Neville has been ill and unable to record, so Shel is on his own in this episode (except for Dan York’s Tech Report). This shorter-than-usual monthly long-form episode includes reports on rethinking thought leadership, maintaining “brand sovereignty” in the AI era, and whether hedging in your communication can serve a useful purpose. Dan’s report was recorded in Vienna, Austria, where AI was front and center at the 126th meeting of the Internet Engineering Task Force. Dan also reports on Bluesky’s Attie AI feature, Instagram’s plans to charge for AI access, Beehiv’s new community feature, WordPress’s plans for version 7.1, and some UK social media regulatory updates.

xx

Links from this episode:

Links from Dan York’s Tech Report

The next monthly, long-form episode of FIR is tentatively scheduled to drop on Monday, August 24.

We host a Communicators Zoom Chat most Thursdays at 1 p.m. ET. To obtain the credentials needed to participate, contact Shel or Neville directly, request them in our Facebook group, or email [email protected].

Special thanks to Jay Moonah for the opening and closing music.

You can find the stories from which Shel’s FIR content is selected at Shel’s Link Blog. You can catch up with both co-hosts on Neville’s blog and Shel’s blog.

Disclaimer: The opinions expressed in this podcast are Shel’s and Neville’s and do not reflect the views of their employers and/or clients.

Raw Transcript:

Shel Holtz

It’s an unusual FIR episode today, with just me and three reports for this long-form installment.

Thought leadership isn’t what it used to be—or at least it shouldn’t be what it used to be. With AI summaries increasingly becoming the way people get the information they’re looking for, how do you maintain your brand’s sovereignty over the information that gets shared about it?

And do you hedge in your communications? There’s actually research into whether hedging is a good or a bad thing.

That’s what’s coming your way in this shorter-than-usual monthly long-form episode of For Immediate Release.

Hi, everybody, and welcome to episode number 523 of For Immediate Release. I’m Shel Holtz in Concord, California. This is our monthly long-form episode for July 2026, and I am on my own.

You may have noticed that we didn’t post a short midweek episode last week. Neville has been quite ill, with an infection in his chest and some other issues, some of them related to the ridiculously intense heat they have been suffering in England. It has kept him from being able to record.

He is planning a trip to the U.S.—here to the Bay Area, in fact. We are scheduled to have lunch on my birthday while he’s here, and right now he is focused on doing everything his doctor has told him to do so he can make that trip. That’s a decision I fully support.

It has been way too long since Neville and I have seen each other face to face in the same room. I am really, really looking forward to it. I hope you’ll join me in wishing him a speedy recovery.

Rather than skip this episode, I’ve decided to do it on my own. We used to do this fairly routinely back in the day, when both of us were full-time consultants and traveled a lot to meet with clients.

Frequently, one of us wouldn’t be available on the day we were recording. The other would record solo, or occasionally, if I had enough notice, I would find a guest co-host.

But today, you get just me.

At this point, we usually start with Neville providing a wrap-up of the episodes we’ve recorded since our last monthly long-form installment. I will take that on, along with a comment or two we have received related to those episodes.

Episode 520 was our long-form episode for June. It covered the PR meltdown that was going on among the big AI frontier labs.

There were five other topics, including one about Wowcher, a U.K. coupon company that sent an email with a promotional statement that upset just about everybody.

It was related to a young child who had been picked up and put into a crocodile enclosure and was in critical condition, the last I heard. Wowcher’s email said, “Snap up these deals quicker than a croc can catch a kid.”

Yes, if you didn’t hear the episode and you’re hearing this for the first time, this is not a joke. This is not The Onion. This was a real promotional message from the company, and it got hammered over it.

Tim Sutton left a comment saying:

“Your closing line is the whole thing, Shel. The ‘AI approved it’ defense itself is never enough. An approval step is not bureaucracy. It’s where a human asks the question no machine thinks to ask: How does this read on the worst possible day? Strip it out to move faster, and you have not saved time; you have removed the brake. I have seen the aftermath.”

Episode 521 focused on Ford rehiring people it had previously let go, ostensibly because AI would be able to do their jobs. AI was not able to do their jobs.

Rick Segal found it interesting that Microsoft, his alma mater from the 1990s, had decided to let the graybeards and their institutional knowledge walk out the door through early retirement rather than undertake the hard-core and honest “Oops, we overhired” cuts.

The decades of knowledge walking out Redmond’s door, he said, are going to be felt eventually.

Eric Carroll replied to Rick, saying:

“They will pay for replacing expertise with engines of mass satisficing. Just as you say, how long will the blast take to propagate? From what I am hearing and seeing, the return on misinvestment is way faster than I expected.”

Episode 522 was about Podcasting 2.0, a new set of protocols Adam Curry is working on with an engineering colleague named Dave Jones.

We talked about whether this would be good for podcasting and podcast listeners, the likelihood of widespread adoption, and some of the obstacles standing in its way.

Vincent Bruneau wrote:

“The slower-than-hoped-for adoption is the most interesting part of the Podcasting 2.0 story. Richer metadata, transcripts, chapters, and better accessibility are genuinely useful features. So why hasn’t it moved faster? That gap between good technology and actual adoption is always where the real communication lesson lives.”

Juraj Schaefer, a podcast producer and editor, wrote:

“Interesting perspective. As podcasting evolves, ownership, discoverability, and meaningful connections will become even more important.”

And Dakshina Senadheera, a podcast editor and manager, shared this thought:

“Interesting conversation, especially around keeping podcasting open while improving the listener experience.”

Thanks to everybody who commented on our previous episodes. You are always welcome to comment.

You can leave comments on LinkedIn, where we announce the episodes, as everybody whose comment I read today did.

You can also send us an audio or text comment by email at [email protected]. You can record a comment directly from the FIR website, FIRPodcastNetwork.com, by clicking the “Send Voicemail” button on the right-hand side of the screen.

Or you can leave a comment in our show notes. There are all kinds of ways you can comment and participate in the show.

I also want to let you know that the interview we did with Pete Blackshaw about the Answer Economy is now available.

It has been getting some really good reactions. People have found real value in this discussion about how AI answers are now the answers people are getting about your product, regardless of where the information the frontier models accumulated came from.

You can find that in FIR Interviews.

The latest episode of Circle of Fellows is also available. Episode 131 is about the evolving media landscape and what it means for media relations.

Our panelists included Diana Degan, a new IABC Fellow from the 2026 class of Fellows, along with Ned Lundquist, Martha Muzychka, and Jennifer Wah.

They talked about whom we reach out to when there are fewer reporters available to tell our stories through the mainstream and trade press we have been accustomed to.

The next episode, coming up on the third Thursday in August at 6 p.m. Eastern, is about AI and the kinds of pivots communicators will have to make as AI becomes a more widely used tool in the communication toolkit.

The panelists will be Bonnie Caver, Adrian Cropley, Theomary Karamanis, and Mike Klein. I’m looking forward to that.

Now I have three reports, as I usually do in the monthly long-form episode of FIR. You just don’t get three from Neville.

As I mentioned earlier, this is going to be a shorter episode than usual.

Two pieces landed in the search press recently that I think belong together, even though they were written a few weeks apart by people who probably weren’t talking to each other.

The first is by Bill Hunt at Search Engine Journal. Search Engine Journal has been around a long time and has been a great source of information about search and adjacent topics.

Hunt points out that, for 20 years, digital strategy meant driving people to webpages. We deliberately fragmented our information across dozens of pages, each optimized for a different stage of consideration.

The example Hunt uses is Ford and its F-150 pickup truck.

The homepage sells the lifestyle. Model pages introduce the trim levels. A configurator lets you picture yourself owning it. Feature pages handle towing and off-road performance. Specifications live even deeper in the site, next to regional offers and financing.

For a human being, that architecture is beautiful. Every page does a job.

For a machine, it’s just friction.

When an AI can’t find a dense, complete answer on your own domain, it doesn’t give up. It assembles the best answer it can from whatever is easiest to retrieve.

Consider the story that broke last week about an OpenAI model escaping from its sandbox and hacking its way into Hugging Face.

What was it looking for? It was looking for the answer sheet—the cheat sheet for the test it had been told to solve.

Rather than do the work to solve the test, it went hunting for the cheat sheet that had all the answers in one place.

That’s no different from this.

Hunt searched for the gas mileage of an F-150 Raptor. The AI Overview built its answer from Reddit, an automotive publisher, and a local dealership. It never touched the Ford website.

Ford has that number. Ford has every number.

Gemini just found it easier to assemble an answer from somewhere else where all that information was in one place.

Hunt calls the thing you’re trying to protect “brand sovereignty”: your ability to remain the authoritative source about your own products, services, and expertise, no matter where the answer eventually gets delivered.

He is emphatic that this is not a search engine optimization problem. It is a governance problem, because no single team owns the whole picture.

Product information, documentation, customer support, legal policy, and commerce are all owned by different parts of the organization. All of them shape how your organization gets represented, and they have been evolving independently for years.

His summary line is one I would hang on my wall: Your website is no longer your digital asset. Your knowledge is.

Communicators have spent 30 years arguing that the corporate website is the front door.

Hunt’s case is that the front door is now a machine reading whatever knowledge it can find. If yours is scattered across content management systems, PDFs, and support portals, the machine will find the gaps—and it will fill them from Reddit.

Meanwhile, Gaetano DiNardi, writing in Search Engine Land—not Search Engine Journal, but another great, longstanding search-focused publication—looked at what is being sold to companies that want to fix exactly this problem.

Once the industry decided that off-site brand mentions drive AI visibility, a market miraculously appeared to sell them. He audited several highly rated vendors selling brand-mention services.

What they are selling turns out to be variations on one thing: renting space on websites nobody reads.

Some of it involves placement on what the SEO world calls private blog networks. These are clusters of sites that exist for no purpose except to sell mentions and links to whoever is willing to pay for them.

DiNardi found those going for 10 to 15 times what a comparable link cost in the old SEO market.

Some of it involves placement on sites with no actual subject-matter focus.

One example he cites has a page about learning-management software sitting alongside listicles ranking the best crypto wallets. That is basically a billboard that will print anything.

Some of it is Reddit astroturfing.

Agencies use what are called aged accounts—profiles built up over months so they look like real community members—and use them to post brand mentions in subreddits that have nothing to do with the brand.

Those posts are frequently removed within 30 days for violating community rules, which tells you exactly what the communities make of them.

Then there are the mechanics.

There is a Slack workflow. The agency generates a placement opportunity. A junior marketing assistant with no way to evaluate whether the publisher is legitimate approves a fee.

In DiNardi’s example, that fee is $250 to add the mention. The agency pays the publisher and then invoices the client to recover it, on top of the retainer.

The Federal Trade Commission’s endorsement guides—and that is a U.S. agency, so these are applicable only in the U.S.—require clear disclosure of paid placements.

These pages generally are not updated to say that the mention was purchased.

Lily Ray, who is quoted in the piece, says this is another evolution of spammy link-building. We have seen this movie before, going back to Google’s first Penguin update in 2012.

The reason it appears to work right now is that large language model citation systems are still immature compared with Google’s spam detection.

Volume from low-quality sources may be rewarded in ways it would not be in old-school search.

DiNardi puts that window at perhaps one to two years before the platforms build countermeasures. He also notes that marketers chasing volume may be confusing the models about their own entities in the process.

Here is why I mashed these two stories together: They are the legitimate and illegitimate answers to exactly the same question.

Who controls what the machine says about us?

One answer says: Organize your knowledge so you are the most useful source available.

The other says: Pay strangers to say your name.

The first is a governance project, and it is the one you should be focusing on rather than waiting to be invited to participate.

Nobody else in the building has responsibility for how the organization is represented as a whole. That is within the purview of the communication function.

The second is going to show up on your desk as a pitch or proposal, probably coming from the marketing department, probably with a persuasive percentage attached to it, along with a deadline.

When it does, the questions you should ask are the old ones.

Is it disclosed?

Would we be comfortable if a reporter published the invoice?

Are we buying a spot on a page that also sells spots to our competitors?

We spent a couple of decades getting pay-for-play out of media relations. I would hate to watch us import it into AI visibility just because the metric is new.

Dan York

Greetings, Shel, Neville, and FIR listeners all around the world. It’s Dan York coming at you from Vienna, Austria, where I’ve been attending the 126th meeting of the Internet Engineering Task Force, or IETF.

These are the engineers and others who make the internet work through all the various protocols—HTTP, email, and all those kinds of things.

One of the big topics this week, of course, was AI. There were a number of sessions looking at what kind of work needs to be done.

For instance, in a world where everybody talks about “agentic, agentic, agentic, agentic,” do we need new protocols for communicating when an agent goes to book airfare and interact with all sorts of systems? Are new protocols needed?

Part of the genius of the internet is that it is built from small building blocks that can be used to do things and then reused in many different ways.

One of the things people are finding is that many of the existing protocols work well. But we are still trying to figure out, in this new world, what is happening and what new things are needed.

One thing happening in the standards world is the same thing we are seeing throughout the rest of the communication world: a lot of slop.

There is a positive side to this. The IETF conducts all of its work and develops all of its standards in English. If you are not an English speaker, or English is not your primary language, it can be challenging to help create new standards.

Back in the early 2000s, before we had all these new tools, I helped some people for whom English was not their primary language. It was painful because they were trying to create standards and describe how they worked, but their English was difficult to read. I helped them improve it.

Now, with these tools, people can contribute in English. They can put their material into the large language model of their choice and get good English back in the format of an internet draft or standard.

That is the positive side. Suddenly, millions or billions more people around the world are able to participate in the standards process in English.

The negative side, of course, is that people are generating so many contributions that they take a long time to triage. This creates a tremendous amount of work for reviewers, leaders within the IETF, and others. Everything is taking much longer.

We have seen this in many other areas. Put up a job advertisement and you get a bazillion applications. Publish a blog post and you get a ton of comments. All these things are happening.

One thing I had not paid as much attention to was the fact that all these email tools now have a feature that says, essentially, “Write a better email.”

People are using that feature, turning what might have been short, not particularly well-worded emails into big, voluminously long messages. That is generating a lot more traffic on the email lists people use within the IETF.

It gets us back to the situation we have seen many times: You have five bullets, feed them into an LLM, and it generates a long block of text. Then the text is too long for someone to read, so they use another LLM to turn it back into five bullets.

There we are, with the snake eating its tail.

There have been a lot of interesting conversations. We’ll see where all this goes.

Speaking of AI, a couple of other things have happened in the broader industry.

First, you may or may not have noticed that Bluesky announced Attie—A-T-T-I-E—its AI assistant. It started as something you could use to build social feeds within the Atmosphere, the broader AT Protocol ecosystem.

You could use Attie to create these feeds. Bluesky has now announced that it is expanding Attie into more of a chatbot that you can ask for information and news from across the broader Bluesky network—the Atmosphere, as it is called.

I don’t have access yet. I’m on the waiting list.

They say these are not chats. They are “quests.” Yes, you heard that right. They are quests—a new way to explore the Atmosphere.

You could ask questions such as, “What’s trending in my network today?” “Who’s worth following in climate tech?” or “Put together a daily briefing on indie game development.”

We will have to see what this looks like, how it works, and all those kinds of things. But it is another example of AI coming into the Bluesky space.

AI systems, of course, cost money to operate. Instagram chief Adam Mosseri has said this is really expensive and that the company will eventually have to throttle people or ask them to pay.

If you are a communicator who has been using Instagram’s built-in AI to generate campaign content, create images, or perform similar tasks, casual use is still free right now.

At some point, however, if you use it at high volume, you will probably wind up being charged for credits or have to take those costs into account.

Stay tuned on that.

Switching to newsletters—but remaining on the subject of AI—Beehiiv, B-E-E-H-I-I-V, one of Substack’s competitors, had a major release this month.

It rolled out something called Communities, which lets you create a community around your newsletter that people can join, where they can chat with one another and do those kinds of things.

At the same time, Beehiiv added AI components, including an AI assistant that can help you examine your content and subscribers, particularly on the administrative side.

Again, we are seeing more AI appearing in different places.

Speaking of AI—as that seems to be the theme of my report this month—I’ll also tell you that WordPress 7.1 is currently scheduled to arrive on August 19, before my next report. The timing aligns with WordCamp US here in the States.

The release will bring a number of new features, including more of the collaboration functionality that was part of the original plan for WordPress 7.0.

It will include notes and other features, along with more collaboration and AI elements. That is coming on August 19.

Finally, let me close with a policy topic.

The U.K.’s Ofcom is pursuing two different initiatives. It has announced a forthcoming ban on anyone under 16 using social media. I’m not entirely sure what that means in practice.

It has also announced that it is investigating TikTok’s compliance because it does not believe the platform did enough to prevent people under 13 from using it.

This will be a test of the U.K.’s law, so we will see where it goes when it reaches the courts.

There is also a proposal under which people younger than 16 would be banned from social media, while 16- and 17-year-olds would somehow magically be blocked from using social media between midnight and 6 a.m.

It remains to be seen how any of that can be turned into reality.

The other problem people have pointed out is that all you are doing is blocking children from seeing some of the harmful material. You are not actually getting rid of the terrible content on the internet.

Everybody else is still exposed to it, including seniors and others who may have as many issues and challenges with it—if not more—than some of the young people in that space.

Anyway, that’s all from here, Shel. I think I’ll go get some Wiener schnitzel and a beer.

Until next month, that’s all. Back to you.

Bye for now.

Shel Holtz

Thanks, Dan. I really enjoyed that report. I was particularly struck by two of the items that you reported on. The first was Addy for Blue Sky. I just really like the idea of using AI this way within social networks. That would come in so handy if I could do that with, say, LinkedIn, rather than use the current search tool, which is fundamentally worthless unless I’m just looking for a person.

Or a company, but if I’m looking for threads around certain topics, it’s really tough, and something like that would be very useful. I’m not on Bluesky enough to really make a difference, but you know, on LinkedIn, maybe even Facebook, that would be awesome. Maybe they’ll pay attention to this and follow suit. Also, beehive with the communities, I think, is terrific because building a community around a newsletter can be tough.

And I think this might signal a way that Substack and Ghost and the others might be able to play in that space. So it was all interesting, Dan, but those were the two that stood out for me.

We talk about thought leadership from time to time on FIR. It has been a tried-and-true content marketing tool for a long time.

It has also been a source of some cynicism.

A stack of research has landed over the past few months that says two things at once: Thought leadership is producing measurable financial value, and most of what organizations are publishing under the label of thought leadership is utterly worthless.

Since two things can be true at the same time, both of those things are true.

The gap between them is where the opportunity lies for those of us who do this kind of work.

Let me start with the number from Axios that got my attention in the first place.

In April, Axios reported on a study from a firm called Cardinal 40 that found high-quality CEO thought leadership was associated with an average of $367 million in shareholder value in a single week.

Here is how the researchers got to that number.

They analyzed more than 1,000 examples of CEO thought leadership from S&P 500 companies and measured each against abnormal stock returns, deliberately excluding anything tied to market-moving news or disclosures.

They were trying to isolate the effect of the words themselves.

Then they did something really interesting.

They tested more than 60 common writing traits—tone, readability, and the kinds of things we all obsess over when we are editing copy.

They found nothing that explained why some pieces outperformed.

So they used AI, because of course they did.

They compared each document with a curated canon of genuinely standout thought leadership and found that communications that sat semantically closer to that canon were associated with stronger returns.

The gap between top-tier and bottom-tier thought leadership worked out to about a nine-tenths-of-a-percentage-point swing in stock performance the following week.

For the biggest companies, that is not a rounding error.

The report estimates as much as $25 billion across the Magnificent Seven—the seven megacap U.S. technology companies: Nvidia, Apple, Alphabet, Microsoft, Amazon, Meta, and Tesla.

The research also found that more thought leadership does not produce more value.

Weak or low-quality communications correlated with neutral or negative outcomes. In the age of AI slop, volume is not merely useless. It can cost you.

Compare that with The Harris Poll’s research on the ROI of thought leadership.

Nine in 10 executives say thought leadership is critical to building authority, and only 20 percent say theirs is actually effective.

Executives in that study estimated a 14-times return on investment, and Fortune 100 executives put the annual value at about $3.6 million.

I need to hedge here a little.

The Harris fieldwork was conducted in May 2022 among 500 U.S. employees at the director level or above. It is still being cited in 2026 as though it is fresh, and it is not.

Second—and this applies across the board—nearly every organization publishing research on the value of thought leadership sells thought leadership.

Harris has a thought-leadership practice. IBM’s Institute for Business Value is a thought-leadership shop. Cardinal 40 evaluates thought leadership for a living.

That does not invalidate the work, but it should temper any enthusiasm you feel about the research.

For what it is worth, IBM’s research is the most consistent of the batch.

Eighty-eight percent of executives consume thought leadership, 87 percent say it shaped a purchase decision within the previous 90 days, and about half of C-suite leaders credit it with driving revenue growth.

One more data point from the Axios piece is something I cannot stop thinking about.

Mentions of “storytelling,” “narrative,” and “storyteller” on corporate earnings and investor calls are up 65 percent since 2020, according to AlphaSense.

The language of our discipline has migrated into the language of capital markets.

It is important to treat the dollar figures as directional rather than literal. Correlation is doing a lot of work in these studies.

Coherent, original executive communication may well be a proxy for a well-run company rather than a cause of its performance.

But the through line across all this research is consistent, and it is the one you can actually act on: Quality is doing the work, and quantity is doing damage.

If the value is real, why is only 20 percent of it working?

The Content Marketing Institute brought together a group of practitioners in July to work through exactly that question: Jill Roberson from Dataweavers, Andrea Ames from Eaton, Lindsey Hagen from Conductor, and the Content Marketing Institute’s own Robert Rose, one of my favorite people to read and listen to.

I love This Old Marketing with Robert Rose and Joe Pulizzi.

Jill Roberson made a point that stood out for me: We have to reset expectations for what thought leadership even is.

Hagen’s point was that the bar has simply risen.

You have to be useful and unique now, and the standard for what qualifies as valuable is much higher than it was.

Roberson recommended putting the hypothesis at the beginning of a thought-leadership piece and making it unmistakable, then delivering on it immediately.

If people are not getting the insight they came for, and getting it quickly, they are gone.

But delivering quickly is not the same as creating quickly.

Ames’s advice was to slow down and be genuinely intentional about the topic.

Robert Rose made the observation that I suspect a lot of you have been waiting for someone senior to say out loud: Our industry has convinced itself that speed is its foundational value, and it just isn’t.

Then there is gating.

Ames said Eaton does not require contact information for its content. There are no forms you have to fill out before you get the download link.

Her reasoning is the reasoning of 2026: She wants large language models to be able to include Eaton’s thought-leadership pieces in the results they produce.

Being exclusionary, she argues, mostly hurts you.

That means the lead-capture form—which has always been a tax on distribution—is now also a tax on being cited by the systems your buyers are querying before they ever contact you.

The Content Marketing Institute also ran a piece in the fall by Abid Rahman, written with Kate Houston, who runs executive thought leadership at Amazon Web Services.

Their diagnosis is pretty blunt.

AI can draft, polish, and structure this content with remarkable efficiency, but it cannot supply credibility, lived experience, or judgment.

It reads as though anyone could have prompted it because anyone could have prompted it.

They offer three ingredients for the real thing.

The first is credible experience.

I talk about “genuine lived experience” somewhat derisively because I read people on LinkedIn saying, “AI has no lived experience,” and then I look at the kinds of things they are writing, which required absolutely no lived experience.

But in the case of thought leadership, it really does matter.

You have to have credible experience to support your ability to make these proclamations.

You also need a genuine audience need and an insight that not many other people can provide.

Then you apply a five-step framework: Define the goal. Choose the focus. Shape between one and three core themes. Build a voice ecosystem of leaders, customers, and advocates who actually have some standing. And map the stories to the right channels.

Their measurement point is one I would like to tattoo on a few people’s foreheads: Thought leadership is not an engine for marketing-qualified leads.

In one of their programs, they do not blast out content at all. They put a CEO in credible venues.

Under those circumstances, brand awareness among the ideal customer profile rose from 17 percent to 51 percent in one year, and request-for-proposal volume increased three-and-a-half times.

Could they attribute a single article to a single lead?

Of course not.

That is the honest answer most of us should be giving.

Now, a different angle.

Yogesh Shah, writing in Entrepreneur—and this goes back to January—argues that the problem is not the thinking. It is the container.

We are in a zero-click world. Audiences do not leave the platform they are on.

Roughly 90 percent of decision-makers say they are more receptive to companies producing high-quality thought leadership, yet engagement keeps declining anyway.

His question is: If a report can be summarized in ChatGPT in seconds, why would anyone read it?

His answer is what he calls experiential thought leadership.

Turn the insight into something people are in rather than something they open.

Think of a live discussion, a workshop-style webinar, a tightly curated roundtable, or a podcast that puts listeners inside a recognizable scenario instead of offering an expert monologue.

He is emphatic that this does not require a large budget. It requires one well-designed moment in which attention is protected.

You can see the tension between these two pieces of advice.

The Content Marketing Institute says to slow down and do the deep work. Entrepreneur says the document is the wrong delivery mechanism.

I do not think those ideas conflict.

There is a line in a Savanta piece from June that I think captures the entire argument in 12 words: You can replicate a product, but you can’t copy a point of view.

The case study from the author of that piece, Matthew Mott, is aimed at technology companies, but I think it applies elsewhere.

A competitor can reverse-engineer your features, match your pricing, copy your positioning, and even hire your people.

What it cannot copy is a track record of saying interesting things that turned out to be correct.

That builds slowly, and it compounds.

His second observation is that buyers cannot really evaluate an AI product.

The technology is opaque, and every vendor’s claims sound alike. Buyers stop assessing the product and start assessing the people behind it, looking for evidence of judgment.

They are conducting that assessment before they ever talk to you.

The sales conversation does not start from zero. It starts from whatever reputation you have already built.

He also argues that relatively few companies are publishing practical, original research on AI right now.

Most of what exists is either so hedged that it is useless or so optimistic that it is not credible.

In a few years, everyone will have a program, and standing out will cost far more.

So, if you are already publishing thought leadership or planning to, what does all this mean for you?

I have a list.

Of course I have a list.

First, audit your top pieces from last quarter and apply the swap test.

If a competitor could have published the same piece with its logo on it, you did not produce thought leadership. You just cranked out content.

Second, find your proprietary data.

This is where I think communicators sell themselves short.

You have more than you think: your own operational data, customer-service logs, field observations, and the ability to conduct surveys.

Survey your employees. Survey your customers. Survey the industry.

The Content Marketing Institute’s Jasmine Williams makes the case for treating thought leadership as a platform rather than a campaign.

One flagship study becomes the hub, and articles, webinars, sales enablement, and employee advocacy become the spokes.

That is a repeatable model.

Third, put the thesis in the first 60 seconds.

Not the context. Not the setup.

Put the claim in the first 60 seconds.

Fourth, reopen the gating conversation and reframe it.

It is no longer lead capture versus reach. It is lead capture versus being cited by the machines your buyers consult first.

Segment your library.

Some assets should stay gated because a download genuinely signals buying intent. But your flagship research probably should not be among them.

Fifth, take one asset you published this year and turn it into an experience: a roundtable, a working session, or a recorded session with someone who disagrees with you.

I think we call that a debate.

Sixth, change what you are measuring—and change it before someone asks you to defend it.

Measure speaking invitations. Measure journalists citing your framework. Measure analysts referencing your numbers. Measure employees sharing the work without being asked.

Denise Brosseau of the Thought Leadership Lab calls the underlying discipline “stick-to-itiveness”: the willingness to keep showing up long enough for any of that to accumulate.

Seventh, protect the executive’s actual voice.

This is the piece only we can do.

AI is a genuinely useful accelerant. It can turn an interview into an article, sharpen the structure, and catch the flabby paragraph.

What it cannot do is have a point of view.

If your CEO’s byline reads like a competent prompt response, you put your CEO’s and your company’s credibility at risk.

Let me end this segment with a little history.

The term “thought leader” is generally credited to a fellow named Joel Kurtzman, who was editing Strategy+Business back in 1994.

He meant something specific: someone addressing the questions senior executives were actually wrestling with.

More than 30 years later, the term has become a punchline.

It became one because we industrialized it. We turned a description of rare people into a content category with a production quota.

The research that came out this year is essentially the market telling us it can still tell the difference—and that it is willing to pay for the real thing.

That is not a bad position for communicators to be in, is it?

Now for another awkward transition to my final report.

I’m going to end with something a little lighter, although there is a real point buried in it.

Knowledge at Wharton wrote up a new research study published in the Journal of Consumer Psychology titled “Effectively Communicating Uncertainty: The Persuasive Impact of Different Types of Hedges.”

You know hedges: “That could work.” “That might be a good approach.”

We all hedge.

Yet most communication training treats hedging as a bug to be trained out of us.

Jonah Berger’s research team ran seven studies and split hedging into two dimensions.

The first is likelihood.

Is your hedge low-probability—“might,” “could,” or “it feels like”—or higher-probability—“likely,” “should,” or “arguably”?

The second is perspective.

Is the hedge floating free, as in, “It sounds like”? Or is it attached to a human being, as in, “In my opinion,” or “It sounds likely to me”?

All seven studies agreed: Higher-likelihood hedges and personal-perspective hedges are more persuasive because they make the speaker seem more confident.

Berger’s example is a mechanic saying, “The repair might work,” versus, “I believe this repair will solve the problem.”

Both statements convey uncertainty, but they have a completely different effect.

The personal version means someone is taking ownership.

Berger describes this as a communication sweet spot. You get the protection of not overclaiming without paying the credibility tax.

It is important to point out that the effect weakened when the communicator was a brand rather than a person, because confidence mattered less.

That is one more argument for having actual humans deliver your message.

I went looking for research that either supports or contradicts this, and it turns out the findings land right between two camps that do not agree.

On one side are decades of work on what is called powerless language: hedges, hesitations, and tag questions.

This research suggested that hedges may be the most damaging of all the powerless markers.

Researchers found something genuinely alarming: When a topic mattered to people, powerless markers did not merely make the speaker less appealing. They flattened the arguments.

Strong arguments performed no better than weak ones once the hedges were added.

On the other side are the uncertainty-communication researchers.

Researchers at the University of Cambridge’s Winton Centre have spent years studying how to convey uncertainty in facts and numbers.

Another study involving more than 10,000 participants found that putting a numeric uncertainty range around COVID statistics slightly reduced trust in the number itself but had no effect whatsoever on trust in the source.

Being candid cost the communicator nothing.

A meta-analysis published this year finds the overall effect small and highly dependent on how uncertainty is expressed, with verbal hedges doing more damage than numbers.

Put all of this together, and here is my take: The problem was never uncertainty. It is vagueness.

Saying, “Here is what we know, here is what we do not know, and here is what would change my mind,” reads as confidence.

Mumbling, “It could go sort of either way,” reads as evasion.

It is the same actual state of knowledge, but the way it is expressed produces the opposite effect.

I would argue that this matters more for us now than it has in years, because the machines drafting our first drafts hedge constantly—and they hedge in the weak way: low likelihood, no perspective, and nobody’s name attached.

You may have noticed that Neville and I hedge our way through every episode of this podcast.

We say things like, “This is correlation, and correlation is not causation.” We say that you should treat a figure as directional.

We note, as I did just a few minutes ago, that a survey being cited is four years old.

It turns out that hedging might have been the right call.

Or let me try that again.

In my view, that was almost certainly the right call.

I would love to tell you when the next monthly episode will be, but Neville and I have not settled on that yet.

Nor do I know when he will be up for recording a short midweek episode.

I may do one solo. We’ll see how it goes. We’ll see what kind of news or research crosses the transom.

Until I get answers to all those questions, that will be a 30 for this episode of For Immediate Release.

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