Mind Readings: The Role of PR in the Age of AI

The age of generative AI. PR folks have been talking about the future of PR for a long time. And my old friend Todd Defferin used to say that the role of a smart PR strategist in public relations is to understand what it is that public relations does and then to innovate on that. So what does public relations do? Like the name would suggest, how you as a company or your brand or stakeholders relate to the public.

Now, in the past, this has largely been media relations. Uh, things like getting your spokespeople on the Oprah Winfrey show, or Good Morning America, getting a placement in the New York Times. But then when the internet happened and the digital age happened, it meant getting your brand in front of influencers or in popular YouTube channels or in any of the places where people pay attention, right? Public relations really is about attention management. How do we get attention for whatever it is that we have from where it is?

If you think about attention being a commodity, right? Think about the even the expression we pay attention, we pay attention, we have attention, we pay it to different things. Public relations has always been about where is that attention and how do we get someone to pay some of that to us, whether it is the broadcast journalist, the newspaper person, the YouTube influencer, whoever it is. Well, there's a new party in the mix. There's always been people, obviously, um, who have been paying attention, but the new party is artificial intelligence, particularly generative AI and how it recommends things to the public, right?

There is an intermediary between the public's attention and us and our our customers or our our business. How do we relate to that intermediary? How do we get that intermediary to recommend us? Well, a big part of understanding this is understanding how generative AI works. How does this tool recommend things when you ask a tool like ChatGPT or Google Gemini or Anthropic Claude, uh, hey, recommend uh some AI consulting firms.

If I have a really good, there should be a lot of text on the internet about me. And in turn, that means that when these models train from this data and learn from this data, they would associate my company, my brand or whatever with um that search term or that that discussion topic. It's not really search, it's it's more discussion. And so a few folks have coined terms like AI optimization or or AI engineization or things like that, and I which is fine. Um, but public relations really is one of the drivers of that.

For example, if I get a piece of content placed with a popular website like martech.org, for example, there's a lot of syndication. A lot of people copy them, sometimes with permission, sometimes without. All those copies also end up in generative AI models because they all train on this corpus called common crawl, commoncrawl.org. And in common crawl, you have pretty much the public internet. So the more your content is shared and published in more and different places, the more likely it is that a model is going to train on it.

And the more times it sees your name, the better. This is why it's a good idea to get into publications that have a lot of syndication. So uh one of the things that a good PR firm or good PR practitioner would know to do is look at how much distribution any one piece of content gets on an a per outlet basis. How many times does somebody copy a content marketing institute article? How many times does somebody copy Christopher Penn's blog?

Web, you could say, like, yeah, this outlet may not have the biggest audience, but boy, does it get a lot of syndication? And as a result, from the purposes and intent of getting machines to ingest that and associate it, it's a really good outlet. So that's an interesting and new twist on how recommendations happen. With generative AI, we are looking for opportunities to get machines to ingest our stuff and doing it as many places as possible. What is that strategy look like?

Well, part of it is research, part of it is understanding which outlets get the most distribution in any means, names, but who gets the most distribution. And they're really there. Um that can help identify that. One of my favorites is Talk Walker. Uh, Talk Walker has the ability to look at how things spread on the internet.

You give it a URL and it says it can say, here's where this was shared. And you can see a sort of almost a chain of evidence, which is a pretty cool visualization. So that's one uh of the many, many tools out there that a good PR practitioner or a good PR agency knows who the big spreaders are, and then you can get uh you can focus your program on getting your content into those places. Another aspect or an angle of this is to say yes to everything, right? Say yes to everything that you possibly can.

Hey, someone wants you to be like a guest on their podcast. As long as they put a transcript up and maybe it goes up on YouTube, great. Say yes to it. Uh, even if it has two listeners, who cares? Because we're not talking about uh influencing humans, we're talking about influencing machine models.

Uh say yes to guest blog posts where you blog on someone else's blog. Say yes to contributed content, say yes to uh speaking on a webinar or being on someone's live stream or hanging out on Twitch or whatever. Any place where you can generate text and multimedia is a good thing. I was recently doing some transcription of episodes like this, and I noticed something funny started happening, and that the funny thing was it was starting to put my name at the front of the transcript to say Christopher Penn, Colin, and then the show transcript. Apply my name to the train guys whisper model.

I don't supply my name uh to that, I just give it the raw audio. So why is it putting my name in there? Because of the the way that I speak and the type of language I use is dissive enough that the language that Whisper is using recognizes it and puts my name in it. Well, where did it get that from? It got creeping millions of hours of YouTube videos, including most of my channel.

I have a huge body of work on YouTube, and as a result, when a model came by, uh a model maker came by and scraped all of that, it discovered associating my closed captions with my voice, and now the model kind of recognizes me with or without my effort, which is kind of creepy when you think about it. But at the same time, also free advertising. Uh, if there are other people who sound like me, it's probably gonna put my name in their transcripts. So I've done a good job by having a lot of content out online of influencing that model. So be everywhere.

Make as much stuff as you can, hire a good PR agency or a good PR team to make as much stuff as you can. It doesn't have to be purely prize winning, it just has to be readable and valuable enough that if a human does stumble across it, they're not going to be like insulted. But make as much stuff as you possibly can and get it out on the public internet as quickly as you can so that when model makers train their models, like Meta just released their Llama 3 model and it was up through the data it to November 2023. If your stuff wasn't out on the web, by then it's not in Lama 3. If it is, if your stuff was out on the web, Llama 3 knows about it, at least to some degree.

So that's what PR looks like in the age of generative AI. You have a new stakeholder, and that stakeholder are the language models themselves. Thanks for tuning in. We'll talk to you in the next one. If you enjoyed this video, please hit the like button.

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