In this Spotlight episode, Josh Brandau, CEO of The Rebooting partner Nota, discusses how AI can be a critical tool for newsrooms in a more-with-less era. osh is a publishing veteran having been CRO and CMO at the Los Angeles Times. That informed his decision to create Nota since like other publishers he saw legacy media struggling to adopt technologies that underpin sustainable businesses. 

We discuss the inefficiencies inherent in a lot of newsrooms that end up taking scarce resources away from the actual news reporting, and how tasks like versioning, content optimization, SEO and tagging can be sped along with an AI assist.

We also take a big picture view of where journalism goes in an AI world, licensing as a growing revenue source and how AI could create other new revenue streams as publishers inevitably move beyond efficiency and begin to create new products that improve the customer experience.

Welcome to The Rebooting Show.

I'm Brian Morrissey.

This week I have a spotlight episode with Josh Brandau, the CEO of The Rebooting's partner, Noda, which is a company that offers AI tools to improve workflow in newsrooms.

we're very excited to partner with Noda on an upcoming online forum where we'll discuss how AI can be a force for good in a, More with less era.

know, the other week I wrote a piece on the rebooting about the AI adoption curve.

I compared it to the Kubler Ross stages of grievance.

I love to do that.

there's of course the anger and, and bargaining phases, which, we're still in, with getting to the deals, that many publishers are striking.

And Josh and I discussed these, but I think a key phase, of this adoption curve is the efficiency phase.

I mean, companies of all kinds are adopting A.

I.

Initially in the hopes of becoming more efficient and therefore more productive.

this is critical, right?

So publishing is no different.

And if anything, it's under unique pressures.

And, you know, the rebooting is recent survey of news publishers found that efficiency was by far the most important.

The biggest opportunity that they saw in AI.

Josh is no stranger to this.

He's a publishing veteran.

He was the CRO and CMO at the Los Angeles Times.

that greatly informed his decision to start Noda.

since like other publishers, you know, he had saw at the times, you know, legacy media that, was struggling to adopt technologies that inevitably underpin sustainable businesses.

we discussed those inefficiencies that are inherent in a lot of newsrooms and end up taking away, scarce resources from the actual news reporting.

and also how tasks like versioning, content optimization, SEO and tagging, which can sound all boring, but that they can be sped along with, an assist from AI.

And this, people inevitably go to job loss, right?

And I think it's real.

But I also think that it's it's really inevitable that the future of these organizations are both slimmer and more productive, and the market just necessitates that Josh and I also take a big picture view of where journalism goes in this kind of AI world, assuming we're going into it, right?

And we both see that licensing is going to be a growing revenue source.

and there's a lot of, there's a lot of challenges to overcome to get there.

but we also talk about how AI could create, new revenue streams as publishers move beyond efficiency and begin to create new products that improve the customer experience.

Imagine that.

so I hope you enjoyed this episode.

Thanks again to Josh and Oda for their support.

Josh, thanks so much for joining me for the podcast.

Really appreciate you having me.

I've been a huge fan for quite some time.

So it's a little surreal to talk to you

All right.

Please, please.

This, this is going to get you nowhere, Josh.

but no, thank you.

I appreciate it.

so tell me about your experience, you know, at a quote unquote legacy publisher.

What, how did it inform, you know, going off to build, build Noda?

Yeah.

I joined the Los Angeles times as the CRO and the CMO at a really interesting time in its life cycle.

So Patrick Soon Shiong had bought it from Tribune.

He'd brought in a bunch of folks.

Who had not been from the industry.

I was a consultant.

I was in media advertising, media planning and buying my entire career before that, and was really helping the sales team understand how to sell to advertisers.

And that kind of evolved into, the position I took at the LA times.

It was exciting in the.

Reality that we were doing something that was super meaningful for the community and the nation at large.

and that really invigorated me and what I was doing, but it was also really telling to see the technologies that apply to, the legacy media space, as we call it, and how woefully weak they are, how there's not really an ecosystem of technology partners that supports it from a grander scale and have porting over a lot of the technologies from similarly, similarly related, media companies.

And other spaces doesn't really work very well for the use cases.

Of media.

So it was really instructive and a lot of capacities and it, it made me and the CTO at the time Ben Gerst, who's my co founder now at this company, Noda, think that, Hey, if there's ever a real, an opportunity, to support legacy media overall, we want to.

Give it a try.

Yeah.

But what specifically with, within, where, where does it break with the tech?

And I mean, a lot of it is, you know, particularly you got to break it, but it's like newspapers have a certain problem that they're operating a legacy publishing system that is complicated.

I mean, it's imagine, I mean, you're like, there's delivery trucks involved in one night, like and that's non trivial is printing plants.

And that's like a particular issue, but is there anything that you saw within there?

and within the ecosystem that sort of said, you know, there's, there's an opening here that, that we can, that we can help these publishers, they're obviously playing catch up, but to actually catch up.

That's a really good point from a CMS construction perspective.

there's a lot of use cases that are very, actually, I think at this point, pretty completely unique, to newspaper delivery on a daily basis.

outside of that, which does keep some of the legacy systems.

truly legacy because a lot of those companies aren't really innovating to the degree that you'd want.

What we saw were a few pain points that were, are very obvious.

So one would be also the nature of the industry breaking news.

So investigative reporting by definition, I had no idea that we were about to break something huge.

But once we do, it's, you know, my teams, the audience teams, consumer marketing teams, content teams, everybody's job to try to get as many eyes on that as possible.

And you're really playing catch up by that story is being told in the marketplace.

So how do we get to a, how do we get to a system that allows us to create assets a lot faster than the traditional models of studios?

So that was one area that we thought we could improve upon.

another area was newsletter creation.

So how do we take all of this gigantic amount of subsegmentation data across our customer base and really apply rigor to it so that we can provide you as a, you know, a customer, with newsletters that you really want to see, or why don't we provide you with newsletters full of.

engaging content that we know you didn't see on site, for example, when you're logged in, to show the value of what we're providing you.

And then, honestly, something as simple as, I had a channel in Slack called Headlines that was just a debate about what's a good headline for my story.

that really has you questioning, you know, how much time needs to be spent on something that isn't necessarily for you.

Your audience, but for, you know, a search functionality that's necessary for an editor or a journalist to think about today.

So there's just like 15 to 20 pretty massive pain points that we think could be solved with, or we thought could be solved with technology and have proven over the last few years that they can be.

Yeah.

So obviously when, when we're talking about solving pain points and problems with technology these days, we're talking about AI, right.

and explain the opportunity that you see, I want to get into the threat stuff, but we'll actually try to turn it around and talk a little bit about the opportunity because it's there.

And I think publishers I think are starting to see it.

If not, I've already seen it.

I mean, we did a survey, just recently, we just, came out with a report of news publishers and we asked, how do you expect AI to impact your business now, 44 percent took the safe neutral, but, 28 percent said very positively.

And 22 percent said, you know, absolutely negatively, you know, and then 6 percent said too soon to tell.

what, what are you seeing as far as the, and what did you see as the opportunities to use AI in order to make these businesses work better?

I think that that's a pretty representative understanding of where the AI technology can lead us.

Now that everyone's not operating from a, Oh, you know, a one or zero or everything or nothing kind of position where it's very clear.

I think if we start from a, the happiest version of what AI can bring to the industry, which is a little unlike this podcast, no effects, right.

We can just be a little, a little rosy about this.

That's why I started with the positive.

What do you want

yeah, I love that.

It's it's a.

It's not a novel technology for our industry.

That's really the best news possible.

So the application of a lot of this stuff really accelerates the ability for you to tell your story, to a larger audience a lot faster and through a lot of different mediums that you couldn't do that before, at least without spending an insane amount of time trying to make that happen.

It's not a novel promise of, you know, what, what AI could bring to this tomorrow.

It's a tool structure that exists.

that we've created and a whole bunch of other folks have created, that allows you to do that today.

So I think that's the really good news if we get back to The idea of what is the purpose of this entire industry?

It's, you know, expressing stories about the world around us to each other so that we can understand and, really appreciate the communities we live in and, and, and the broader spectrum of what's happening with humanity.

I think that this is amazing because it doesn't make you choose, you know, the one format that you're really good at telling that story and it allows you to, tell that story in any format you possibly can.

And I think that's.

I think that there's a lot to that.

but I think broadly.

It's the real promise of what this technology brings that this industry has been waiting for for 20 years as it's been trying to evolve past, you know, being very, very good at three things.

So structurally being very good at writing articles or structurally being very good at writing or creating a new segment, or, or a radio segment.

Those are really the three major areas of optimization for these gigantic, structures.

Now you can utilize that same ability to tell a story in a singular format.

And apply it to every other format, which serves not only the purposes of why you do anything, which is.

Telling that story, but also applies it to way more people in the way in which they want it and expect it from every other part of their lives at this point.

So I think that's awesome.

So bucket out the opportunities between, and these are crude, cause I'm sure that they'll, they'll bleed.

but like one is like efficiency, right?

I mean, I think we see this overall and it's interesting because AI is an interesting technology paradigm.

Cause I haven't, I can't think of one that has arrived with as much consternation.

And mostly because I think the way it's been packaged has been as a, a people replacer, right?

It's been, it's been viewed as an either or thing is AI gonna, take your job, et cetera.

And, I think that was maybe a tough brand thing.

Cause I, I, from what I've seen, you know, there's a lot of trepidation and a lot of the consumer surveys about AI for that, that very reason, but there's efficiency, right?

Which doesn't necessarily have to mean that.

jobs are going away.

They're going to change.

Some will go away, I'm sure.

I'm always going to bet on humans finding other, finding, finding, finding work, finding jobs that are just always going to.

So there's that bucket, right?

Then there's the using AI to develop, say, new products, right?

I mean, we just saw Politico is or enhance existing products, but with new features, you know, they just hooked up an AI firm out of Y Combinator in order to Google.

Which makes total sense, I think, for their model, to give Politico Pro subscribers the ability to comb through tons of data and get, you know, just the, just the bullet points they needed.

It's a very Washington thing.

Nobody can read anything that's not like five bullet points, I think, in Washington.

That's what I learned in my early career there.

and I think that's why Axios actually went with the smart brevity format.

That's my conspiracy theory.

and, and then there is, you know, is this going to be like a tool for, you know, Better ad revenue or of some of some sorts?

Or is it, you know, to, to create like entirely new experiences and products?

but let's, let's, let's start like, one is the efficiency, right?

And I think my thing is, has always been, that has always, but has recently been that, you know, publishing is going into a more with less era or if it's not already there.

And just the reality is a lot of the structures.

Of these businesses need to slim down, even as they do more things.

And so you've got a problem there.

It's just like, you have to do a bunch of different things, but you also have to cut down on the size of your infrastructure.

where is that the biggest opportunity for AI?

Yeah, that's really great way to put it.

I think the downward pressure on the industry overall has been for quite some time, right?

The expectation that we're just going to eliminate a lot of positions that did this one thing really well.

so now you take it on, so just report the story, edit the story, also optimize it for all the social channels.

We still want to be on, even though that's changing on a daily basis.

If you have time, could you make a video out of the story?

If you have time, could you make a newsletter to tell everyone about it?

Could you, make this into, you know, could you look at all of our tier point content libraries and try to find other, Recirculation opportunities here for something related to the article you're writing on.

All that stuff now falls on to a smaller and smaller and smaller team.

but we know in order to get more engagement to make the business viable, you have to do a lot of that stuff.

So what you either create less net new information for the world and everyone loses, or you find these tools that can help you do that.

without, with ideally like optimizing your day so that you can tell more stories as opposed to you.

You know, a larger media entity saying like, great.

I can have less people again, tell the same amount of stories.

but I think that the challenge there is, you know, when I always saw this within you know, small media organizations, because, you have to do more with less, right?

So you, you go through these periods where you have people doing four different things, right?

And you, there's total trade offs, right?

Because they don't, they're not, nobody's good at, at four different things.

Things rarely are that, but you know, using the, the, and you have real business needs, but that's the real challenge.

And, but using the tools would seem like a easy win, not easy, but it's a win for publishers in that, you know, you see it all the time with I always say, Sort of ruefully joke, the copy editors go first, right?

I mean, that's still need, copy still needs to be edited.

And I think a lot of times people go to replacement when these technologies are their best, at least the more I use them, like they augment.

You know, they're, they're an augmentation to me because I'm the writer, I, this is a unique, I'm like the writer, I'm the editor, I'm the art director, I'm like, you know, I gotta do it all.

So I love AI, if it can give me the ability to do more with less and scale myself.

Yeah, I think that's typically, I think where we're, yeah, where we're at in terms of the life cycle of understanding a lot of the tech that's out there.

One, I would say.

Early innings for AI in general and the construction of what, you know, what it's used, what it's good for, even for our own industry, it's simply not going to replace a human being yet.

It's not good enough, even at the most finely nuanced version of it to do that.

which is a great thing.

We believe that humans should never be replaced, but it's certainly good enough to get you.

Examples of how you need to optimize that article for different social platforms, right?

It's certainly good enough to get you to a place where it can provide you with a starting point summarization of your articles for your newsletter.

it can certainly bullet point out longer form pieces of content.

it can do a lot of things.

It's, and it definitely SEO optimization suggestions.

Which I like to say is kind of robots talking to robots, really, if you think about it.

It's better to leave it to the robots to sort it out.

yeah.

no, I think that's a great point.

I mean, because we always go to all or nothing and it's, things are rarely like that and I mean, I, I use AI like all the time, like last week's podcast, afterwards I like.

ran the transcript through Claude.

It was like, I'm going to write an intro to this, and I want you to like, give me bullet points of your, what you think were the biggest, the most important takeaways.

Focus on this, this, and this particular, no more than 750, you know, and did I read that?

No, of course I didn't, but did it like spur connections that, you know, ultimately got me instead of going back and relistening to the whole podcast to, to understand, like that saved me, I don't know, two hours.

Right.

And I think the result was actually more comprehensive.

I mean, this is a very small and basic example, but we start to, we start to do that across everything.

You start to add up, you know, some real gains.

Yeah, what if you're in a newsroom doing, 10 or 50 or 600, like the LA Times was when I was there.

You know, that's two hours of savings per, per editor, per day.

That's an incredible amount of savings.

And ideally is utilized to then find more stories or help tell those stories in a better capacity.

Yeah,

So which specific areas, you've mentioned like areas like, SEO, tagging, there's like a lot of, cause AI, I think originally, or just initially gets pointed at quote unquote cognitive manual labor, and I don't mean it in a bad way.

We all do cognitive manual labor.

Sometimes like a good part of my day, I feel is cognitive manual labor.

Do you know what I mean by that?

Like it's usually pointed at these kinds of use cases and for anyone who's not operated within like a newsroom, there's a lot of cognitive manual labor.

I think where I saw this in my career being frustrating is even something as simple as excitement from an editorial perspective means that they're not adding the right tagging structures to the story before they push it to go live because they broke it and they're really excited about it.

And then, you know, because they didn't do that, they didn't take the extra few minutes to figure that out.

They start immediately getting downranked by everyone who had to follow that breaking story with a regurgitation of their own story.

Functionally.

So,

I like wouldn't have tagging.

I wouldn't have tagging for our articles because I knew that the reporters wouldn't do it.

And I would be spending like the rest of my days there bugging people to like tag stories.

And I was like, I'm just giving up.

I wouldn't have to give up if, if AI was doing it for them.

Cause then.

it wouldn't be such a big chore

So that's the, there's three pieces of that, what you just said that makes my company viable and that's exactly, these are points of friction and everyone's kind of

reporters will never do the extra

step of tagging stuff.

even, even with the best of their intentions.

And frankly, that's because they shouldn't be charged with having to figure out how

know, I know it drove them crazy.

I'm sorry to any former, former reporters of mine who are listening to this because I would make them do their own art and stuff like this.

And every single time I would ask where a story was, they're like, I'm just looking for art.

I was like, if I had asked you this two hours ago, I feel like this would be the same answer.

but what about like going beyond that of creating new.

new experiences or new products.

Cause one of the things that I am, I'm really on is the more I use perplexity and these, these various tools, and yes, it's very early disclaimer, disclaimer, disclaimer, et cetera.

The more I think about how Expectations will change and I just cannot, you know, I think that the, the expectations have already changed.

I go back to, you know, same day shipping, right?

When, when people got same day shipping from, Amazon, right?

Or one click any of the, it just becomes like the expectations you go and someone's like, it's going to be five days.

I'm like, what?

What are you talking about?

Like, no, we get things and then we just complain about them.

And it's Oh, this Uber said it was going to be seven minutes away.

And it's nine minutes.

And it's you're ordering a car.

It's going to show up like on your phone, which is kind of nuts.

and so I wonder about how publishers can, we'll get to some of the defense stuff, but like how they can use this to keep ahead of consumer expectations.

You know what I mean?

So let's quickly hit on internal expectations, because that, I think, to your point, has gone from fear they're going to take our jobs, to, you know, I think the life cycle is fear they're going to take our jobs, to we're trying some stuff internally, to oh, this really works, to now almost everyone is using it in some capacity individually but not really talking about it, to okay, there's some enterprise grade tools that are specific for our use cases, let's start incorporating those, which are kind of like ours.

And now we're at a place where we just launched a thing called tone builder, which we can get you down to the specific tone of voice of a journalist, as opposed to just the, you know, the publication at large, because that was a

Oh wait, how does this work?

I like this.

I want to use this, Josh.

Yeah, well, let's do it.

it's just, it's, it's like a lot of when we say AI, I think we will have more nuanced terminology that we can deploy in the future, but it's just, Just taking a corpus of data that's specific to you, Brian, and you're, and ingesting that into our model and then outputting a version of that tone that you and I can talk about, is this you or are you more.

Bombastic.

Are you more regimented in your, you know, use of adverbs, that kind of thing.

And we get to a place where all the outputs of all of our tool sets are then in your structural tone.

Yeah.

I mean, that's like the

80 percent of the way there to 90 percent of the way there.

And we already get people who say, how do we get to like 99 percent of the way there?

A hundred percent.

And we say, what.

Remember two years ago when you were afraid of them taking your jobs?

Now you're functionally telling me that you want to train them to do a hundred percent of your tone of voice.

Well, it's about scale.

I think everyone needs to scale themselves to some degree, I think, in this world.

And it's A lot of it is to me, at least the ones that I'm using now, a lot of it is to get things 80%.

And they're not quite at 80 percent there, but I'm like, I'll take 60%.

I mean, we're talking about 0 percent otherwise.

So I'm like, I will take them particularly for certain tasks.

And I feel like you said, it's almost like people don't want to talk about how They're using AI tools because of some of the ham handed, I don't know what other word to use, of the examples early on, which were, Oh, let's replace, let's, let's pass off some robot, you know, writing something and let's gin up some like fake profiles.

It's Yeah.

I mean, obviously that's not the right way to use AI.

I mean, must we say this?

apparently we must,

I don't care what the technology is lying to your audience, generally bad.

never a good idea.

never, ever.

I mean, that's, that's also like a Rubicon.

Yeah, we have not crossed as we say, everything that we create, a human has to be involved in pretty dramatically.

right.

and we feel as if that's the way that you need to be always.

We're just being assistive.

that's internally, that's just been interesting to get to that point, right?

Where it's taking our jobs all the way to like, trying to ask us how they can get closer to taking jobs, with outputs.

And then externally, I think the big opportunity is, I still see it as being multimedia for the first real time.

and I mean multimedia now by saying like, I create a story in the function that I'm best suited to create that story in.

And now I put it into this tool structure and it can become anything else.

You need it to be with the same store Corey at its heart.

Oh yeah.

Well, that's, yeah, that's the magic of media, right?

it's like you create one, one thing and then you create more value out of it either in different windows or in different formats.

And that's when you start to unlock value where, and that's what I talk about scaling yourself.

Right.

Right.

I'm always trying to do, you know, I do two podcasts.

And I also write two newsletters.

I try to link them together in order to like, not, I mean, there's always so much I'm going to do throughout, throughout the week and trying to bring new value out of it.

Cause there's people who listen to these podcasts that don't, read the newsletter or, you know, vice versa.

And so you're not, You're not trying, I'm not trying to, you know, just be duplicative, but I'm trying to create new value in, by, by just by transferring a piece of intellectual property and quotes into a new format.

I mean, it just makes sense.

And that's going to get wilder.

I mean, we want to

go with an articulation.

Let's get wild.

How wilder?

My favorite example is like, I want, a cartoon dinosaur to sing me the top 10 stories of the morning and Kate, like a K pop

tone of voice, which is,

Do you think that stuff was really going to take off?

Feels novelty, doesn't it?

it does seem super novel, but it also seems like we're the, we're in the first inning.

Right.

I think expectations shift and grow over time.

I certainly know that that, what I just described is possible today.

Okay.

I know,

I know.

we did something, the People Versus Algorithms, this other podcast that I do, Alex, my co host, downloaded all of the transcripts of our podcast for the last year and a half or something and uploaded it to Notebook LM, which is Google's, AI tool.

It's not Gemini.

I mean, they've got like so many things going on.

I actually like, like Notebook LM a lot.

And it created a podcast.

About our podcast.

that summarized like the views and everything.

And it was, it was fine.

It was interesting.

It got some things wrong.

It said Alex was a former design head at Facebook and it was Airbnb.

But, it was interesting.

Like I'm not you know, it's one of those use cases where I'm like, I'm not.

Totally, into, but then I was like, wait a second, if you could train this on my tone of voice, like I've produced an unholy amount of content for many years and you know, I'm always trying to figure out ways to the secret of any publication.

Is it is just consistency right and consistency of voice you want the.

Advertising to sound like, you know, the same, I think, you know, the best, maybe the worst that sometimes parts of the economist or like it reads like, you know, one.

one perfect, you know, person who's, who's writing it.

And I don't know, I think things like that can, can become, you know, really beneficial.

particularly, you know, the, the, the reality is it's as I said, with more with less, you're just going to have to, there's not going to be a choice and it's not like a morality play.

don't, don't fool your audience.

Right?

if you, if the AI creates terrible stuff, then like the audience is going to see that it's going to be terrible, right?

I don't see the benefit right now of anyone saying, we're going to have the AI do, do, do the work of reporters.

And if they are, then that reporting work wasn't that valuable because from what I see from a lot of these AI engines.

I think that's right.

That's a good distinction for right now.

Maybe in the future, they'll be good enough to do that.

And I don't know what that looks like, but right now that's what the value of all this is, is we're, we're telling human authored stories, to other humans.

I think the format in which those, those people want to consume that is the benefit or the, the opportunity that we're faced with.

there's going to be some grotesque union battles on this because it's like, if you're training, if you're training the AI on, on, on people's style and approach, like what is your, you know, I mean, it's a good segue actually on, on the sort of initial battle.

Cause I know you get, you get the questions all the time about the AI crawlers, right?

Is that the first

Yeah.

I mean, we get all the, now the first question we get, if it's.

Combative or, you know, interested in something is probably how, what's the value of our archives, the foundational model companies.

And the second one would be, yeah.

Like what, what data do you use and what data do you keep and how do you, you know, disseminate between those two, those are very normal

You mean like Noda?

what data do you guys

Yeah.

What, what for us, for our tools.

So you're running all of your, your editorial through our tools, pretty much every story, once you start getting the hang of this, you know, Do we keep all that?

Do we not keep all that?

What do we do with it?

So those are pretty normal conversations.

Well, let's talk about that because they're both like kind of related, right?

I think a lot of a lot of the initial, you know, the the sort of You know, negative, as you said, we put the positive first.

a lot of the sort of negativity around this is, you know, I think it's warranted, right.

is this is sucking up a bunch of our content.

We never consented to this.

This is not maybe, maybe legal.

I don't know.

We'll see.

But like, you know, robots.

txt was supposed to, you know, they just ignored that anyway.

So, you know, like in our.

In our survey, almost a third of people said that they were blocking, some kind of LLM, from, from crawling their content.

And this is something that comes up in any of the dinners that we do.

it comes up about what to do about, about these, these AI, companies.

And most publishers are cutting deals.

You know, because they don't have a lot of leverage.

That's my sort of takeaway.

you might not think it's right, but it's, I don't see how certainly individual publishers can, can't have much leverage in any negotiation.

And then on top of that, you just see that the industry is not going to have some sort of united front for a whole bunch of different reasons.

You know, some antitrust, some just the structure of the industry.

Where do I even start there?

yes, I'd say that they ask these questions of us in terms of have we've seen the deal structure of anyone else, broadly, you know, not specifically, obviously, but I think the narrative that we've always give back to everyone and we're hearing this more and more is, you know, this is probably the 5th version of this.

So the last 15, 20 years where it's.

Hey, tech, big tech says we're creating a new technology and a new platform that's going to be robust.

We haven't figured out the marketplace for it yet.

So we need your data.

And once we get the data and get the marketplace figured out, you know, we'll figure it out monetarily, how this works with you and it.

inevitably does not work very well for media, and it works very well for them.

so I think the opportunity here is to not sit on the sidelines actually, but to start trying to set the marketplace value of your data, with these companies.

And I think that if you, you could probably argue in a mid to long term scenario that this forces some of the larger players that have not been very good at paying Google specifically for data, to start paying because their competitive set that's coming up, whether that be open AI search product that they're releasing perplexity, you know, or another one, maybe pro rata, for example, because they have that rag structure, they're going to need local market, very specific news data to prove out their search products.

Explain, explain rag for those who don't know, cause it's actually a key term.

Yeah, retrieval augmented generation.

So functionally, it's making sure that it's that the output of the AI is based on something that it has the ability to retrieve and augment that output with.

So for example, here would be a news story if they have access to all of News Corp's data.

So they would have access to all of their publications in terms of digital sites.

and you're searching for something in A Fox, local affiliate, they've told a story about it.

That search will then, augment, augment the retrieval, but basically augment the output of what it's trying to tell you, based on the data of, of Fox

Hmm.

I have a couple of axioms in life.

One is that most, parents overrate the talents of their children.

And the other one is that, publishers overrate the value of their data.

that's, yeah, I think that everyone thinks that an archival deal is probably a conversation if they have not had these with foundational monocompanies, they're thinking they might at some point and they want to know how much they get paid for it.

But what we're hearing is, you know, until the laws, to your point, are passed, it's just a polite, the robots file is just a polite way of saying, please don't take this.

It's not legally binding.

It's the problem.

So these companies are just going to say whatever they're going to say, but they're, as we've seen in anyone who does these tests, they're still taking the data.

not stopping them.

It's just a polite suggestion at this point,

Right.

And, and, and they've raised a lot of money.

They have a lot of money.

a lot of money.

And they have an ethos.

That's very specific.

I know everyone's talking about the Eric Schmidt, like what he said, steal it until you can't basically.

but

Well, they have a track record.

They have a track record.

I mean, I was running that Charlie Brown trying to kick the football gif for a decade, any of the stories about publishers dealing with these same companies.

So, I mean, at some point when you come around, no, no, no, no, this time, this time it's going to be completely different.

And, we're in this as business partners.

So, it'll be interesting because look, the reality is.

You know, there's overall like an ethos in a lot of these companies that they don't need publishers.

They've sort of walked away from news at Google and Facebook.

and, you know, Mark Zuckerberg was just out there just recently saying, you know, publishers don't want, that's fine.

Like we don't really, you know, we don't really need publishers at all for LLAMA.

so, you know, a lot of this is just the marketplace will, Sort it out.

But to me, like it, it might actually be a weird situation because I look at it's like the most valuable content.

The publishers have been creating in the previous generation was evergreen content.

It was library content because that, the structure of search and how search was the front door and news, the value.

Was never, the value just went down right when it got published, right?

If it's news, there's new, right?

Whereas in this, it seems like, you know, the value of news could actually go up in some ways because there's a ton of archival content out there, right?

But like the newest, freshest stuff, won't these models need that?

it depends on what they're using that for, right?

So if they have a search.

then absolutely they have to have it because the only benefit of new news to your point, is for search.

Like people want to know what's happening right now.

and if they don't have that, these companies don't have the ability to get that information.

Information from you because you figured out a way to block them from doing so or legally they're not allowed Then that's not a very good product.

So they won't they'll fail compared to their competitive set

Yeah.

So give me like the optimistic take for, you know, what a grand bargain would, would look like, that leaves publishers like in a, in as best of a possible position.

Wow Okay, so if I'm a let's say I'm a publisher Then, and my hope would be that regulation happens where it's, it's deemed to be, you're unable as a technology company to just take any data that I put out there.

I don't know what the mechanism for that would specifically be.

I don't think anyone really has the perfect idea of it, but you have to pay me for it, at least from a percentage perspective, so, augment or help me achieve, you know, the creation of that actual labor that goes into telling that new story, give me some version of that back in terms of revenue.

Right.

And do that at scale, do that consistently and do that pretty transparently.

But I think what is also an opportunity in this marketplace that no one's really talking about as much anymore, or yet rather is, not just foundational models, but all the applications building all this other stuff that needs that data to be successful.

I think an example that someone gave me last month that really resonated was American express travel.

I believe, you know, they have a chat bot, they have a whole travel experience, use your points here.

But how do they, what if I'm asking, you know, what's the best time to go to Paris or where should I stay there?

so con in as traveler, wouldn't that not be that archive would be super valuable for that chat bot.

Yeah.

there's thousands and thousands of those examples.

Yeah.

I think it's like a lot of the focus ends up going just on the, like the LLMs, you know, but there's, there's a lot that, that goes on and, you know, you could see a situation where the.

The licensing line and publisher balance, you just go, it gets bigger and, and, you know, the, the deals on themselves might not be massive.

Right.

But if you're cutting a lot of them, it can add up, you know, but I think those mechanisms have to get in place.

It's really interesting also from a use case perspective, the more nuanced and individualized that the publisher data is, the better they're suited for stuff like that.

So if you're a medical journal, all of a sudden.

You have a lot more interest from a wide variety of applications than I think you would have had a year ago.

and then from a data marketplace perspective, those four or five companies that are emerging are trying to do that, I think provide transparency from a basic CPM model.

Converted to per word.

That's really what we're talking about.

oh my god.

Price per word's coming back.

Who knew?

Everything old.

future looks like the past.

I still get people sometimes talking.

It's well, how much do you pay per word?

I was like, oh wow, still doing per word.

I don't like the incentives there.

So I'm like, why don't you just write really long?

That's what I would do.

Like 3, 000 words whether you want them or not.

so we might have, like, much longer content.

what, what do you, do you see, like, internal resistance to, you know, within editorial teams to, to using Noda?

not anymore.

mostly because we've been very cautious about how we go to market in terms of staying away from anything that isn't just assistive.

So, honestly, the places we're going next are more back of house than front of house editorial work.

So archival retrieval is a problem that I've never, I've never met a company that had great.

Structured archives.

so that's a, that's an area of improvement that also gets back to, you know, the, the possibility of IP.

I think creating packages for IP based on your archival retrieval.

Is a huge opportunity for the market.

you can put a matter, imagine putting parameters as easy as, you know, this had to be a murder, this had to run for a month, consistently, we had to have at least a hundred images.

We had, whatever you want.

And it just like goes through your archives and says, here's a story that's all packaged up.

This could be really compelling for the next, great documentary or whatever you want to sell it for.

that's exciting, but also the real time nature of that in terms of augmenting what you're already working on.

So.

If you're writing in a CMS, it should be able to pull up everything that's been written about that subjects whenever, in whatever capacity you want.

Like I want the last three stories written about the subjects.

I want all the images related to the subject that we've already published.

just save a ton of time and then add a lot more value.

Okay.

So you're, like, on top of the CMS.

Yeah.

We integrate within all the CMSs or not all of them, but a lot of them.

And they won't build these tools themselves, or they'll try to, but you're a specialist, so your tools will be better, right?

Yes, I believe that.

yeah.

Well, no, it's always the question.

It's like, well, wait, if you're on top of it, won't the underlying technology just add that to their, their own stack?

And I feel like I've

they will

that question a lot over the years.

I think they will add versions of this and you know, part of what I have to do as the CEO here is incentivize them to find value in just having our built and operated

tools

as I said, like I've asked this question a million times.

I don't know how many times I asked companies over the years.

I'm like, why won't Google just do this?

And I was like, do you know how many things Google is doing?

They're like, this is all we do.

I remember like Balance Exchange.

I used to love Balance Exchange.

They're like wonder kid now.

They like, when they were the only thing they did was like exit intent, was get people to give you their email address.

They were really good at it.

So many other like platforms that we use would be like, no, we can do that too.

I'm like, no, but this is all they do.

They're really good at it.

That's it.

okay.

So what about the data question?

How do you, I assume you're not stealing people's data and then selling it out the back door.

no, yeah, this is where I'll break that news.

That's what I'm doing.

shit.

no, we're, I mean, I think that's another area that we get a lot of questions and we're very conscious of and I spent frightening amount of money with lawyers.

That's one of my maxims, I guess, in life is like lawyers win.

Yeah.

The lawyers always win.

The lawyers are tough.

Although this one, you could see it being at least a draw because you can, you can.

You know, the legal bot can, can get you, you know, you might not need as many lawyers, but the lawyer, the lawyers that have remained will win.

yeah, so the idea just there being we want to be very straightforward and, transparent about use of data and there's benefits to giving us more of your data and there's benefits to not giving us any, it really depends on how you want to structure it with us.

By not giving us any of your data, we're not learning about anything that you're doing.

So you're just relying on our model version, to just give you one shot, options for what you create.

And then we talked about tone builder,

like, how do we take that data?

Do we utilize that data?

Do we keep that data?

What happens to the data?

And then broadly, do you want your own instance of our entire company basically built for you that goes nowhere else and you control, and that's the highest level of the other way that gets back to kind of like no data again, almost.

Yeah.

So I guess it's just you're going to be transparent about it and there's trade offs to different approaches and like companies need to end up making, making those trade offs at the end of the day, right?

Yeah.

I mean, it's still just, I think part of what gets lost with a lot of this is it's just a higher level version of machine learning and machines can only learn when you give them information.

So if you limit the amount of information you give them, your outputs are not going to be.

As nuanced as you would hope them to be.

So there needs to be some sort of understanding there that there is that trade off now from a foundational model perspective, I think we saw a lot of the conversations happening around.

We can't use, we don't want to work with anyone in AI because, you know, they've been training on this data that was illegally taken, which I think that's.

A little, bit of hyperbole right now.

It's not necessarily illegal.

It just may be illegal in the future.

but the reality there is, is there's no way in which anyone besides five or six companies can compete at the foundational model perspective.

We have open source models that are accelerating their ability to be almost as good, but a lot of those are built by.

some of these large tech companies.

so what we've done in the background, we've just created effectively the lattice work around all of these model structures, and we are constantly training new ones, both open source and closed in the backend.

And then we're chain modeling.

So a lot of our outputs that you see, one output will be from one, one weighted version of our subsetted, model structure, another will be from another, and that's just because they're good at different things.

So you talk about like, you know, tagging and SEO and, and, and even then, but then you get into like tone builder, play this forward though, like three years, right?

what is this?

How, how is this technology being used and how is news being produced then?

And I mean, it's hard to look ahead.

I was going to do five years, but it's impossible.

I have a feeling it's going to be.

Incredibly different in a lot of different ways, not just operationally, but I think the way people are retrieving information is going to be radically different.

And if the way people overall are retrieving information is going to be radically different.

I just do not see how, You know, news companies that are in the information retrieval business at the end of the day are not going to end up, you know, what they, how they operate and what they produce is not going to be radically different.

I think that that's right.

I don't, I hope it's three years, but it's probably, you're probably right.

I think we can we can extend the time horizon because of our own industry to five and it's still like what we just predict in three.

but yes, I think structurally where we, where we've been, has been having conversations for 20 plus years about, okay, where's the value of this, this kind of entity go.

what do we do next?

How do we transform?

Just like technology has transformed the expectations of everyone to receive information in new ways.

and I think the reality is like this technology now gives us the opportunity and a head start over almost every other industry.

I want to point that out again, like it is ready to go and it works for our industry today.

Proven.

Works today.

And many other industries are like, Oh, novely this, yeah, down the road, we can see how this will really make an impact.

But today you can change your entire structure of your company to suit the needs of the future.

Yeah.

And hopefully

what that means is more journalists, less everything else from an infrastructure around it to support it necessary.

yeah, I mean, my thing is inevitably a lot of organizations will be leaner.

They'll be smaller.

Right.

But I think the weight of what those organizations do will be.

My optimistic take is that it will be more aligned with the overall purpose of the company.

I don't root for anyone to lose their job, whether they're an ad ops or anything.

Right.

But for these companies to fulfill their missions.

they're going to need to, to cut down and be more efficient with a lot of the in between that is not, making the product.

so, I mean, just to take a step back, I mean, the whole goal is to have a sustainable news ecosystem.

And I don't, it's not like you're going to get there by having a flood of money coming in, like from the government or all of a sudden, Mark Zuckerberg is going to wake up and be like, Oh yeah, I, I, I need to like, no, that's just not happening.

So, I, I think, you know, it has to, it has to move in that direction and have to use technologies in order to, you know, do not just do what.

What's being done now, quote unquote cheaper, but to do it better.

And you know, that that's where the weight of the industry will have to move.

I would guess.

I would agree.

I mean, it just, it shifts back to the fundamental thing that humans are best at, which is storytelling.

Yeah, I mean I'm a little wary of how of how far it will encroach in some of the storytelling but I have a feeling that it's gonna put a premium on on more human You know, experiences of all kinds.

And, we see it all the time with the mechanized agriculture industry.

You know, people pay premium for, for, all kinds of like craft and bespoke things.

And, you know, there's a lot of, it's not like it depends on the model, right?

Like there's, there's absolutely room.

I believe I might be in a minority here for AI produced.

There is absolutely room for it.

I don't think that the LA Times should do it.

I don't think the New York Times should do it.

doesn't fit with their models.

But, I, I definitely, you know, if you look at what the way these tools are going, they're going to be used absolutely in, in all aspects of the publishing business.

And that includes creation.

Some people will use it more.

I mean, I.

I think that provides an opportunity because the market can sort it out.

people will price, you know, the different content.

you know, some organic meal costs very different, than, you know, a trip to the McDonald's drive thru.

That's a good way to put it.

I think, I think there's also just the human desire to.

Understand, that someone else is doing this, right?

There's, there's that craft, I guess, to your point.

I want a very smart person that I respect to have thought through this, so that I can get their take about what this is, as opposed to just like a regurgitation of the facts.

Yeah, I believe that.

I just think it's just the same.

It's like, they're not going to be robot waiters.

There's just not, maybe in I don't know, Jimmy Johns or something of that nature.

You're not going to go into like French laundry and get like a robot wig.

I'm trying to think maybe in some parts of Japan.

I mean, they really like, but anyway, overall, I think the point, the point holds, awesome Josh, anything we didn't cover that we should have?

I think we even got to some of the fun parts of what's happening from a data marketplace perspective, which is great.

Brand new excitement.

no,

Okay,

I loved it.

I had

cool.

Awesome.

Josh, thank you so much for doing it.

It's great.

I really appreciate it.

Thank you, Brian.