
that will make websites Obsolete.

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it's a real possibility.

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And also the wisdom, or folly of site
specific chatbots that we're starting to
see from publishers, and the underwhelming
example of AI news aggregator particle,
as well as why Pete is obsessed with

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Google's Notebook LM and whether we will
see the contours of a grand deal to iron
out the economics of news content if AI
keeps gaining ground the way it has been.

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This episode is brought to you by EX.CO,
the machine learning video platform
trusted by leading media groups like
Advanced Local, The Arena Group, Hearst
Newspapers, NASDAQ, News Corp, and more.

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The translation of this is that basically
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So outdated programmatic pipes,
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Get in touch with EX.CO's video
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That is E X dot C O Thanks so much, EX.CO.

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Now on to my conversation with Pete.

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Well, Pete, thank you so much
for, for joining me today.

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it was a busy weekend with this deep seek,
announcement, but I want to get into, I
want to get into that a little bit, but,
and the overall sort of AI arms race,

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but really, you know, the idea of this,
for me was to get a look ahead at how
AI is going to impact overall the media
industry in the, in the year to come.

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and we'll get into news, but
beyond that, but this weekend,
AI never sleeps, I guess.

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And, a new Chinese, AI model came out
called DeepSeek, and it does a lot
of the things these other models do.

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I played around with it a little bit,
I read about it more and, you know, to
me, the, the big difference is how, is
how Much a cost to, to put together.

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at least the journals reporting had 5.

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6 million versus the, you know, billion
plus, that U S models are making, I,
which is by the way, is a little ironic
because the tech people keep claiming
that they're going to eradicate wasteful

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spending from the government and I
start to wonder if, if what they're
doing is a giant waste of money, that
maybe they're not the people to do that,
but that's for a different podcast.

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what is the significance of DeepSeek?

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It's, it's being painted, you know, very
hastily as something of a Sputnik moment.

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And, and by that, it meant that
like, you know, when the, when the
Soviets came out with Sputnik, it
was like, Oh no, they're going to
be able to compete technologically.

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And, and the Soviets actually did.

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Did do a great job.

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Soviet engineering was amazing for what
it could do within the constraints.

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And it seems like something a little
bit similar here because the US, in
its alliance with our tech oligarchs
has tried to, control, the transfer of,
of technology and GPUs to, to China.

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So give me your, your, the,
the, the assessment of, of
the significance of this.

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Pete: Yeah.

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Well, first thanks for having me.

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It's super thrilling to be here.

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Always love talking to you.

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so yeah, the deep seek thing.

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Like you, I think you nailed
it out of the gate and that the
big factor here is, is cost.

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And what I try to do is I sort
of think about this less in
the sort of macro level, but we
can get into that a little bit.

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And more on the sort of like,
what is the trickle down effect of
something that is inherently cheaper.

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Just to run and, you know, essentially
like I even, well, you know, small time
people like me feel the cost of AI.

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Sometimes I try to automate a bunch of
different things like social and whatever,
and I have certain bots I've built.

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And sometimes in some of these platforms
that I use, like I notice my costs.

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Going up because of the model
I pick, whether it's for GPT 4.

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0 versus the 4.

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0 mini.

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And, you know, it's, it's for a
solopreneur like me, it adds up to
hundreds of dollars, not, not the tens of
thousands, but you can sort of scale that

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for sort of publishers and media folks
who are trying to, you know, essentially
automate chunks of their operations.

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And it's like, Oh, okay.

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So I need to use.

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This other model that doesn't give
me quite as good results, which
might actually sort of place it
below a threshold of quality, and
therefore the AI thing is not viable.

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Now this creates a whole level of
viability for those scaled projects.

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potentially if, if they.

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End up using it.

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So, so, so that's the, the most sort of
immediate sort of trickle down effect.

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I see.

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Now, the other thing about this
is that it's out of China, right?

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Like, and I kind of feel like, well,
didn't we just have a big row over Tik TOK
and, you know, Influence and having that
power, a certain amount of infrastructure.

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Now it's obviously super
early days for deep seek.

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I mean, you know, just came out and
no one's really, you know, using it
in a, in a big way, but I feel like if

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it does become this thing where it's
essentially as good as the current models
and sort of keeps pace with whatever.

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Is, is coming out in, in a sort of
open source, commercial way, you
know, like it's going to create some

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of these questions and influence
and, and, you know, is there some,
some strings behind the scene?

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Whether that's founded or not,
you know, knowing that it's, you
know, kind of the same kind of open
source deal as llama, it's, it's,

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Brian: Yeah, I mean, it's, it's key
that it's, it's, it's not open AI,
which is not open, it is open source.

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And so that would seem to get around.

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I know the, there's already been
some early, you know, whenever anyone
gets access to one of these, these
chatbots, they try to get it to like,

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say something bad or, and so if it's
from China, you go and you ask it about
Tiananmen Square, Xi Jinping, and, and,
and you see that it's, Goose prize.

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it's not going to return, some sort
of, you know, answers about the
massacre there or about Xi Jinping's,
record with the Uyghurs or whatnot.

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but I would guess, you know, because
it's just a model and it's open
source that the idea is anyone
can take that and, and build.

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You know, a model that doesn't have those
kind of quote unquote controls on it.

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Now, I, I'm a realist.

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I mean, I, I've, I've been in the midst
of this PR campaign from Silicon Valley
that, you know, they are, they all of a
sudden have clock have wrapped themselves

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in the American flag after being these
massive multinational corporations that
use tax havens and all kinds of things.

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But okay, whatever, it's a business.

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And, it puts a lot of pressure on
them because, you know, they have
been firing, telling us that we need
to fire back up, Three Mile Island.

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And, you know, Sam Altman at one point
was talking about raising 7 trillion,
which I don't know where he came up
with that figure, in order to, to get
more, data centers up and running.

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And this is just, at least in
the initial, Reports that I've
read is just far more efficient.

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So I think the biggest impact right
now is like, you know, the stock market
is, do not look, do not check your
401k, I guess, because the stock market

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is dominated by these, these tech
companies and, And it's, it's calling
into question whether or not all of this
spending is, is, is really necessary.

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That's, you know, a tech
story and a finance story.

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It doesn't really impact to me, you know,
the idea that the costs will come down
is just axiomatic to All of technology,
you know, all of these products are

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going to be, you know, very jury rigged
in the beginning and they're going to be
incredibly expensive and they're gonna
be clunky and we all dealt with that.

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Anyone who used the Nokia and 95
phone can attest to that these things
do get better, and they get cheaper.

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so it, it will be interesting to see
how this, because it would seem to speed
up the deployment of this technology,
like you were saying, into real use

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cases, because I guess sometimes with
this, I struggle a little bit with
the AI story because it lands in, it
could, it could, it might, it should,

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you know, range when we start to talk
about actual, you know, Real life
use cases that are not incremental,
you know, and so where where are we?

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With that right because to me a
lot of the the general coverage of
this It falls into either academic.

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I don't care what agi I don't care
like I don't know why I should care
about some nebulous line where it has
crossed and whether it's PhD level

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or whether it just has like a master
of science or whatever What the only
reason any of this stuff matters?

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Is if it has real impact on how we live
and how we work like that's all that
matters this other stuff is nonsense
and the You know, pouring billions
or trillions into data centers.

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Great.

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I saw this with Broadcom and Global
Crossing with Lane Broadband.

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And there was a ton of
money lit on fire then.

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There was a lot of companies
that went out of business.

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I assume it's going to be the
exact same right, right now.

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But like, where are we with
real life adoption of these
technologies and the impact?

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Because whenever I, like, we have
these dinners and I ask people in
the media industry, what are you
Give me like what you're using AI for
and like I just hear like chat GPT

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Pete: Yeah, yeah.

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It's funny.

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Like here, you know, when you're
reading about AI, when you're kind of
living it, like maybe like certainly

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I am and you as well, like you're kind
of in this bubble and you think, Oh
my God, everyone knows everything.

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It's, it's going so fast.

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I can't keep up.

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And then, you know, I, a big part of my
business is teaching, uh, newsrooms, PR
teams, creative teams, how to use AI.

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And then, you know, I sit down with these
groups and it's like, Oh, everyone's
still just kind of messing around
with a few prompts here and there.

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Right.

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So to your point of like, when is this
like real, when is it actually arrived?

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I think it's when that part
of my job kind of goes away.

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Right.

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When, when it's so inherent and so

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Brian: Let's stretch it out
Some a bit longer than Pete

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Pete: Yeah.

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Well, I hope so.

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You know, like I, I definitely see demand.

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So I think it's a ways out.

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Like, you know, so everything we hear
about, you know, deep seek and Stargate.

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And everything else, yeah, it's, it's
all exciting and a little scary, but
when it, you get down to the, the
general level, like my wife doesn't
even really use chat GPT at all.

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you know, it's not really
affecting the normies quite yet.

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I think we're going to get closer.

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So there's, there's a whole layer
of chat GPT use that I think.

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Layered up, or leveled up when they
introduced search and, you know, it
always could access the web and sort
of oblique ways, but when search came

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out, it's like, oh, you know, this can
actually, Potentially replace your,
your browser experience in some ways.

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And certainly it's an early
adopter thing right now to do that
and sort of change your default.

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But I feel like that's like the first
kind of step towards some future
where, you're just talking to the AI
and it's just bringing you things.

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This is sort of like the agentic.

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Brian: Yeah.

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So let's, let's break down the agentic.

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so, you know, I think, look, I,
I stepped outside of my apartment
the other day and I, I saw like a
Salesforce like agent force, billboard.

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I'm like, oh no, it's on now.

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When, when Salesforce is rolling
out the giant, the, the out of
home campaigns, you know, it.

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Pete: in the airports.

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Have you seen those?

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Brian: you know, when you, when you
see airport out of home, you know,
they're going to like jam this stuff
down our throats, like all year.

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So yeah, we're going to hear a ton,
ton about agents and agentic AI.

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And that's basically, I mean, my
sort of definition of it is they
go out and they do things for you.

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It's, it's the, at least
this is in theory, right?

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Like the idea is you're going to, it's
not going to like point you to a bunch
of links or tell you how to do something.

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It's going to go and do something.

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It will book the flight, et cetera.

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Now, when these things start.

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It's going to be so much, this
is my prediction, maybe I'm too
cynical, maybe, it's going to be so
much easier to just do it yourself.

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00:13:51,876 --> 00:14:00,926
Cause when people talk about it being
like infinite interns, I'm like, anyone
who's had like interns knows that's
like, sometimes like it's more work
than actually, and I'm like, infinite?

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00:14:01,106 --> 00:14:02,306
Are we sure we want that?

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00:14:02,306 --> 00:14:04,326
Can we just start a little bit lower?

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Pete: infinite managers

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Brian: You know, cause I, I, this
is like a little bit of an aside,
but, I can remember I went to.

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I got out of J school in 2000.

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00:14:13,421 --> 00:14:16,706
I just got a note about our 25th
anniversary and I joked that, I

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00:14:16,911 --> 00:14:17,661
Pete: I just had mine.

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00:14:18,026 --> 00:14:27,386
Brian: I joked, I joked to the woman they
should have like those of us still sort of
in the profession, or I guess I still am
like, you know, bring us out like, D Day
veterans, like the last D Day veterans.

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But, I don't even know if I would
qualify, but the people that are like
still working for newspapers, let's bring
them out, give them a round of applause.

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but I can remember then we had a Super
Bowl party at the, for the 2000, Super
Bowl and, the, like the guy who is
the Dean of like the new media lab or
something, he, he insisted on ordering

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like the Domino's pizza, like through
the internet, because you could, And
guess what Pete, we had a keg party
with no pizza because that goddamn pizza
did not show up and something similar,

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00:14:57,341 --> 00:15:00,741
Pete: was always, what did the, the,
the progress bar on the dominoes just

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00:15:00,966 --> 00:15:04,476
Brian: this is before that, this is before
Crispin Porter Boguski got involved.

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This, this was like, you
know, very, very early.

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And it was like, everyone was like,
Can you just pick up the phone?

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So anyway, I think agents are going to
be something similar, but at the same
time, it, you know, everything starts like

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this and just like, you know, e commerce
took off, it didn't stay with the, just
pick up the phone to order the pizza.

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God damn it.

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it's going to get going, but what
give, like, what does this mean for,
for publishers in general, because
to me, if you play this out, like,
let's just assume that, that what.

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is claimed for, for this agents actually
eventually comes to fruition in a
real way, not a hack together way.

174
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And like a couple of years, like
to me, it's like, That's kind
of the end of websites, right?

175
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Because like websites are just like, this
is said to me at a dinner the other night,
like they're just some kind of UI front
end to a database at the end of the day.

176
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And it's just like calling
and retrieving data.

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And the website itself is
just kind of like a cloud.

178
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Clunky intermediary at some
point, but like what do you think
the big impact of this agentic
vision of AI is for publishers?

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Pete: Yeah.

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Well honestly it ties back to a little
bit what we were just talking about.

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'cause you know, there was a, we
were talking about like the Stargate
stuff and the big picture stuff and

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all this, you know, compute power
that we're gonna get and that power
and infrastructure, all that stuff.

183
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What that's gonna empower is a lot
better inference, time compute, right?

184
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Which is to say like when you actually
search for something online, some
information, it's actually gonna be
able to process so much more so quickly.

185
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That Right now, like AI search as it
exists is a little bit kind of like
the baby version, like it seems like
it's going out and reading all of these
articles in real time and doing it.

186
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And it's not really like, there's a
lot of schema and a lot of metadata
that's, that's still involved in that.

187
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but once we get there and, and you,
you can actually do this, that is a
hundred percent when like websites
are kind of obsolete, you know, it's
like, okay, now, like, as long as.

188
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We work out the monetization
and the, you know, the,

189
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Brian: It's a big

190
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Pete: licensing.

191
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That's a, yeah, it's a big asterisk,
but if that, you know, the models are
kind of emerging and you could see some,
someone running with the ball with that,

192
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then everything just kind of goes into
this blob of information as soon as it's
published and then, you know, as soon
as someone asks for something that that

193
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fits the pattern of it's, it's going
to get summarized and, you know, they
can go in and go deeper at some point.

194
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So, That's a bit, obviously it's a
bit of a ways off, but I think that
that is the, the train we're on.

195
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Brian: Do you see anything like that?

196
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You would point people to?

197
00:17:43,952 --> 00:17:54,111
that you can start to see this like
happening because I think one of
One of the big questions is how much
publishing Brand really matters and
the you know, I think a lot of times

198
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Particularly in journalism, you know
trust the trust flag comes out right
and it's just used as kind of like I
don't know gauze to some some degree.

199
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I feel like because it's never backed
up with actual You know data or or
evidence There's this just kind of like

200
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religious belief that you know, our our
audience trusts us They're loyal and
everything and and i'm like, I don't know.

201
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I mean the data i've seen is that
people Do not really for the most part
have a primary news source and The
obvious to me like the obvious sort of
mainstream sort of news experience is

202
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that it's very personalized It's very
tuned To, the, to the person's sort of
interest, maybe to their proclivities
and ideological, biases, or just beliefs.

203
00:18:47,839 --> 00:19:04,899
And that it is delivered in the format
that the person, the user or the audience
member, like consumer, I guess, prefers,
the idea that it's going to be packaged
by a bunch of, you know, newsrooms seems.

204
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I don't know.

205
00:19:05,814 --> 00:19:06,804
I don't know if it's arrogant.

206
00:19:06,804 --> 00:19:16,194
It just seems like sort of outdated,
like, okay, there's some people who
want the packaged version, but there's
some people who are going to, you
know, package it themselves, right?

207
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Like, I mean, that, that seems obvious,
or just like an obvious end point.

208
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and

209
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Pete: think,

210
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Brian: don't know what, what is,
what is your sort of take on that?

211
00:19:24,970 --> 00:19:26,870
Is that sort of how you see things going?

212
00:19:26,870 --> 00:19:32,080
Because I just like wonder, do you see
things out there that point towards this?

213
00:19:32,520 --> 00:19:35,370
This agentic future for, for news.

214
00:19:35,870 --> 00:19:36,240
Pete: yeah.

215
00:19:36,240 --> 00:19:41,350
So I'd point to two different trends
here, which are kind of one is
they're almost like the opposite of

216
00:19:41,350 --> 00:19:48,120
each other, but they're actually, I
think different ingredients of, of
what this agentic future might be.

217
00:19:48,120 --> 00:19:56,780
So one is just all the deals that
we're seeing with all, you know,
open AI and others, mostly open AI
right now with publishers, and it's
always kind of like the same group.

218
00:19:57,255 --> 00:19:58,805
But it is sort of happening, right?

219
00:19:58,805 --> 00:20:07,855
So it's like, you know, whether it's,
News Corp and everyone else and Reuters,
et cetera, you know, we're starting to
see that with Meta and Microsoft too
now they're, they're making these deals.

220
00:20:07,855 --> 00:20:16,645
So what this sort of, you know,
portends to me is like, oh, there is
a model emerging, but in the, in the
initial sort of phase of it, there's
going to be a certain sameness.

221
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To what you get, that is news
on a lot of these AI interfaces.

222
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And I think there's this counter
trend that people don't want that,
you know, and this is why I like sub
stack and, and individual creators.

223
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while, while, while that sort of
summary of sort of what's going
on is helpful, it's like, what
I really, what I really want is.

224
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The, the news and points of view
from the people, that, that I want,
that, that, that I'm following.

225
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Right.

226
00:20:45,208 --> 00:20:50,828
So the other trend is, is really
counterintuitive, but I would point
to the popularity of notebook LM.

227
00:20:51,328 --> 00:20:53,328
And what that is to me.

228
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Is yeah.

229
00:20:56,283 --> 00:21:03,133
So, so Google notebook LM, it's actually
been out for a while, almost a year,
I think when Google first released it
and it's essentially like a folder.

230
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And the way I sort of talk about is
like with chat GPT with, with your.

231
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Interaction with AI prioritizes the chat.

232
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I mean, you can attach
files to it, your chat.

233
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that's fine.

234
00:21:14,858 --> 00:21:23,448
What notebook LM prioritizes is
the files, like the data you're
giving it and the chat and the AI
interaction is sort of secondary.

235
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It sort of flips the script.

236
00:21:25,088 --> 00:21:29,348
On the chat bot, which it was
a super brilliant innovation.

237
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because what that empowered and what
really put it on the map is the idea.

238
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You can take that data and
reversion it into whatever you want.

239
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And their, their big product was podcast.

240
00:21:40,878 --> 00:21:44,498
Hey, let's have a couple of chatty
people talk about the data we have here.

241
00:21:44,808 --> 00:21:46,368
Brian: And they're very
human sounding, by the way.

242
00:21:46,368 --> 00:21:46,778
I've talked

243
00:21:46,908 --> 00:21:47,908
Pete: I did a great job

244
00:21:48,088 --> 00:21:52,580
Brian: They're, they, they, I
mean, they, I, I've mentioned
before, I have this like thing.

245
00:21:52,580 --> 00:21:59,474
I like keep like like a journal,
I guess, of like, of stuff I'm
thinking about with the business and
beyond, but, I do it like every day.

246
00:21:59,474 --> 00:22:04,354
I, and you know, I'm a writer, so
like I, I have a lot of data, like
over the last couple of years.

247
00:22:04,684 --> 00:22:08,134
It's very strange to listen to a
podcast that talk, talking about you.

248
00:22:10,294 --> 00:22:11,024
Pete: I couldn't do it.

249
00:22:11,024 --> 00:22:11,884
I haven't done that yet.

250
00:22:11,884 --> 00:22:12,664
I've thought about it.

251
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I think I inevitably will.

252
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I've got a bunch of notebooks

253
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Brian: They're very nice.

254
00:22:16,114 --> 00:22:19,764
I mean, AI, I will say this AI engine,
they have tuned them to be suck ups.

255
00:22:19,784 --> 00:22:20,654
They're all suck ups.

256
00:22:20,654 --> 00:22:22,084
They're never going to tell you there.

257
00:22:22,424 --> 00:22:26,484
So I think that's the problem
with the, the sort of truth thing
and trust thing, because like.

258
00:22:26,719 --> 00:22:28,469
They're too, they're too positive.

259
00:22:28,479 --> 00:22:32,499
Like when I ask, like, you know, Oh,
can I make a, this dish with this?

260
00:22:32,499 --> 00:22:34,309
This they're never like,
no, that's terrible.

261
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Like that's a terrible idea.

262
00:22:35,859 --> 00:22:36,009
Like,

263
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Pete: There's actually like a few actual
podcasts you can get in the, probably
more than a few in like the Apple podcasts
app that are just notebook LM feeds.

264
00:22:44,399 --> 00:22:50,669
And if you listen to a bunch of these
together, like, like a few, you, you
start to really feel the weakness of this.

265
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It's like, Oh, it's really cool
that they're conversational, but

266
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Brian: it's a parlor trick at some level.

267
00:22:55,329 --> 00:22:59,809
I mean, even the perplexity perplexity
has an AI, like, you know, summary.

268
00:23:00,208 --> 00:23:02,458
News podcast, podcast product.

269
00:23:02,508 --> 00:23:16,057
And it's kind of dull if you ask me,
but look, I think a lot of this points
to, and this is where I want to get
at with, with how you see newsrooms
and publishers using this technology.

270
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because look, it's good at summarization
and it's good at versioning and to
me, and it's good at pattern matching.

271
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So if you look at the, Professions, the
areas that have been affected the most.

272
00:23:28,197 --> 00:23:31,857
obviously developers, you know,
that's the sort of home run use case.

273
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It has not replaced the writers.

274
00:23:33,867 --> 00:23:39,787
Weirdly, it is, it is, you
know, putting pressure, downward
pressure on, on developer jobs.

275
00:23:39,797 --> 00:23:42,207
It's good at coding because it's
good at pattern matching, right?

276
00:23:42,677 --> 00:23:48,406
But when it comes to, you know,
content, speaking broadly, it's,
it's it's good at summarization.

277
00:23:49,196 --> 00:23:55,086
Like, I mean, I, after, after we do
this podcast, I'll download a transcript
and I'll ask it to summarize it.

278
00:23:55,126 --> 00:23:56,456
It will not write it for me.

279
00:23:56,456 --> 00:24:05,656
I think a lot of times people get to
the output, but like, honestly, like
my own summarization skills as I get
older and I do more things like are
not as good as, as they used to be.

280
00:24:06,096 --> 00:24:10,706
And so it will catch things that I would
probably not remember or have to go back.

281
00:24:10,706 --> 00:24:15,776
And so that saves me, I don't
know, like, you know, it's
like 15%, 20 percent of time.

282
00:24:16,306 --> 00:24:17,176
and a lot of.

283
00:24:17,416 --> 00:24:27,882
A lot of content that is created is
some form of summarization at the end
of the day, or the existing product,
it, it, you know, the, the atomic unit

284
00:24:27,912 --> 00:24:34,382
of news, which is still like the sort
of article for, for many publishers,
you know, can easily be summarized.

285
00:24:34,392 --> 00:24:39,342
That's why, that's what I see a lot
of publishers, they're pointing at
it to, to do the three bullet points.

286
00:24:39,699 --> 00:24:41,169
Which isn't exactly inspiring.

287
00:24:42,264 --> 00:24:51,364
Pete: yeah, it's like everyone's
using it to kind of do axios almost
right where whereas axios kind of
like almost reduced the atomic unit to
something arguably even smaller than an

288
00:24:51,469 --> 00:24:52,329
Brian: yeah, yeah.

289
00:24:52,329 --> 00:24:55,089
400 words, like, and bullet
points in the, in the bold.

290
00:24:55,574 --> 00:24:58,644
Pete: yeah, but I would I think
notebook LM suggests to me.

291
00:24:58,919 --> 00:25:05,339
Is that the, the, the innovation of doing
it with the data and then doing this,
whether it's a podcast or, you know,

292
00:25:05,339 --> 00:25:12,089
whatever your preferred way to consume
that data or get an overview of that data
is, the data can be whatever you want.

293
00:25:12,189 --> 00:25:12,529
You know?

294
00:25:12,529 --> 00:25:20,119
So what if it's like all the things
that you actually do care about,
like basically bridging the idea of
like, Oh, all the things you follow.

295
00:25:20,527 --> 00:25:25,375
Somehow putting that in a sort of
notebook LM idea and then just getting it.

296
00:25:25,769 --> 00:25:26,549
Summarize for you.

297
00:25:26,549 --> 00:25:32,699
And that's, that's the agentic future
where you have these sort of news agents
on the consumer side of this going out,

298
00:25:32,699 --> 00:25:40,189
like, give me all the stuff that I care
about, summarize it all together and give
me that, that daily brief, however it is.

299
00:25:40,189 --> 00:25:41,259
And we're kind of there, right?

300
00:25:41,269 --> 00:25:43,879
Like, I mean, open AI just
came out with operator.

301
00:25:44,329 --> 00:25:46,489
which is their computer use thing.

302
00:25:46,489 --> 00:25:55,059
So it's essentially, it can take over
your computer or, or work, even work in
the background and just get stuff or you,
whether it's, you know, ordering the pizza

303
00:25:55,059 --> 00:26:03,839
or the flight or hey, summarize all these
stories on, newspapers or whatever and
the times and, and the journal and you

304
00:26:03,849 --> 00:26:04,919
Brian: Well, you can, like, set it.

305
00:26:04,919 --> 00:26:15,724
You can say, hey, every morning, I
want to have like a summary of, you
know, this topic, this topic, and this
topic use these, these sources, et
cetera, or, or just leave it up to them.

306
00:26:15,784 --> 00:26:26,094
And I can see that being a very
compelling use case for most, for not
most, for a large chunk of consumers.

307
00:26:26,104 --> 00:26:31,134
I think sometimes those in the news
business that are particularly those
that are extremely online, And are

308
00:26:31,144 --> 00:26:37,614
constantly consuming news, don't
recognize that it is simply not a
major part of most people's lives.

309
00:26:37,804 --> 00:26:42,644
you know, they would, we would call
these people like low information
voters, whatever, guess what?

310
00:26:42,644 --> 00:26:49,354
They're the majority, you know, and
in some ways it's kind of sensible,
you know, the news product itself is.

311
00:26:49,704 --> 00:26:51,944
To me, it's not in a great shape.

312
00:26:52,174 --> 00:26:56,094
You know, the user experience is, is,
is horrendous on a lot of these sites.

313
00:26:56,564 --> 00:27:02,664
a lot of, you know, these, these
stories are like too narrative
driven for, for many people.

314
00:27:02,804 --> 00:27:04,994
I don't think a lot of people
want your storytelling.

315
00:27:05,384 --> 00:27:07,424
lot of people just want to know
what's going on in the world.

316
00:27:07,924 --> 00:27:20,204
and real or perceived, there's,
there's, there has been a lack of,
of trust in some of the bias that,
many people believe has, has cropped
into the, the news product itself.

317
00:27:20,624 --> 00:27:26,744
so I wonder how AI can be used and, and
specific examples that you see to improve.

318
00:27:27,124 --> 00:27:29,274
The, the, the product, right?

319
00:27:29,294 --> 00:27:34,154
Like, so I talk a lot about how
publishers are downstream of tech.

320
00:27:34,254 --> 00:27:40,664
And I guess what I see out there,
and maybe it's, maybe I'm not
seeing as much as I should.

321
00:27:41,164 --> 00:27:44,424
I don't see publishers sort
of looking to innovate.

322
00:27:44,889 --> 00:27:49,769
You know, putting a chat bot on your
site, it to me is like, okay, great.

323
00:27:49,809 --> 00:27:56,659
Like, I mean, honestly, like who's
using them when you start to ask
for usage, you know, then, then
the conversation gets changed.

324
00:27:57,251 --> 00:28:05,571
because I, to me, the obvious thing
is like, yes, it's very good at
summarization and piecing things together,
but that just points to multi source.

325
00:28:06,071 --> 00:28:14,191
Like, Outside of a few use cases,
particularly like around finance
publishers, you know, a Bloomberg or
something that has unique data sets.

326
00:28:14,691 --> 00:28:23,531
I honestly don't see why I would,
if I wanted to have a summary every
morning of Trump executive orders.

327
00:28:23,911 --> 00:28:26,651
I don't understand why I would want
it just from the Washington Post.

328
00:28:26,691 --> 00:28:30,271
Like, why wouldn't I want Politico and
all these other sources, Punchbowl, etc.

329
00:28:30,771 --> 00:28:31,091
Pete: Yeah.

330
00:28:31,091 --> 00:28:38,911
And that's kind of where I don't know
if that user experience innovation can
even be achieved at the publisher level.

331
00:28:38,911 --> 00:28:39,691
Like, yes, you're right.

332
00:28:39,691 --> 00:28:41,121
You're going to, you're
going to see these chatbots.

333
00:28:41,121 --> 00:28:42,691
Some are going to be better than others.

334
00:28:43,271 --> 00:28:45,371
Some summarization is going
to be better than others.

335
00:28:45,881 --> 00:28:57,406
but there's a couple of barriers, to, to
it being like this great experience, one
is just like you said, the availability
of content, you kind A more of a broader
view from, from multiple sources.

336
00:28:57,826 --> 00:29:02,876
Another is that publishers are
rightly very concerned about accuracy.

337
00:29:02,896 --> 00:29:09,556
And there is a Still like hallucinations
are just inherent to the technology
and yes, you can reduce them.

338
00:29:09,556 --> 00:29:15,556
And yes, there are, you know, safeguards
you can put in place, but generally
those safeguards and the publisher

339
00:29:15,556 --> 00:29:20,266
side anyway, have been so restrictive
that, you know, the Washington post
chat bot just won't say anything.

340
00:29:20,686 --> 00:29:30,616
If you go anywhere near, like outside
of the guardrails, it has, times AI,
which I actually like, I actually write
like their UX, and, and how they've.

341
00:29:31,006 --> 00:29:40,036
presented on person of the year But
the thing is those summaries that they
did of the person of the year you could
tell They weren't generative or they in

342
00:29:40,036 --> 00:29:47,386
other words, they were generated yes,
probably by ai but they were also vetted
by an editor because every time you
would go to the Smaller medium version.

343
00:29:47,386 --> 00:29:53,646
It was exactly the same word
for word, which says, Oh, it's
not actually doing this on the
fly, which means it can't scale.

344
00:29:54,016 --> 00:30:00,766
You know, you couldn't do it in the
way that we're talking about, where
it's totally tailored to whatever the
user is doing at any given moment.

345
00:30:01,236 --> 00:30:07,336
Now, tech companies are much, well,
shall we say less concerned with
accuracy, unless they're bullied into

346
00:30:07,486 --> 00:30:13,186
it, like, like Apple was, you
know, Apple was bullied into
dialing back their notifications

347
00:30:13,306 --> 00:30:15,816
Brian: yeah, but, like, also, like,
that's a great example, you know.

348
00:30:15,876 --> 00:30:20,146
Apple was using these AI summarizations
of news and it was screwing it up.

349
00:30:20,456 --> 00:30:22,646
And, you know, they just
like kind of rolled it back.

350
00:30:22,676 --> 00:30:26,096
They didn't get like a ton of
like blowback that I could see.

351
00:30:26,096 --> 00:30:28,336
Like, I mean, like things
just kept going on.

352
00:30:28,346 --> 00:30:36,796
Like if like the New York times
did that, there would be like a
million VCs with pitchforks that
were blaming DEI or something for it.

353
00:30:36,796 --> 00:30:39,826
So it's like, you can't win at
some point if you're a publisher.

354
00:30:40,326 --> 00:30:40,686
Pete: Yeah.

355
00:30:40,686 --> 00:30:53,736
So there's kind of a level of, and also
like the, the idea of hallucinations,
the fact that it's not a human doing
it, like it's similar to the self
driving car problem where even if.

356
00:30:54,236 --> 00:30:59,186
A self driving car is demonstrably
safer than humans, driving cars.

357
00:30:59,186 --> 00:31:01,726
And I don't know if we have the data on
that, but that's the supposition, right?

358
00:31:02,146 --> 00:31:13,636
the fact that a self driving car even
kills one human, you know, even if
the aggregate is lower because of
this sort of phase we're in and it's
new, it's, it's much more serious.

359
00:31:14,096 --> 00:31:18,826
Uber, Drop their, self
driving after that, accident.

360
00:31:18,826 --> 00:31:24,586
I forget where it was, but, so, so I
don't know if like hallucinate, like
it's an analogy, like you get, what I'm

361
00:31:24,596 --> 00:31:31,636
getting at is if a publisher screws up
with hallucinations, again, even if it,
the error rate might be lower than humans.

362
00:31:32,116 --> 00:31:35,926
The fact that it's this AI doing it
and there's sort of this mistrust.

363
00:31:36,416 --> 00:31:44,506
I don't, again, I don't know if
it's ever going to be able to cross
that threshold of being accurate
enough that they're going to apply
it at scale and be confident.

364
00:31:44,756 --> 00:31:54,436
You know, that's, that's kind of the
thing that, might hold back, like them
being able to sort of control their
own destiny, because like I said, the
platforms will apply this at scale.

365
00:31:54,626 --> 00:31:57,916
And, the next one that I think
will be really interesting.

366
00:31:58,416 --> 00:32:00,466
Is Amazon because

367
00:32:00,956 --> 00:32:07,166
the yeah, well, it's because Alexa is such
a bad product compared to chat GPT right

368
00:32:07,166 --> 00:32:07,476
now.

369
00:32:08,026 --> 00:32:13,086
And again, I'm not sure what
they're going to release.

370
00:32:13,126 --> 00:32:16,256
They're obviously going to release
an AI version of Alexa at some point.

371
00:32:16,266 --> 00:32:18,686
Maybe it's, it's going to
be like a big question.

372
00:32:18,696 --> 00:32:23,706
Is it going to be more like apple
where it's very underwhelming and
doesn't really do much, or are
they actually going to go for it?

373
00:32:23,746 --> 00:32:26,526
Are they going to go for like
a real chat GPT interface?

374
00:32:26,756 --> 00:32:28,276
And if they do do that.

375
00:32:28,776 --> 00:32:35,756
I could definitely see Generative
summaries of the stuff you care
about in a conversational way.

376
00:32:35,756 --> 00:32:39,396
Like, like that could suddenly
become a real thing people are doing.

377
00:32:39,396 --> 00:32:42,076
Cause a lot of us still have these
microphones all over our house.

378
00:32:42,076 --> 00:32:42,636
Like I do.

379
00:32:43,026 --> 00:32:50,956
Brian: I I've never it's funny because
like I remember When they came out with
that we were doing like flash briefings at
the time for and the idea was like people

380
00:32:50,956 --> 00:32:55,656
are going to Be ordering their groceries
off these things and like I think they're
just sitting there and are just like

381
00:32:55,996 --> 00:32:56,856
Pete: Giving us the weather.

382
00:32:57,011 --> 00:32:58,981
Brian: At this, yeah,
it's like parlor trick.

383
00:32:58,991 --> 00:33:04,311
You know, my parents are always like,
you know, asking like Google to tell
them what the weather's going to be.

384
00:33:04,311 --> 00:33:08,781
But like beyond that, it just seems
like kind of an underwhelming product.

385
00:33:08,841 --> 00:33:13,831
And then we just sort of move on
and forget about all the hype that
it was going to change everything.

386
00:33:13,914 --> 00:33:15,564
but with, with which.

387
00:33:15,854 --> 00:33:26,064
Which publishers do you think, like,
give me examples of publishers that are
actually using AI to create compelling,
products at the end of the day.

388
00:33:26,564 --> 00:33:28,294
Pete: Well, products.

389
00:33:28,314 --> 00:33:29,644
Well, the post and the times are both

390
00:33:29,919 --> 00:33:30,849
Brian: It can be features.

391
00:33:30,859 --> 00:33:32,049
I'll even take a feature

392
00:33:33,716 --> 00:33:36,556
Pete: I think the post and the times are
doing some good stuff with investigative.

393
00:33:36,566 --> 00:33:40,926
That's not quite a product, but
being able to process a lot of
data and do a better investigation.

394
00:33:40,926 --> 00:33:41,036
Yeah.

395
00:33:41,301 --> 00:33:41,851
That's good.

396
00:33:41,891 --> 00:33:51,991
again, I said, like I mentioned, time AI,
I think is, is a good experience, because
it makes it a nice sort of Chipotle
style menu of what you're getting, as

397
00:33:51,991 --> 00:33:56,971
opposed to this chat bot, I don't think
chat, even as much as I'm a pro chat
person, I don't think it is the thing.

398
00:33:58,051 --> 00:34:03,291
That's going to turn, you know, publisher,
experiences into something amazing.

399
00:34:03,661 --> 00:34:12,011
I do think it is helpful to have chat
so you know how to play in AI, and get
some good user data on sort of what
people who are actually coming to your

400
00:34:12,011 --> 00:34:18,231
website care about, and, and that'll
help you sort of play in these bigger
summarization engines, but I, I agree.

401
00:34:18,231 --> 00:34:18,711
It's not.

402
00:34:18,936 --> 00:34:20,376
It's not the best product for everything.

403
00:34:20,846 --> 00:34:35,028
but that said, I do think a certain amount
of, I don't know if anyone's really doing
this, but it's like chat, on the article
level, you know, where it's like, Oh,
like, what are the places I can go deeper?

404
00:34:35,523 --> 00:34:42,813
And get sort of follow up questions
about, you know, like, like that's,
that seems like the place for chat for

405
00:34:42,813 --> 00:34:47,203
me, you know, it's like, okay, I've,
I've read this article and I actually
want to go a little deeper on it.

406
00:34:47,203 --> 00:34:50,923
And rather than going back to
Google, maybe there's something
here that points me somewhere.

407
00:34:51,423 --> 00:34:54,823
I'll, I'll give sort of a, can
I give an anti good products?

408
00:34:54,963 --> 00:34:58,673
I guess, which would be, so there
was a lot of hype around particle,

409
00:34:59,173 --> 00:35:00,703
Brian: Oh, yeah, I was
just looking at that.

410
00:35:00,813 --> 00:35:01,203
Yeah, yeah.

411
00:35:01,343 --> 00:35:08,633
I mean, that's like a former Twitter,
executive started this sort of AI
news, summer summarization, app.

412
00:35:08,653 --> 00:35:10,783
I mean, news aggregation apps.

413
00:35:10,893 --> 00:35:11,233
I don't know.

414
00:35:11,243 --> 00:35:14,243
There's, there's a graveyard somewhere
that's like filled with like.

415
00:35:14,663 --> 00:35:18,913
Circa and all of them, they, for whatever
reason, they, they make a ton of sense.

416
00:35:19,333 --> 00:35:25,533
but they've never really taken off
outside of existing aggregators.

417
00:35:25,753 --> 00:35:30,753
Just putting one, like if you have, if
you're already aggregating, if you're
one of the choke points of the internet,

418
00:35:30,753 --> 00:35:37,893
if you're, you know, Google with Google
news, okay, you know, Apple, Apple news
is like, has emerged as just a massive.

419
00:35:38,213 --> 00:35:43,653
force, but that's, You know,
that's just the second order
impact of controlling distribution.

420
00:35:44,153 --> 00:35:45,433
That particle doesn't have that.

421
00:35:45,933 --> 00:35:46,313
Pete: right.

422
00:35:46,313 --> 00:35:46,743
Yeah.

423
00:35:46,743 --> 00:35:53,003
And I find, I just find as a design,
like it's, it's really busy, you know?

424
00:35:53,003 --> 00:35:55,893
Like it's almost like the design is anti.

425
00:35:56,233 --> 00:36:00,433
AI in the sense that AI was supposed to
simplify things and summarize things.

426
00:36:00,853 --> 00:36:05,603
And I tap into a particle summary
and it just has like a bunch
of different fonts and colors.

427
00:36:05,603 --> 00:36:10,393
And I'm, I'm like, well, what's the
path to the story that I actually
want to read about in this summary?

428
00:36:10,393 --> 00:36:11,333
And I can't find it.

429
00:36:11,333 --> 00:36:12,093
Like every time I look,

430
00:36:12,123 --> 00:36:12,533
Brian: It's very

431
00:36:12,953 --> 00:36:13,963
Pete: just go back to Google.

432
00:36:13,983 --> 00:36:14,223
Yeah.

433
00:36:14,273 --> 00:36:16,793
Brian: would say it's very branded
rather than just like bare bones.

434
00:36:16,793 --> 00:36:26,123
And you look at like, what is, I
mean, even the sort of positioning
of a lot of these, AI, products
is, is very, I mean, it's very.

435
00:36:26,623 --> 00:36:30,083
Like ChachiBT is like
incredibly bare bones.

436
00:36:30,083 --> 00:36:34,823
And like, it's also, you know, I don't
know if this is intentional or not.

437
00:36:34,833 --> 00:36:39,333
Like they don't abstract the, like
the, the speeds and feeds part.

438
00:36:39,333 --> 00:36:46,063
That's what we used to like, call it
like intact coverage, but like, you know,
it's like, I got to choose which model

439
00:36:46,063 --> 00:36:52,793
and like, you know, you go and you're
just like, I can understand like, to
me, It's going to hold back adoption.

440
00:36:52,803 --> 00:36:59,753
Like, I mean, convenient, always
wins and, and simple always wins.

441
00:36:59,813 --> 00:37:04,803
And the idea that I need to choose
which, which model I'm going to use.

442
00:37:05,303 --> 00:37:07,993
I mean, that's like CB radio land.

443
00:37:08,053 --> 00:37:10,803
Like that's not like, you know,
people don't want to do that.

444
00:37:10,803 --> 00:37:13,953
I think I feel fairly confident
in speaking for humanity.

445
00:37:14,358 --> 00:37:15,168
Pete: Oh, totally.

446
00:37:16,018 --> 00:37:16,098
Yeah.

447
00:37:16,098 --> 00:37:16,718
Yeah.

448
00:37:16,718 --> 00:37:17,098
Yeah.

449
00:37:17,278 --> 00:37:23,848
It's not going to work, but yeah, like
features, you know, I mean, the spoken
articles thing was kind of a, a level one.

450
00:37:24,303 --> 00:37:25,743
Of what's happening.

451
00:37:25,753 --> 00:37:35,543
And again, I'm not sure how comfortable
publishers will be doing this, but
like in applying like a notebook LM
style to a batch of coverage, I think

452
00:37:35,543 --> 00:37:40,663
that's sort of the logical next step
of like, I don't want to hear just like
someone speaking this article to me.

453
00:37:40,663 --> 00:37:43,893
I want like a conversation and sort
of a summary and overview of that.

454
00:37:44,393 --> 00:37:46,413
I think that's kind of
inevitably going to be coming.

455
00:37:46,848 --> 00:37:49,428
it's just a matter of
like, can they scale it?

456
00:37:49,518 --> 00:37:55,378
Will it, will it, will it be
something that, that, just becomes a
standard way of we consume something,

457
00:37:55,718 --> 00:37:55,928
Brian: Yeah.

458
00:37:55,928 --> 00:37:59,898
And I think one of the, the, the big, one
of the big outstanding questions, right?

459
00:37:59,928 --> 00:38:03,438
Is how AI is going to
drive productivity, right?

460
00:38:03,478 --> 00:38:06,388
And you can look at
productivity two ways, right?

461
00:38:06,438 --> 00:38:09,358
One, you need productivity
to drive economic growth.

462
00:38:09,448 --> 00:38:13,228
It is just how labor productivity
is how you drive economic growth.

463
00:38:13,228 --> 00:38:18,758
So if you're not against, if
you're a de growther, you can be
anti productivity, but I think
most people are not de growthers.

464
00:38:19,258 --> 00:38:20,988
and on the other hand.

465
00:38:21,253 --> 00:38:27,483
There's a lot of unease because,
you know, driving labor productivity
means, you know, fewer overall jobs.

466
00:38:27,483 --> 00:38:34,593
That's generally why, you know,
historically, you know, for instance, like
unions have tried to hold back automation

467
00:38:34,593 --> 00:38:40,903
and all kinds of different technological
advances that, you know, from on an
economic level, drive, drive productivity.

468
00:38:40,903 --> 00:38:42,723
So that's a good net net good.

469
00:38:43,178 --> 00:38:49,358
Because it means that you can deploy those
labor inputs to more productive endeavors,
at least that's the theory, right?

470
00:38:49,358 --> 00:38:54,068
But generally, you know, people do not
like change when it is visited upon them.

471
00:38:54,088 --> 00:38:56,628
They usually like change when
it, when it affects other people.

472
00:38:56,628 --> 00:38:58,988
I've noticed it's kind
of like accountability.

473
00:38:58,988 --> 00:39:00,968
It's much better for other
people than ourselves.

474
00:39:01,548 --> 00:39:08,568
Who is like, so how did, like,
obviously news organizations in
particular, but publishers overall are
under pressure to do more with less.

475
00:39:08,578 --> 00:39:10,978
I mean, we've seen like
cuts like nonstop, right.

476
00:39:11,008 --> 00:39:17,898
And you know, AI has, you know,
at least the theoretical, you
know, benefit of doing just that.

477
00:39:17,938 --> 00:39:18,488
I mean, I think.

478
00:39:18,988 --> 00:39:28,708
I would say for me, it is like a 10 to
15 percent these various tools added up
together increase in, in productivity.

479
00:39:28,798 --> 00:39:35,778
It's not like, I think a lot of times,
again, like I say, people go to like
the writing of the stories, they want to
just hit a button and things like happen.

480
00:39:35,778 --> 00:39:42,148
And like, I don't, maybe I just
haven't found that button or
just been able to hook things
together, but that ain't the case.

481
00:39:42,158 --> 00:39:45,528
Like it will, it, it is, it speeds the.

482
00:39:46,023 --> 00:39:48,743
You know, the, like the
production of this podcast, right?

483
00:39:48,783 --> 00:39:51,793
Like that, it absolutely,
you know, helps do that.

484
00:39:51,923 --> 00:39:56,013
it helps with, you know, if you use
Grammarly for like copy editing.

485
00:39:56,033 --> 00:39:56,493
So

486
00:39:56,993 --> 00:39:57,413
Pete: Yeah.

487
00:39:57,553 --> 00:40:00,403
Brian: who ends up, I mean,
you've written about this before.

488
00:40:00,613 --> 00:40:04,333
it would seem like all of these things,
cause we're seeing already, like.

489
00:40:04,833 --> 00:40:13,693
This exists within the context of,
you know, organizations are, are
slimming down, particularly in the,
in the middle management layers.

490
00:40:13,723 --> 00:40:14,143
Right.

491
00:40:14,643 --> 00:40:25,713
And you don't have as much of,
of that, in, in most publishing
organizations, just because, you know,
the business is, is not such that
you can run it like Google for sure.

492
00:40:25,848 --> 00:40:26,208
Pete: Right.

493
00:40:26,708 --> 00:40:27,528
Brian: but like.

494
00:40:27,908 --> 00:40:33,448
It would seem like this would
empower the people who are making
the content to do more, right?

495
00:40:33,448 --> 00:40:36,768
To be able to to to be
more productive, right?

496
00:40:37,168 --> 00:40:51,288
but that some of those Coordination
I saw like described as glue jobs
Will end up Being affected quite a
bit by, by AI tools and technology.

497
00:40:51,708 --> 00:40:55,278
Pete: Glue jobs, like
assistant editors, copy

498
00:40:55,378 --> 00:40:56,138
Brian: Yeah.

499
00:40:56,138 --> 00:41:04,498
So like, you know, you're moving data
around, you're doing some kind of
coordination role, obviously anyone
who's been inside, a news organization

500
00:41:04,498 --> 00:41:09,588
can tell, like, you know, the people
actually making the content are a
minority in, in these companies, right.

501
00:41:09,798 --> 00:41:11,778
And that's because there are a ton.

502
00:41:12,278 --> 00:41:17,338
Of coordination roles, if you're
going to take subscriptions,
you're going to have a saves team.

503
00:41:17,588 --> 00:41:19,228
You're going to have a growth team.

504
00:41:19,238 --> 00:41:20,638
You're going to have all of these.

505
00:41:20,668 --> 00:41:22,058
And it adds up.

506
00:41:22,268 --> 00:41:25,558
It's a lot of people to
run a publishing business.

507
00:41:25,558 --> 00:41:29,248
I think that's what this
sort of reaction to that is.

508
00:41:29,708 --> 00:41:32,238
You know, in the area we're
both broadly in, right?

509
00:41:32,238 --> 00:41:40,488
Like, it's like you, you slim it down
to like the bare essence and, you know,
you can, you can, you might not be

510
00:41:40,488 --> 00:41:46,748
able to do as much as, you know, people
with 75, 100, 200 people, but like you
can cover a lot of ground these days.

511
00:41:47,793 --> 00:41:57,363
Pete: Well, I also think in, in a lot of
places, there's going to be a sort of,
a small, not a select few, but people in
the newsroom who are really embracing.

512
00:41:57,803 --> 00:42:00,233
AI tools are kind of going
to build their own glue.

513
00:42:00,633 --> 00:42:08,163
So I know a bunch of people who
are in newsrooms who are kind of,
they're not coders, but they're
maybe AI productivity enthusiasts.

514
00:42:08,163 --> 00:42:08,393
Right.

515
00:42:08,393 --> 00:42:16,603
And they, what, what I find
interesting is that it's almost this
merger of editorial and product now.

516
00:42:16,663 --> 00:42:25,272
And I think that's kind of a reality
going forward, that those two things
are going to be much closer and in
some roles, you Almost a dual role.

517
00:42:25,282 --> 00:42:32,202
Like I know a bunch of editors now
that spend a lot of their time building
tools and maintaining those tools.

518
00:42:32,212 --> 00:42:43,312
So, yeah, this one publication that it's
more regional and a lot of the reporters,
who are covering various things like the
courts and, and recalls and whatever are.

519
00:42:43,657 --> 00:42:51,697
used to spend a good chunk of their
time in the morning, like, you know,
figuring out what's going on in all
these things and, you know, go to the
right government website, et cetera.

520
00:42:51,697 --> 00:42:53,757
And like, what's changed, what's
the case has been updated.

521
00:42:54,227 --> 00:42:59,157
And now they, alongside, some product
stuff have, have built these tools.

522
00:42:59,167 --> 00:43:00,417
Again, like no code tools.

523
00:43:00,417 --> 00:43:01,097
They don't know code.

524
00:43:01,127 --> 00:43:11,417
They're just taking some off the
shelf platform, plugging in some
generative stuff and doing it in sort
of a left brain kind of way, which
And now that's, that's done for them.

525
00:43:11,427 --> 00:43:11,807
Right.

526
00:43:11,837 --> 00:43:17,867
And, and, again, like not, not normally,
like, you know, a few years ago,
you'd get your product team to do it.

527
00:43:17,877 --> 00:43:21,777
If it even fits on their roadmap, you're
like, yeah, we'll do that someday.

528
00:43:21,807 --> 00:43:24,327
Maybe it's six months, a year
down the road, et cetera.

529
00:43:24,727 --> 00:43:29,267
and now there's a lot more
sort of empowerment of, of just
sort of doing that yourself.

530
00:43:29,747 --> 00:43:32,287
So, so that's kind of a neat development.

531
00:43:32,297 --> 00:43:34,677
It does mean for certain roles.

532
00:43:35,177 --> 00:43:42,927
You're going to be like that,
that 10 percent efficiency isn't
necessarily getting realized
in the, the story building.

533
00:43:42,927 --> 00:43:47,147
It's more in sort of the, the
infrastructure building of, of the place.

534
00:43:47,147 --> 00:43:50,193
But I think it sort of generally
levels up, Of everybody.

535
00:43:50,193 --> 00:43:53,122
But yeah, I think a big question
is like, what do you do with that?

536
00:43:53,122 --> 00:43:54,853
10 or 15%, right?

537
00:43:54,873 --> 00:44:01,253
Like, you know, in roles I've had,
you know, do, do you want the people
to go deeper on certain things?

538
00:44:01,253 --> 00:44:02,923
Do you want more stories?

539
00:44:03,293 --> 00:44:09,263
Like what's going to have the most
impact, to, to the publication and like,
and sort of in today's environment, I

540
00:44:09,283 --> 00:44:15,443
think it's like going deeper, you know,
like it is like, you don't want to
necessarily want more stuff out there.

541
00:44:15,904 --> 00:44:17,664
You want just better
stuff for your audience.

542
00:44:17,674 --> 00:44:19,284
So yeah, it kind of depends
on what you're doing, but

543
00:44:19,294 --> 00:44:28,324
Brian: You know, like I mean, for
instance, like, I'm doing an online forum
later this week, with, Newsweek and, you
know, they've used AI and other, you know,

544
00:44:28,324 --> 00:44:39,074
tools to double their output basically
with the same, With the same amount of
staff and, you know, that like, I think,
you know, I'm going to talk to, to them

545
00:44:39,074 --> 00:44:49,524
about it with the chief product officer,
but, you know, the reality for a lot
of these models is you need, they still
need to talk, they still need traffic.

546
00:44:50,079 --> 00:44:55,169
In their models, like, and it's
easy to say, Oh, it's about
death, not breath and stuff.

547
00:44:55,199 --> 00:45:05,859
But the reality is, like most businesses,
you're operating your existing business
and you're trying to build the next
business and you can't, you can't just
put the existing business on pause.

548
00:45:06,099 --> 00:45:16,999
you're going to have to, so I, I have
a lot of sympathy because there's a lot
of pressures in, in the marketplace,
obviously, and, you know, it depends on
your model, but a lot of models still need

549
00:45:17,469 --> 00:45:28,939
to efficient ways to, to create, To create
traffic so which which leads me to the
search thing and I want to just you know
Close with a little bit on that because
you know, obviously the big concern

550
00:45:28,979 --> 00:45:49,224
for for publishers is is the change to
search and You know, I think sometimes
it can be Both underplayed and overplayed
underplayed in that the search is so
central to Many if not most publishing

551
00:45:49,234 --> 00:46:01,270
models it has been The not just the
dominant but the most stable algorithmic
Distribution source for, for, for audience
and, oftentimes very high quality, right?

552
00:46:01,290 --> 00:46:05,750
Compared to a lot of the kind
of nonsense audience that
you, that publishers would get

553
00:46:06,010 --> 00:46:06,330
Pete: Yeah.

554
00:46:06,330 --> 00:46:08,180
And it's, it's death has been exaggerated.

555
00:46:08,430 --> 00:46:11,160
Brian: Right, because it is still Google.

556
00:46:11,220 --> 00:46:20,290
I saw one, you know, the stats are
kind of difficult to get in there, but
I saw one saying, Oh, Google finally
dipped below 80 percent share for
the first time in like nine years.

557
00:46:20,740 --> 00:46:22,570
You know, and it was like 79.

558
00:46:22,570 --> 00:46:23,450
9 versus, and it's like.

559
00:46:23,870 --> 00:46:25,350
Okay, that seems pretty dominant

560
00:46:25,370 --> 00:46:28,630
Pete: think it was actually 90,
you know, it was like, you know,
there's, it's, it's just so dominant.

561
00:46:28,640 --> 00:46:29,560
Like, it's like, okay,

562
00:46:30,015 --> 00:46:39,195
Brian: You know, so we're, I mean, like
we talk, we use like perplexity and
these kinds of things, but, you know,
regular people are not for the most part.

563
00:46:39,195 --> 00:46:47,455
I mean, they are yes, but like in the
broad sweep of how dominant search is,
this is, this is chipping away and,

564
00:46:47,565 --> 00:46:50,625
Pete: I also tried to switch to an
AI search as kind of my default.

565
00:46:50,645 --> 00:46:51,385
I couldn't do it.

566
00:46:51,635 --> 00:47:00,485
And I think it's because if you're
extremely online and you, you just develop
this rhythm with Google as like, you know,
firing up a new tab and getting links

567
00:47:00,760 --> 00:47:01,670
Brian: you know, it's everywhere.

568
00:47:01,670 --> 00:47:08,070
And so we'll see if the, we'll see how,
how the Trump administration deals with
some of the leftover antitrust cases.

569
00:47:08,400 --> 00:47:16,940
Pete: Oh, yeah, true, but it is kind
of a, just a thing in terms of what I
want when I search the internet or want
something from the internet, most of

570
00:47:16,940 --> 00:47:26,930
the time I'm that processing that AI
does, I can actually still do faster
with a search results page when just
my head, like, Oh, here's the link.

571
00:47:26,930 --> 00:47:33,300
And that I was actually looking for in the
first two, it kind of depends if you're
looking for information or something
very specific, like a destination.

572
00:47:33,800 --> 00:47:34,090
Yeah.

573
00:47:34,590 --> 00:47:48,150
Brian: But when, I mean, I think the
big sort of concern that I hear all the
time is, you know, search has gotten
less reliable, AI, the whole promise of
these chat bots is to return just what

574
00:47:48,150 --> 00:47:58,690
you're looking for and not like, The
publisher web page on a search, result
is often just an intermediary to what
people, you know, want like, you know, I,

575
00:47:59,204 --> 00:47:59,484
Pete: Yeah.

576
00:47:59,499 --> 00:48:03,009
Brian: the search yesterday about
which street streaming the Eagles game.

577
00:48:03,289 --> 00:48:05,529
I ended up on a USA Today.

578
00:48:05,779 --> 00:48:07,829
Yeah, horror show of a page.

579
00:48:07,869 --> 00:48:19,093
No offense to my friends at Gannett, but
like that is not a page I would want to
take to the sort of UX Hall of Fame That's
just filled with it was basically a Fubo
ad, but it was just filled with junk to

580
00:48:19,093 --> 00:48:29,501
try to get through to And I get it I get
everyone has to pay bills But nobody's
gonna nobody's gonna protest that that
stuff goes away Like I don't think so

581
00:48:30,001 --> 00:48:30,601
Pete: I mean, agreed.

582
00:48:30,601 --> 00:48:40,921
It's like, you know, it's an existential
thing for publishers that AI search,
you know, is going to essentially
substitute what they're doing.

583
00:48:40,921 --> 00:48:45,661
And I, but I think once you get
sort of to more granular level of
what they're actually serving up.

584
00:48:46,161 --> 00:48:53,661
You find that some, some types
of content are more at risk for
being substituted than others.

585
00:48:53,661 --> 00:48:54,001
Right.

586
00:48:54,021 --> 00:48:59,751
And you know, like, that's kind of
what I was getting at with sort of
the sameness of things earlier in, in

587
00:48:59,791 --> 00:49:04,781
terms of these AI summarization engines
sort of, and that's partly due to the
licensing deal, but also partly like what.

588
00:49:05,411 --> 00:49:12,421
AI is good for like, we're always
going to want to on Saturdays go, you
know, settle in with a podcast or a

589
00:49:12,421 --> 00:49:18,361
feature article or our favorite writer,
our favorite sub stacker, and just,
you know, kind of go deep on that.

590
00:49:18,861 --> 00:49:21,611
and AI's might be good at
sort of pointing you to that.

591
00:49:21,621 --> 00:49:23,011
And to some extent.

592
00:49:23,511 --> 00:49:29,471
Even, some discovery within a summary,
if you find something interesting
that you want to go deeper on, but

593
00:49:29,471 --> 00:49:34,361
for, for the most part, if you're
just kind of like, okay, I'm busy,
but I need to know what's going on.

594
00:49:34,361 --> 00:49:43,981
You know, AI, AI is an amazing, tool and,
you know, like to your point of like the
publisher experiences and a lot of these
websites, I mean, it's, it's kind of a

595
00:49:43,981 --> 00:49:50,651
better experience, if you, if you don't
need to do anything deeper than, than
what, just a summary of what's going on.

596
00:49:50,961 --> 00:49:54,951
So I think it's absolutely going to
become a greater and greater part.

597
00:49:55,451 --> 00:49:58,621
Of just how people get information.

598
00:49:59,121 --> 00:50:08,661
And it, the reversioning of it, I
find interesting because if you think
about how attention is now so spread
out over various apps and putting

599
00:50:08,661 --> 00:50:15,971
aside, like the Tik TOK ban sort of not
withstanding, you know, some people just
like want to scroll all day on Tik TOK
and kind of get their noobs that way.

600
00:50:15,971 --> 00:50:24,171
And, you know, whether that's YouTube
shorts, whether that's, you know,
podcasts, And, and then this is
something that I've written about too,
and I don't have the answer to it.

601
00:50:24,171 --> 00:50:28,401
Like how much does human authenticity
even mean in that experience?

602
00:50:28,401 --> 00:50:32,141
Do you care that that avatar
you just saw on TikTok?

603
00:50:32,536 --> 00:50:41,286
Was actually AI, if it's, you know,
something relatable and, and gives
you the information and experience
you're looking for, maybe, I mean, it,

604
00:50:41,356 --> 00:50:48,586
you know, again, it, it, it, I think
in terms of the way people consume
things and what I was just talking
about, I think it matters a lot less.

605
00:50:49,086 --> 00:50:52,096
For your day to day stuff and matters more
for that stuff you're doing on Saturday.

606
00:50:52,596 --> 00:50:53,006
Brian: Yeah.

607
00:50:53,036 --> 00:51:01,126
I mean, already, Pew found that,
39%, of people 18 to 29 get their
news regularly from, from TikTok.

608
00:51:01,276 --> 00:51:09,446
So I mean, the patterns are changing
already and AI to me is just a giant
accelerant to a, to a lot of those.

609
00:51:09,916 --> 00:51:11,446
what about the lawsuits, right?

610
00:51:11,476 --> 00:51:14,616
I mean, it's 2025, the year when.

611
00:51:15,116 --> 00:51:19,906
Not just the lawsuits, but
like coming to some kind of
economic understanding, right?

612
00:51:19,906 --> 00:51:35,588
Like I mean there we've seen the
smattering of Ai deals like these are just
with you know a few Obviously publishers
not the mass number of publishers out
there I regularly ask publishers and, and

613
00:51:36,088 --> 00:51:45,938
about using the, the, the, the different
like toll bid or pro rata, like in
order to basically position themselves
to, to get some kind of economic value.

614
00:51:45,938 --> 00:51:54,428
Most of them are just like deploying
the tool because it's free the
tools because they're free and like
sort of monitoring, which means
they're not doing anything about it.

615
00:51:54,468 --> 00:52:00,728
And, you know, there is,
again, we are always left in
a could might in the future.

616
00:52:01,058 --> 00:52:12,897
You know, we're going to be able to,
you know, bring some kind of economic
value out of these, These AI answer
engines, because breaking in some

617
00:52:12,897 --> 00:52:19,587
ways, the bargain that underpinned
Google search, which was, we're going
to let you crawl our webpage webpages.

618
00:52:19,927 --> 00:52:21,777
We're going to let you scrape them.

619
00:52:22,277 --> 00:52:28,707
even though if Google won't claim it's
scraping, but they're scraping data
from those, those webpages and they're.

620
00:52:28,777 --> 00:52:31,157
Putting it on their website
and a search results.

621
00:52:31,157 --> 00:52:35,137
And the bargain is you're going to
get traffic that you can monetize.

622
00:52:35,137 --> 00:52:37,487
And by the way, you're probably going
to use our tools to monetize it.

623
00:52:37,487 --> 00:52:38,357
So we're going to get a taste.

624
00:52:38,857 --> 00:52:39,747
that's off.

625
00:52:39,807 --> 00:52:45,057
I mean, the, you know, perplexity for
instance, said, Hey, robots, robots.

626
00:52:45,067 --> 00:52:53,827
txt, which is, you know, the, The little
code that basically told Google and other
crawlers, stay away from these pages.

627
00:52:53,857 --> 00:52:57,427
you do it like for transaction pages
or pages of personal data, et cetera.

628
00:52:57,427 --> 00:52:58,747
But you know, you always had that option.

629
00:52:58,747 --> 00:53:10,177
You could opt out of the system and you
know, perplexity, which interestingly,
their CEO has kind of turned into a little
bit of a heel in this drama, which I
think is just where Silicon Valley is now.

630
00:53:10,247 --> 00:53:11,607
I know something about that guy's off.

631
00:53:12,107 --> 00:53:15,247
So, you know, they're just saying, Hey,
we're not going to pay attention to this.

632
00:53:15,257 --> 00:53:24,157
So are we going to see any sort of
understanding at all this year of
how the economics to all this work?

633
00:53:24,157 --> 00:53:32,627
Because a lot of these AI, you
know, the vision for these things
break the, the economic model.

634
00:53:32,847 --> 00:53:33,157
Right?

635
00:53:33,157 --> 00:53:37,877
And, and look, Silicon Valley,
that's their thing, you know, move
fast, break things, et cetera.

636
00:53:37,897 --> 00:53:40,687
They love breaking other people's
economic models, not their own.

637
00:53:41,187 --> 00:53:47,147
so are we going to start to see
some kind of understanding about
how, how the economics work out?

638
00:53:47,147 --> 00:53:52,497
Because, you know, you can get into
the doom loop of like, well, there's no
economic incentive to create content.

639
00:53:52,537 --> 00:53:53,837
Like it will not be created.

640
00:53:53,857 --> 00:53:56,557
And then it's going to be
filled with junk, et cetera.

641
00:53:57,062 --> 00:54:07,192
Pete: So one thing, so I'm not, obviously
I'm not a hundred percent sure how this
is all going to work out, but one thing
I am sure of is that there is a consensus

642
00:54:07,202 --> 00:54:20,332
building, that might actually already
be taken over, which is that if an AI
engine is going to ingest a publisher
content and then summarize it, that.

643
00:54:21,157 --> 00:54:25,577
Is the industry is deciding that's
more like syndication than search.

644
00:54:26,077 --> 00:54:29,007
And this is, I'm talking mostly
about the real time stuff, right?

645
00:54:29,007 --> 00:54:30,677
Not putting training data aside.

646
00:54:30,687 --> 00:54:33,837
Like if you're summarizing news, if
you're summarizing stuff, that's out

647
00:54:34,112 --> 00:54:35,872
Brian: yeah, they need direct payments.

648
00:54:35,942 --> 00:54:37,862
The indirect stuff does
not gonna work here.

649
00:54:37,912 --> 00:54:41,032
Because it's just, it's like,
I don't know, little citations.

650
00:54:41,072 --> 00:54:43,912
I don't, it's not gonna work the
same as search, I don't think.

651
00:54:43,922 --> 00:54:47,692
I know Google says that people, you
know, they're driving as much traffic.

652
00:54:47,692 --> 00:54:52,002
I just, it, it's just hard for me,
cons, using these, these services.

653
00:54:52,002 --> 00:54:55,992
The entire point of them is to not
have to go to all these websites.

654
00:54:56,207 --> 00:54:59,507
I mean, like, come on, that's
the, like, that's the USP.

655
00:55:00,397 --> 00:55:04,662
Pete: And we're starting to
say like Google's kind of
stayed away from news so far.

656
00:55:05,008 --> 00:55:11,088
left it to chat GPT and perplexity,
but we've saw the first hints of them,
they are moving in that direction.

657
00:55:11,108 --> 00:55:18,078
And, and obviously that's gotta be
on the roadmap that this, they're
going to apply generative summaries
to news and Google news at some point.

658
00:55:18,458 --> 00:55:27,198
and you know, that's obviously
going to be a big Bomb that lands
in the industry when, when that
happens, but they'd started at CES,

659
00:55:27,208 --> 00:55:32,778
they announced for their Google TV
platform, they would start summarizing,
giving sort of some basic summaries.

660
00:55:32,948 --> 00:55:36,868
I don't think that product's
out yet, but that, that was one
of their, big announcements.

661
00:55:37,206 --> 00:55:37,446
Brian: Yeah.

662
00:55:37,446 --> 00:55:47,996
Wait till they start to stitch together,
like, you know, video of, you know,
different newscasts and, and YouTube
and like, Oh, that sounds like a pretty
good, and sounds like a doable product.

663
00:55:48,056 --> 00:55:51,236
I mean, I'm not an engineer, but it
seems, it doesn't seem so farfetched.

664
00:55:51,416 --> 00:55:54,536
Pete: you imagine those Notebook
LM people as, as avatars, right?

665
00:55:54,556 --> 00:56:00,136
They're just going to be people that
are just chatting at a, at a news desk
or, you know, in the field somewhere.

666
00:56:00,566 --> 00:56:11,126
but so, so the thing is with these
deals and the lawsuits, so far it's
only the big players, if you're a small
player, yes, you can go to a toll bit
or a Dapier or any of these other sort

667
00:56:11,126 --> 00:56:19,921
of startups that are trying to create
this, you know, the problem is that
they haven't really gotten the attention
of the big AI guys yet in a major way.

668
00:56:20,311 --> 00:56:28,891
And my sense is they won't until the
sort of people at large, demand it.

669
00:56:28,911 --> 00:56:40,341
In other words, like someone's got to
crack that user experience where, Oh yes,
I want, My news this way, and I expect
it from you chat GPT and everyone else.

670
00:56:40,341 --> 00:56:47,991
perplexity, again, perplexity is
sort of a different animal because
they don't do licensing, they do
rev share, obviously they've gotten
into legal hot water over that.

671
00:56:48,440 --> 00:56:52,850
With News Corp and, you know, we'll,
we'll see how that all plays out.

672
00:56:53,350 --> 00:56:59,140
I got to say, given my, what I sort of
just said about the consensus emerging,
I, I worry a little bit about perplexity.

673
00:56:59,470 --> 00:57:08,180
I do think kind of their, the sheen
of that service, which was kind of a
darling about a year ago and everyone was
admiring as kind of worn off and they're
kind of throwing spaghetti at the wall

674
00:57:08,200 --> 00:57:12,110
Brian: Well, this TikTok thing, that's,
that's, that's where I, I like, I, I,

675
00:57:12,350 --> 00:57:13,040
I'm like, I'm out.

676
00:57:13,130 --> 00:57:14,060
I'm out on this guy.

677
00:57:14,090 --> 00:57:15,290
I mean, there was two things.

678
00:57:15,290 --> 00:57:22,670
One was when the, the CEO during the
New York Times tech worker strike
said, oh, we'll take over your server.

679
00:57:22,670 --> 00:57:30,900
I'm like, eh, I, I, that just struck me as
just this Silicon Valley buffoonery from
all the money being thrown around there.

680
00:57:30,900 --> 00:57:32,610
I was like, this guy's
too big for his britches.

681
00:57:32,610 --> 00:57:34,050
He's not focused on the right things.

682
00:57:34,440 --> 00:57:36,510
And then there's this talk of.

683
00:57:36,950 --> 00:57:47,250
Of taking over Tik Tok and, and it
being half owned by the government,
because obviously perplexity does not
have a control distribution choke point.

684
00:57:47,250 --> 00:57:51,910
So it's just some random app that
people have to download and, and the.

685
00:57:52,270 --> 00:57:54,926
Just the deck is stacked against it, know,

686
00:57:55,056 --> 00:57:57,546
Pete: yeah, it's, it's a,
it was an odd thing to do.

687
00:57:57,566 --> 00:58:00,046
Obviously I think it was
more of a publicity stunt

688
00:58:00,156 --> 00:58:06,586
Brian: that's what I mean when
people are doing publicity stunts
It's just like I listen to a podcast
the the it was actually prescient.

689
00:58:06,896 --> 00:58:16,159
It was a more or less the podcast
and Sam Lesson was saying anytime
people are are Getting excited about
spending money versus making money.

690
00:58:16,159 --> 00:58:21,179
It's like be very be very
concerned So I think we're
seeing that in the stock market

691
00:58:21,179 --> 00:58:21,679
today, but

692
00:58:21,699 --> 00:58:23,069
Pete: the thing is I wrote about this too.

693
00:58:23,089 --> 00:58:28,192
I do see kind of a, I guess a synergy
there to pick a terrible word.

694
00:58:28,192 --> 00:58:39,669
It was kind of like, you know, if,
if TikTok has this AI connection,
which, you know, it doesn't really
have an AI strategy right now and
has this connection, to perplexity.

695
00:58:39,939 --> 00:58:43,489
That can, you know, it's users
actually kind of find useful.

696
00:58:43,489 --> 00:58:44,849
I'm not sure what that would be.

697
00:58:45,319 --> 00:58:48,389
I could see that, that sort of
working out for them in some way.

698
00:58:48,389 --> 00:58:56,769
Like I said, they sort of
instantly have kind of a, an
AI strategy that's specifically
about surfacing, good summaries
and pointing people to the right.

699
00:58:57,259 --> 00:59:04,119
information if they want to go deeper
on something and then obviously
TikTok, the benefits to TikTok are,
are huge because, you know, you're
just exposed to all these users.

700
00:59:04,539 --> 00:59:05,069
will it happen?

701
00:59:05,099 --> 00:59:08,989
Um, probably not, but it's,
you know, it's, it's an
interesting thing to think about.

702
00:59:08,989 --> 00:59:19,184
And even if it's not, even if it's not
perplexity, it's kind of like, well,
Is there, is there some AI solution
that takes TikTok to some other level?

703
00:59:19,184 --> 00:59:20,924
Anyway, TikTok's got a lot of problems.

704
00:59:21,214 --> 00:59:26,794
Doesn't, I don't think it has to
worry about its long term strategy,
until it's sort of figured out this,
this whole situation with the ban.

705
00:59:27,294 --> 00:59:27,804
Brian: Okay.

706
00:59:28,104 --> 00:59:29,794
All right, Pete, let's leave it there.

707
00:59:29,904 --> 00:59:31,314
this was, enjoyable as always.

708
00:59:31,314 --> 00:59:32,444
We covered a lot of grounds.

709
00:59:32,554 --> 00:59:33,214
really appreciate it.

710
00:59:33,469 --> 00:59:33,659
Pete: Yeah.

711
00:59:33,659 --> 00:59:34,409
My pleasure, Brian.

712
00:59:34,624 --> 00:59:35,324
Thanks for having me on.