They cover the llms.txt hype cycle and why it never actually mattered, the sloppy habits that have made webpages worse instead of better, and why AI Overviews trust has followed its own bell curve as users start questioning what gets surfaced. They also dig into commodity versus non-commodity content and how personalization means writing for actual customers instead of an algorithm. The episode closes on scaled content abuse: why high-volume, low-quality publishing feels productive right up until it tanks performance.

Chapters:

0:53 Google I/O and the AEO/GEO chatter

2:58 Reading Google's actual guidance on AEO and GEO

9:14 llms.txt vs. robots.txt

12:52 Content chunking and structured data overreach

15:04 AI Overviews and the trust bell curve

17:16 Writing for the algorithm vs. writing for humans

20:28 Commodity vs. non-commodity content

27:29 Tables, listicles, and knowing when to stop

30:34 Scaled content abuse

32:46 Alphabet soup, Alphabet the company, and wrap-up

Subscribe for more marketing and AI strategy conversations from Xponent21, and drop your biggest AI search question in the comments.

Peter sells office printers and managed IT for Cobb Technologies, and he's been in sales for 40 years. None of that is what people bring up when they meet him: they tend to bring up the Segway. He walks through the night it started – an unsold auction item at a Sterling charity gala, a nervous event organizer, and a two-minute crash course in balance before he rode it onto a dance floor with a microphone in hand. That one bid became a video format he's repeated since: ride in, spin, deliver a short business message, spin out.

The conversation with Peter gets into what actually made the format work past the novelty. Peter learned early that the Segway gets attention, but the content has to hold it, and he talks through how he tracks success now — not just views, but how often a prospect brings the video up before he does, because that's the icebreaker that gets him in the door. Chuck and Kayleigh push him on whether any of it shows up in revenue, and Peter answers directly: being known for something different is what brings the business in.

There's also an honest stretch on what it takes to get in front of a camera in the first place. Peter describes himself as an ambivert, walks through the self-image work that took him over a decade to get past, and lands on a phrase he's coined for it: being "friends with the lens." Kayleigh connects it to her own experience building out video for Richmond Water and Channel RVA, and the two trade notes on why reps matter more than talent when you're starting out.

Peter closes out the episode by previewing what's next — an electric unicycle series called "Sales Lessons on Electric Unicycle," built from a bank of 60 topics he's already written for sales reps and managers who want to be remembered in a crowded market.

Chapters

3:16 — Who Peter Larson is and what he does at Cobb Technologies
4:38 — The charity auction that started it all
7:48 — Turning a one-off stunt into a video series
8:39 — Getting through the early days of nobody watching
9:49 — The Segway is the vehicle, not the message
15:03 — Measuring success beyond likes and views
17:06 — Why the Segway works as an icebreaker
18:01 — The Cobb Technologies connection
20:48 — What Peter wishes he knew when he started
21:36 — The video that almost ended in a brick wall
24:39 — Being an ambivert on camera
25:29 — Working through self-image to get comfortable on screen
27:39 — Getting reps: the fastest way past camera fear
36:00 — Why video builds trust faster than text

Connect with Peter Larson: https://www.linkedin.com/in/peterlarsenjr/

Cobb Technologies: https://www.cobbtechnologies.com/

About Channel RVA Channel RVA is Richmond's independent media network, built by people who live and work in the 804, telling the stories the bigger networks skip. Subscribe for more from To The Power of X and the rest of the original lineup.

#Marketing Podcast #ChannelRVA #RichmondVA

AI search is more conversational than a Google search bar ever was. That demands more human work, not less. Keyword tracking still has a place: treat it like a topic map, then expand into the actual questions people ask, the language they use, and the problems they're trying to solve. Jenna explains why the team is anti-slop, why LLMs are predictive text that still need a human doing the reasoning, and what "good farmer SEO" means: going back through existing content and making it clearer, fresher, and easier for both AI and people to use.

Brand reputation feeds AI visibility too. Customers complaining on Reddit or third-party sites shows up in what AI surfaces. That's why digital PR and outside validation matter, and why a single "AI visibility score" can hide what's actually happening across ChatGPT, Gemini, Claude, and the rest. If you want reporting you can explain to a client without jargon, and a strategy built on outcomes instead of vibes, this episode's for you.

In this episode:

• Why keyword tracking is a starting point, not the whole strategy
• Turning keywords into a topic map instead of a checklist
• Why AI SEO takes more human involvement, not less
• What "good farmer SEO" actually means
• Topic authority vs. just publishing a lot of content
• Being anti-slop: why LLMs can't reason and still need a human
• How Reddit reviews and public sentiment shape AI visibility
• Why digital PR and third-party validation matter for AI search
• The problem with a single "AI visibility score"
• How to explain AI SEO to clients without the jargon

Chapters 

0:34 The AI search disconnect: when rankings lie 

1:46 Why AI search is more conversational 

3:31 Keywords as a topic map, not the whole strategy 

5:57 Why AI SEO needs more human work, not less 

7:08 Talking to clients & semantic search 

8:53 What is "good farmer SEO"? 

9:39 Topic authority vs. just having content 

11:00 Why we're anti-slop: LLMs can't reason 

13:41 Reddit reviews & brand reputation 

14:50 Case study: pricing pages & qualified buyers 

16:23 How Reddit shapes your AI visibility 

17:53 Digital PR & third-party validation 

19:11 The problem with one "AI visibility score" 

21:26 How to explain AI SEO to clients (no jargon)

Happy searching.

In this episode, Chuck, Kayleigh, and Jenna get into what it actually looks like when AI stops being a tool and starts being a stand-in for judgment. That includes what AI hallucinations really are (and why the confident tone is the dangerous part), how a language model actually processes a question before answering it, why AI-generated content is getting easier for both people and Google to spot, and where AI is genuinely useful versus where it's quietly making you worse at your job.

If you touch ChatGPT, Claude, or Gemini at work in any capacity, this one's for you.

01:06 How you're using ChatGPT wrong 

03:24 The red flags of AI replacing strategic thinking 

05:08 When AI is actually a legitimate sounding board 

05:51 The "yes-man" problem of AI

07:35 What an AI hallucination actually is 

12:03 How AI actually answers your question (shadow prompting) 

14:24 Learning "strategy" from internet slop 

16:13 Why people can tell when content is AI-written 

18:18 Slop, sameness, and standing out 

19:21 The competitive risk of pure AI content 

19:45 Google is starting to stop indexing AI content 

Want a marketing team that knows when to use AI and when not to? 

Book a discovery call: https://xponent21.com/contact

That's the problem Chuck, Kayleigh, and Will sit down to unpack in this episode of To the Power of X, and it's a problem that's been sitting in the background of almost every client conversation Xponent21 has had over the last year. 

When a client asks "what is AI saying about my business?" there isn't a clean answer. Not because the question is wrong, but because the tools built to answer it aren't built on real research infrastructure. They're built on shallow signal, marked up to enterprise prices, and handed to marketers who are expected to tell a story with it.

So Xponent21 built something else. CARL — the Cognitive AI Ranking Lab — started as internal software to help the team move faster and with more precision. It's since grown into something bigger: a research lab that prompts large language models across hundreds of topics and industries, captures those responses as data points, and analyzes how AI-assembled answers change over time as the models evolve.

In this episode, Will Melton breaks down what CARL actually is, why we built it, and what the early research is starting to show about how LLMs construct answers — including things like shadow prompts, compute thresholds, and the personalization layer that makes "what is AI saying about me" a genuinely hard question to answer at scale. 

Kayleigh brings the client-side perspective: what it's actually like to navigate those conversations without good data, and why the variation in LLM outputs is only going to get more pronounced as the models get more user-aware.

If you work in marketing, run an agency, or you're trying to figure out what your AI visibility spend is actually buying you, this one's worth your time.

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CARL Labs: https://carlaio.com/

Xponent21: https://xponent21.com/