Ever notice how one email subject line makes you click without thinking, while another one just sits there? That’s not an accident, and it’s not really about logic. Marketing consultant and author Nancy Harhut calls it “innate intelligence,” the hardwired, gut-level reactions that decide almost everything before our rational brain even shows up to the meeting.
I recently listened to a great podcast breakdown of Harhut’s work and ended up buying her book, Using Behavioral Science in Marketing. I love this stuff so much I want to share the highlights, because once you see these patterns, you can’t stop noticing them in your own inbox.
It’s not about how we think, it’s about how we feel
I’ll admit it, I’ve been on the receiving end of this one myself.
Earlier this year I was staring down a future I hadn’t lived yet… a slow season, a client retiring, income drying up… and instead of sitting with that fear, I wanted it gone. Fast. So when an ad for the Play Makers AI course showed up promising a quick fix, I bought in on the spot. I never even got the course offerings in writing before I handed over an embarrassing large chunk of money. That’s not rational buying. That’s fear buying. I wasn’t evaluating a product, I was trying to make an anticipated future feel less scary right now.
Harhut would say I fell for exactly what she teaches: people don’t buy features, they buy relief from a feeling. A financial software company once described its own product as “the antacid for a diet of tough decisions” instead of listing what it actually does. That line sells a feeling, not a feature, and so did that course ad. It worked on me.
Two other patterns fall under this same idea:
Loss aversion. We feel the sting of losing something roughly twice as strongly as we feel the pleasure of gaining it. That’s why “order today or pay more tomorrow” works better than “save money if you order today.”
The endowment effect. Once we feel like something is ours, even something as small as unused account credit or a spot in a drawing, we don’t want to let it go. “Your credit expires tomorrow” pulls harder than “here’s a discount.”
Our choices are less rational and more reactional
We react first and rationalize second. A few tactics that lean into this:
Autonomy bias. Give people a choice, even a small one like “what’s your preference,” and they’re more likely to act, simply because choosing feels like being in control.
Labeling. The words you use to describe someone can shape how they behave. Calling someone “the kind of person who helps” nudges them toward acting like one.
We rely on auditory cues
This one surprised me. Phrases that rhyme, alliterate, or just sound good tend to feel more true and more persuasive, even when they carry the exact same information as a plainer version. A “reason why,” even a flimsy one, also makes people more likely to comply with a request. Simply adding “because” works.
Why this matters for your marketing
None of this is about manipulation. It’s about writing copy that matches how people’s brains actually work instead of how we wish they worked. If your email open rates or ad clicks feel stuck, the fix might not be a bigger discount. It might be a rewrite that leans on feeling over features, offers a small choice, or simply explains the reason why.
If you want to go deeper, Nancy Harhut’s book Using Behavioral Science in Marketing is worth the read, and you can find more of her work at HBT Marketing.
Want help auditing your own emails or landing pages through this lens? Let’s talk.
Why does your writing sound like AI, even after you’ve edited it? A handful of specific words, sentence patterns, and punctuation habits, sometimes called “AI-isms,” are doing most of the damage. None of them are wrong on their own. Together, they’re the reason a piece of copy can be grammatically perfect and still feel like nobody actually wrote it.
I ran into this question the useful way: by accident, in my car, half listening to a marketing podcast between client meetings. The host kept saying “AI-isms” like everyone already knew what that meant. I didn’t. So I pulled into the shadiest parking lot I could find, because (as usual) it was 100-plus degrees and Texas summer turns shade into a competitive sport, and opened the “talk live” voice feature in the Gemini app to ask.
What came back was specific enough that I want to share it with you. If you use AI to help draft content for your business (or personal) writing these are the tells worth knowing, so your content stops sounding like AI wrote it and starts sounding like you.
The words that make writing sound like AI
Ask any AI to write with authority and it reaches for the same short list of words: delve, tapestry, testament, furthermore. None of these are incorrect. They’re just overused, to the point that trained readers (and, increasingly, untrained ones) clock them as synthetic on sight.
The paragraph structure is a tell too. AI-written content loves to close every section with a tidy summary sentence, the written equivalent of a bow on a box. One or two of those is fine. Five in a row and your reader starts skimming. This isn’t just a you problem either, outlets like Yahoo Tech and academic-writing tools like Paperpal have written their own guides on it recently.
AI punctuation tells
This is the part of the conversation I found super interesting and useful. #nerdalert Beyond word choice, AI leans hard on:
M-dashes, to connect two clauses that could just as easily be two sentences
Colons, to introduce lists and explanations, sometimes even when a list isn’t needed
Semicolons, to chain multiple ideas into one long, winding sentence
I’ll admit the whole punctuation discussion makes me a little defensive. I’m a bedazzle lovin’ Texan girl who slept through high school English…I use exclamation points and ellipses like they’re going out of style!!! (And that’s just how I talk, y’all, not a sign an AI wrote it.)
The point isn’t that any single punctuation mark is a red flag. It’s the pattern of the same handful of marks showing up over and over until it starts to feel formulaic instead of human.
How to fix writing that sounds like AI
None of this requires a rewrite from scratch. It requires a pass aimed at exactly why your writing sounds like AI in the first place:
Swap the buzzwords for plain language. If a sentence would sound strange coming out of your mouth in conversation, rewrite it until it wouldn’t.
Vary your sentence length on purpose. Short sentence. Then a longer one that gives the reader room to breathe. That contrast is most of what makes writing feel human.
Read it out loud. Your ear catches stiffness your eye will skim right past.
Watch your punctuation patterns, not just your vocabulary. If every third sentence has a colon or a semicolon in it, that’s worth a second look.
Teaching your AI tool your actual voice
Here’s the part that matters most if you’re using AI to assist you with writing. You can, and should, teach your AI tool what your brand sounds like instead of editing every draft after the fact.
The method:
Pull three to five pieces of your best past content, the ones that actually sound like you.
Label each one, and add a line about why it’s a good example (the tone, the structure, the audience it’s for).
Include a few different formats (a social post, an email, a blog paragraph), so the AI sees your voice adapt across contexts instead of learning one narrow pattern.
Save all of it as a single reference document, with a section specifically on sentence rhythm: how long your sentences tend to run, where you get punchy, where you let a thought stretch out.
That document goes in front of the AI before it drafts anything of yours, which is exactly why the output stops sounding like AI in the first place. It’s the difference between technically correct copy and copy that actually sounds like the person whose name is on it.
The irony isn’t lost on me
I wrote this post about AI-isms with an AI’s help, then edited it looking for the exact tells I just described. That’s not a contradiction, it’s the whole point. AI is a genuinely useful drafting tool. It is not a substitute for a human pass before anything goes out under your name sounding like AI wrote it.
If you’re building content for your business and want a brand voice document that actually keeps your AI tools in line, that’s exactly the kind of groundwork worth doing once and reusing for years. Reach out if you’d like help putting one together.
TL;DR: The LinkedIn algorithm 2026 update rewards relevance over volume. Personal profiles are outperforming company pages, saves matter more than likes, and comments on other people’s posts do more for your visibility than your own posts do. Scheduling tools like Hootsuite don’t hurt your reach, but “publish and ghost” behavior does. The real growth lever is showing up consistently in other people’s feeds, not just your own.
If your LinkedIn reach has felt like it’s dropped off a cliff lately, you’re not imagining it, and you’re not alone. The LinkedIn algorithm 2026 overhaul was real, and most of the advice still circulating online is built for the platform as it existed two or three years ago.
The good news: the businesses and nonprofits winning on LinkedIn right now aren’t the ones posting the most. They’re the ones posting smart, and engaging even smarter. Here’s what’s actually working right now, broken into the three things that matter most: what to post, how to schedule it, and how to show up in other people’s feeds.
Why the LinkedIn Algorithm 2026 Update Changed Your Reach
LinkedIn’s feed no longer rewards volume or follower count the way it used to. Instead, it’s reading your content and asking whether it’s genuinely relevant to the specific people seeing it. A few shifts worth knowing:
Personal profiles now dramatically outperform company pages. If you’ve been pouring effort into a business page and wondering why nothing sticks, that’s likely why. People trust people, not logos, and organic reach on company pages has shrunk to a sliver of what it was a few years ago.
Saves matter more than likes. A “save” tells LinkedIn someone found your content worth revisiting, and it’s a far stronger signal than a quick reaction. If you want your content to travel, make it save-worthy: frameworks, checklists, and step-by-step guides people want to keep handy.
Relevance is beating recency. A post that’s genuinely useful can keep gaining traction for weeks, not just the first 24 hours. That rewards timeless, practical content over reactive hot takes.
What to Actually Post on LinkedIn
Lean into carousels, documents, and native video. These formats are pulling significantly more engagement than plain text or single images right now. If you have a blog post, a case study, or a set of tips (like this one!), turning it into a swipeable carousel is one of the highest-leverage moves you can make.
Keep the hook tight. You’ve got roughly the first 200 characters before someone has to click “see more.” Spend real time on that opening line, it’s doing more work than the rest of the post combined.
Don’t bury a link in the post body. LinkedIn wants to keep people on the platform, and posts with outbound links get noticeably less distribution. If you need to share a link, drop it in the first comment instead.
Skip the engagement bait. “Agree?” posts, hashtag stuffing, and generic “thoughts?” calls-to-action are actively being deprioritized. What works instead is a genuine, specific question tied to the value you just delivered, something that invites a real answer, not a reflexive comment.
Show up as a person, not a brand. Authentic stories, lessons learned, and behind-the-scenes moments consistently outperform polished corporate messaging. Formulaic, obviously AI-generated posts are seeing a measurable reach penalty, so if you’re using AI to help draft, make sure the final version sounds like you.
The Scheduler Myth, Debunked
Here’s one that trips up a lot of small business owners: no, scheduling your posts through a tool like Hootsuite, Buffer, or Later does not hurt your reach. That claim gets repeated constantly in marketing circles, but under the current LinkedIn algorithm 2026 rules, it’s not backed by evidence. Posts published through official, approved scheduling tools are treated identically to posts published manually, LinkedIn’s ranking system doesn’t know or care which button you pressed.
What does hurt your reach is the behavior schedulers can encourage: batch-scheduling a week of content on Monday morning and then never logging back in. LinkedIn’s algorithm rewards accounts that stay active, replying to comments, engaging with others, and quietly penalizes accounts that “publish and ghost.” So schedule freely, but build in time to actually show up after each post goes live.
One practical note: LinkedIn’s own native scheduler is free and reliable, but it doesn’t support carousels, polls, or newsletters. If those formats are part of your strategy, you’ll need a third-party tool for that piece regardless.
Where the Real Growth Happens: Engagement
Here’s the part most people skip, and it’s honestly more powerful than posting itself.
When you publish your own post, LinkedIn typically shows it to only a small slice of your network right out of the gate. But when you leave a thoughtful comment on someone else’s post, especially someone with an engaged following, your name, headline, and photo get exposed to their audience. Comments are, functionally, free advertising in front of a warm crowd.
A few rules for doing this well:
Quality over quantity, always. Ten genuinely thoughtful comments a day will do more for you than fifty generic ones, and generic, low-effort commenting can actually work against you now that LinkedIn can detect it.
End your comment with a real question. Not “thoughts?”, something specific tied to what you just said. It practically guarantees a reply, and that back-and-forth is exactly the kind of signal that gets rewarded.
Comment on the right people, deliberately. Build a short list of who you actually want to be visible to, industry peers, prospective clients, local community leaders, and make a habit of engaging with them consistently, rather than scrolling and reacting at random.
Reply fast after you post. The first hour after you publish is the highest-leverage window you have. Replying to every comment in that window measurably increases both total comments and views on the post.
Reshare with intention. A plain repost (no added commentary) is a clean way to amplify someone else’s good work without competing with it for attention. If you want to add your own commentary, make sure it’s substantial, thin commentary tends to underperform a plain repost.
Save what’s genuinely useful. Saving isn’t just a bookmarking habit for you, it’s a signal that shapes what LinkedIn shows you going forward, and it’s worth being intentional about what you save rather than defaulting to “like.”
A Realistic Weekly Rhythm
You don’t need to live on LinkedIn to make this work. A sustainable cadence looks something like:
A few times a week, post original content, a mix of carousels, quick tips, and the occasional personal story.
Daily (even just 10 to 15 minutes), comment thoughtfully on 5 to 10 posts from people you’ve deliberately chosen to engage with.
After every post you publish, spend the first hour replying to comments instead of walking away.
Weekly, reshare one or two posts worth amplifying, and save anything genuinely valuable for your own reference.
Key Takeaway
The LinkedIn algorithm 2026 update rewards the same thing good networking always has: showing up consistently, adding real value, and actually paying attention to other people, just with a system now built to notice when you do.
If you want help building out a content and engagement plan that fits your schedule (not an unrealistic “post every day” plan you’ll abandon by week three), that’s exactly the kind of strategy work we do at Strategic Online Solutions. Get in touch and let’s build something sustainable.
Data Centers in Texas: What’s True, What’s Not, and What’s at Stake
If you’ve scrolled through local news lately, you’ve probably noticed something strange. A year ago, most politicians in Texas were bragging about data center investment coming to their district. Now some of those same politicians are running against it. Zoning meetings that used to be sleepy are packed. National polls show opposition to data centers has jumped from about half of Americans to roughly three out of four in just twelve months.
At the same time, you’ve probably also heard the other side: these projects bring jobs, tax revenue, and investment that most towns would kill for.
So which is it? Are data centers going to drain our water, spike our power bills, and get built behind closed doors with no say from the people who live next to them? Or are they an economic opportunity North Texas would be foolish to turn away?
The honest answer is: both concerns are partly true, a lot of what’s circulating is flat-out wrong, and the real story is more interesting (and more fixable) than either side’s talking points suggest. This piece pulls together a recent local panel discussion held right here in Fort Worth, featuring a UT Arlington computer science professor, a TCU energy expert, and a longtime energy journalist, along with national reporting and polling on the broader backlash. The goal is simple: give you a clear, factual picture you can actually use.
First, What Is a Data Center? (Hint…you already use them daily)
Before we get into the arguments, let’s clear something up: most people who have opinions about data centers couldn’t tell you what actually happens inside one.
Here’s the plain-English version. A data center is a building full of computers, similar in concept to the laptop or phone in your hand, just on an industrial scale. Power comes in, usually from the electric grid or from an on-site power source. That power runs racks of servers and chips. Those servers heat up fast, so cooling systems pull the heat back out. And the data itself, meaning your search results, your Netflix stream, your work email, your car’s navigation, flows out the other end.
Here’s the part most people miss: data centers aren’t just for AI.
They’re the physical machinery behind basically everything digital you already rely on. Every time you say “Hey Siri,” or “Hey Alexa,” stream a show, check your bank balance online, or watch a doorbell camera notification pop up on your phone, a data center somewhere is doing the work. AI has added a new, much more power-hungry category of computing to that mix, but the buildings themselves have been quietly running the internet for two decades.
A lot of the current backlash comes from a simple realization: people assumed “the cloud” was some weightless, abstract thing. It isn’t. It’s real buildings, using real water and real electricity, usually built close to where people live.
Why North Texas Became Ground Zero
Texas isn’t an accident in this story. According to real estate research firm JLL, Texas has more data center capacity built or under construction than any state except possibly Virginia, more than double what’s happening in Virginia by some counts. North Texas specifically is already one of the top three or four data center markets in the country, and that was true even before the recent AI boom.
Land. Texas has a lot of it, and it’s easier to build here than in dense coastal markets.
Power know-how. Decades of oil and gas experience means Texas has real expertise in building and delivering energy infrastructure fast.
A flexible power grid. Unlike much of the country, Texas runs its own grid (ERCOT) as a competitive, energy-only market. That structure makes it easier and often cheaper to get new power projects built here than in most other states.
Capital and entrepreneurs. Texas has investors and developers who know how to move quickly on big infrastructure projects.
None of that means every project is a good one. But it explains why this conversation is happening here first, and loudest.
The Good: What Data Centers Can Actually Deliver
Set the noise aside for a moment and look at what’s actually happened in towns that got this right.
Quincy, Washington started attracting data centers back in 2006. It’s a farming town of about 8,500 people. Today it hosts 30 data centers, supporting roughly 900 direct jobs plus several more local jobs for every one of those. Data centers now fund more than half of the town’s property tax base, which has paid for a new pool, a $120 million high school, and a sports complex. The town’s poverty rate dropped from nearly 30% in 2012 to about 6% in 2024.
Loudoun County, Virginia, home to the world’s densest cluster of data centers, tells a similar story at a bigger scale. Data centers sit on about 3% of the county’s land but generate around 40% of its budget. The county has cut property taxes every year for a decade, down to some of the lowest rates in the region, while still outspending its neighbors on schools and public safety.
These aren’t cherry-picked press releases. They’re documented outcomes: lower taxes, better-funded schools, and real jobs, not just during construction but ongoing technical and engineering work.
Closer to home, this same pattern is starting to show up. West Texas has a Microsoft-backed project expected to reach nearly 2.7 gigawatts, using brackish (non-drinkable) water instead of freshwater, and Microsoft has published detailed local tax and job figures for similar projects. A Riot Platforms campus in Corsicana is scaling toward a full gigawatt of capacity. These are the kinds of projects that, done right, function less like a factory moving in and more like a new, well-funded institution planting long-term roots in a community.
None of this happens by accident, though. It happens when a project is well-run, transparent about its plans, and genuinely invested in the community around it. Which brings us to the concerns worth taking seriously.
The Bad: Real Concerns, Not Imaginary Ones
It would be dishonest to wave away every worry as overblown. A few concerns are legitimate and deserve real answers, not just reassurance.
Water use, especially in summer.
This is where things get genuinely nuanced. Newer data centers increasingly use “closed loop” cooling, where water carries heat away from the servers, gets cooled, and gets pumped back in, over and over, instead of being used once and discarded. That’s a real improvement. But as Dr. Mohammad Islam, a UT Arlington computer scientist who studies this exact issue, pointed out at the Fort Worth panel: even closed-loop systems aren’t zero-water, and the water use that does happen tends to be heavily concentrated in the hottest summer months, not spread evenly across the year. A company can accurately say its facility uses “10 times less water annually” while still requesting a very large daily water allowance for July and August. Both things can be true at once, and that nuance rarely makes it into either the marketing or the outrage.
Grid strain and the pace of buildout.
Texas’ grid operator, ERCOT, is trying to plan for a wave of new demand while the actual size of that wave is genuinely unclear (more on that in the misinformation section below). Multiple panelists agreed the honest answer is that the grid physically cannot be built out as fast as demand is arriving, which means delays and growing pains are likely regardless of how well-intentioned any individual project is.
The “behind the meter” loophole.
Some data centers are built to generate their own power entirely, without connecting to the public grid. This can mean faster construction, but Dr. Islam raised a pointed concern: because current regulations and moratoriums are mostly written around grid-connected facilities, a project that stays off the grid could sidestep water and environmental rules altogether, including running on gas power with fewer restrictions. That’s a real regulatory gap, not a hypothetical one.
Secrecy and NDAs.
Nationally, a lot of data center negotiations with local governments have happened under non-disclosure agreements, which makes it hard for residents to know what they’re actually getting, or giving up. Microsoft recently announced it’s ending this practice with local governments, which is a meaningful signal, but it’s the exception industry-wide rather than the rule so far.
These concerns share a common thread: they’re not really about whether data centers are good or bad in the abstract. They’re about whether a specific project is well-designed, honestly disclosed, and held to real standards. That distinction matters enormously, and it’s exactly where most of the public conversation goes wrong.
The River of Misinformation: Sorting Fact From Fear
Here’s where things get messy, and where a little fact-checking goes a long way.
The water math is often wrong, sometimes by a lot.
A widely circulated book on AI’s environmental footprint miscalculated data center water use by a factor of 1,000. The error has been acknowledged, but the original, wildly inflated number is still being quoted as fact in plenty of arguments today. If someone cites a jaw-dropping water statistic, it’s worth double-checking the source before repeating it.
The “data centers are spiking your power bill” claim doesn’t hold up well under scrutiny.
When researchers actually compared where electricity prices are rising fastest against where data centers are being built, they didn’t find a strong statistical link. That doesn’t mean electricity costs are a non-issue. It means the impact depends heavily on how a specific project is built and who pays for the grid upgrades it needs, not on some universal rule that data centers always raise your bill.
The eye-popping “queue” numbers are mostly noise.
You’ll often hear that hundreds of gigawatts of data center projects are “in the pipeline” nationally or in Texas specifically. In reality, a lot of that number comes from the same projects being counted two or three times as developers apply in multiple places to hedge their bets. At the Fort Worth panel, veteran energy journalist Jennifer Warren estimated that the realistic buildout in Texas through 2030 is closer to 12 to 15 gigawatts, a small fraction of the inflated headline figures. Big numbers make for scary headlines, but they’re not the same as reality.
A lot of the anger isn’t really about data centers at all.
National polling found people are actually more comfortable living near a nuclear power plant than near a data center, even though nuclear plants carry real, well-documented safety and waste considerations that data centers simply don’t. That gap is a strong hint that the backlash isn’t purely a rational risk calculation. Much of it is a stand-in for broader frustrations: worry about AI replacing jobs, years of accumulated distrust toward big tech companies, and a general feeling that communities don’t get a say in what gets built around them. Those are legitimate feelings. They’re just a different conversation than “is this specific project safe.”
Media coverage skews negative on both data centers and the industries that came before them.
This was something the panelists compared to how oil and gas coverage has worked for decades: the bad actors and worst-case stories get the headlines, while the routine, well-run projects that quietly fund schools and employ locals don’t make the news at all. That doesn’t mean the bad stories are fake. It means they’re not the whole picture.
What Actually Works: Rules, Not Bans
Here’s the most encouraging finding in all of this, and it rarely makes headlines: most people don’t actually want data centers banned outright. They want them held to clear, enforceable standards.
Recent national polling found 57% of people prefer clear rules that allow responsible projects to move forward, compared to just 23% who want a blanket ban on new projects. When people were shown a hypothetical project that met real disclosure, cost, and public review requirements, most said they’d support it moving forward. And when asked whether companies should be required to pay for the grid upgrades their own facilities need, rather than passing that cost to everyone else’s electric bill, support jumped to nearly 80%.
That lines up closely with what came out of the Fort Worth panel. Instead of trying to rank companies as simply “good” or “bad,” the more useful approach is a checklist: who owns the project, how is it financed, what’s the plan for decommissioning it decades from now, what water and power commitments is it making, and is any of that information actually verifiable by an independent source, not just the company’s own press release.
Some developers are already moving this direction voluntarily. Meta has launched a billion-dollar fund for community initiatives near its sites. One of its Louisiana projects generated enough local tax revenue to fund $50,000 teacher bonuses. OpenAI paired a recent Ohio project with tens of millions in community grants and technical education credits. Microsoft has ended NDAs with local governments. These aren’t universal industry practices yet, but they show what “good operator” behavior looks like in practice, and they give communities and regulators something concrete to ask for from everyone else.
What You Can Do
If you want to form your own informed opinion instead of just absorbing whatever shows up in your feed, a few things help:
Research the specific project details, not the industry-wide headline. Ownership, financing, water source, and community commitments tell you far more than a scary or glowing national statistic.
Treat both extremes with skepticism. If a claim sounds like “this will cause a drought” or “this will definitely lower your taxes,” it’s probably oversimplified in one direction or the other.
Show up locally. Zoning meetings, panels like the one held recently in Fort Worth, and city council sessions are where the actual, project-specific decisions get made, not on social media.
Look and ask for independent verification, not just company-published numbers. Universities and independent researchers are increasingly stepping into this role, and that kind of nonpartisan information is exactly what’s been missing from the national conversation.
My Prediction on Data Centers in Texas
Data centers aren’t going away, and pretending North Texas can simply opt out of this economic shift isn’t realistic, or necessarily in our own best interest. The opportunity here is genuinely large: good-paying jobs, major tax revenue, and long-term infrastructure investment, the kind of thing that funded new schools and lower taxes in towns that got it right.
But “getting it right” is the whole game. The difference between a data center that strengthens a community for decades and one that just leaves behind a substation and a higher electric bill comes down to a few unglamorous things: real transparency, enforceable community commitments, and a willingness from all of us to ask sharper questions than the headlines are asking.
North Texas has the land, the power expertise, and the economic momentum to do this well. Whether we actually do comes down to demanding good rules now, not blanket bans, and not blind acceptance either.
P.S. Let’s not forget China
Chinese AI labs (Kimi K3, DeepSeek, and Qwen) are closing the gap with America’s best models shockingly fast this year. What analysts used to call a six to eight month U.S. lead has shrunk to something closer to weeks. Frontier AI runs on compute, and compute runs on data centers. Every project we slow down here doesn’t just cost North Texas jobs and tax revenue, it’s ground we’re not covering in a race that has real national security stakes: whose AI systems the rest of the world ends up building on, and whose rules get baked into them.
I guarantee youChina is not waiting around for us to sort out our zoning meetings.
Wouldn’t you rather see this capacity built here, and built responsibly, than watch it get built somewhere else, or by someone else?
Sources: National reporting, polling, and industry data referenced in this piece draw on “Why Everyone Suddenly Hates Data Centers,” The AI Daily Brief, August 21, 2026 (also available via the AI Daily Brief episode page), along with the Fort Worth panel discussion linked above. And my personal opinion.
Client: Lynn Busch — an established Marriage and Family Therapist in Fort Worth, now rebranding as a marriage coach and launching a new flagship service: 2–3 day intensives for high-achieving couples
What we did: A full competitive landscape review of three direct competitors, followed by a SWOT analysis, before writing a single word of launch marketing
What we found: The “compressed intensive” format isn’t blue ocean — one competitor already owns it. But the combination Lynn can bring to it — deep clinical tenure, a blunt no-fluff voice, an explicit high-achiever niche, and a gender-inclusive audience — is a combination no competitor currently holds
What changed: We now have a positioning statement and a ranked list of whitespace opportunities to build the intensive launch campaign around, instead of guessing at what makes Lynn different
Why We Conducted Competitor Research….Before Writing A Word Of Marketing Copy
Lynn Busch has been my client for years. She recently made the decision to shift her practice — moving from traditional marriage counseling into marriage coaching, and building a new flagship offer around it: 2–3 day intensives for “marriages in crisis” who want fast, effective results instead of months of weekly sessions.
We didn’t start with marketing copy. We started with an AI visibility audit and got her foundation in shape so she could actually be found. Then we built out what the intensives would include. Only after that did we sit down and ask the question that should come before you write a single headline: who else is already selling something like this, and where does Lynn actually stand apart?
That’s what a competitive analysis and SWOT analysis are for. A SWOT — Strengths, Weaknesses, Opportunities, Threats — is a standard business-strategy framework for mapping what’s true internally (strengths and weaknesses) against what’s true in the outside market (opportunities and threats). It’s not a formality here — it’s a filter. It tells you which claims are actually true, which ones are already taken, and which ones are sitting there unclaimed.
What We Looked At
We identified three direct competitors in the marriage coaching space and compared them across the dimensions that actually matter to a prospect trying to choose between them: practitioner credentials, target niche, delivery format, pricing transparency, content and acquisition strategy, brand voice, scale claims, and — critically — whether they already offer anything like a multi-day intensive.
The short version of what we found across the category as a whole:
Every competitor leads with founder story and testimonials as their primary proof — none publish independently verified outcome data.
Every competitor claims national or remote reach — geography isn’t a differentiator for anyone anymore.
Nobody has a visible technology layer — no assessments, dashboards, or structured between-session tools on any competitor’s site.
One competitor already offers a multi-day intensive format, and markets it explicitly to “busy executives.” So the format itself isn’t the differentiator — how Lynn frames it has to be.
That last point mattered the most. It would have been easy to market “the intensive” as a novel idea. It isn’t. The real opportunity was somewhere else.
Where the Real Whitespace Is
Once we mapped all three competitors against each other, a pattern showed up: each competitor’s biggest strength is also, structurally, its biggest limitation.
Competitor A has built a genuine media business around a high-achiever niche — but restricts it to men only, and two of three of their practitioners aren’t licensed clinicians.
Competitor B has the deepest tenure and the largest scale claim in the category — but no niche at all, and a delivery format (phone-only, no video) that reads as dated next to everyone else.
Competitor C has a large staff and full pricing transparency — but is a generalist clinic with no specific niche, competing on breadth instead of focus.
None of these are accidents or oversights. Each business made a deliberate trade-off to build the brand it has. That’s exactly why the gap is stable — closing it would mean a competitor tearing down years of their own brand equity. Which means the opening isn’t going anywhere.
That opening is this: nobody in the category combines deep clinical tenure, a blunt/tactical voice, an explicit high-achiever niche, and a gender-inclusive audience. Lynn can hold all four at once. No one else reviewed can.
We ranked the resulting opportunities by strategic value, from strongest to weakest:
The tenure + tactical voice + high-achiever niche + gender-inclusive combination — the strongest, least contestable position available
Executive intensives, positioned on niche rather than format novelty — real demand, and most of the category still doesn’t compete here
The dual-career executive couple segment — no competitor reviewed markets to this group directly, and it’s a growing one
AI-native discoverability — none of the three show any content built for AI answer engines to cite, which is a compounding advantage the earlier you claim it
A hybrid or async engagement model — real whitespace, but the easiest for a competitor to copy once someone proves it works, so it’s the weakest of the four on its own
The Positioning Statement That Came Out of It
Every piece of this fed into a single sentence we can now build the entire intensive launch around:
For high-achieving professionals whose marriages are in crisis and want quick and effective results instead of open-ended therapy. Lynn Busch is a marriage coach who has seen it all with 40+ years of clinical training and practice behind her, she offers straight-forward, tactical strategy without the restrictions, the funnel-driven marketing, or the one-size-fits-all approach that limit today’s leading competitors.
That statement only stakes a claim on ground nobody else in the category currently holds. It doesn’t compete on scale, on price transparency, or on being first to market with a multi-day format — because we already confirmed none of those are winnable claims for Lynn to make. It competes on the one combination that is winnable.
What’s Next for Lynn’s Marketing?
With the competitive landscape mapped and the positioning locked, the next phase is building the actual launch campaign for the intensives — messaging, page content, and outreach built directly around this positioning statement, instead of marketing copy written in a vacuum and hoping it lands.
If you’re about to launch a new offer and haven’t yet asked who else is already selling something like it, that’s the step to take before you write a single word of marketing copy. It’s a lot cheaper to find out where you actually stand out before launch than after.
If you’ve heard someone say “SEO is dead” this year, you’re not alone — and it’s not true. But it is changing, and if you own a business or run your own practice, it’s worth understanding what’s actually shifting, because it affects whether new customers can find you.
Yoast, the company behind the world’s most popular WordPress SEO plugin, recently surveyed 59 SEO professionals — from people just starting out to veterans with 10+ years of experience — and published their findings in The Yoast Perspective 2026, with commentary from their Principal SEOs, Carolyn Shelby and Alex Moss. I read through the full report, and here’s what I think matters most for business owners who don’t live and breathe SEO.
First, the reassuring part:
Only 10% of the SEO professionals surveyed think the field is “declining”
Just over half (51%) said it’s “evolving”
A third said it’s “thriving”
So no, you don’t need to abandon your website or your marketing plan. The fundamentals — good content, a fast and well-built site, and being seen as a trustworthy source — still work. What’s changed is where people are searching and what counts as success.
The big shift in SEO: it’s not just Google anymore
For years, “SEO” meant one thing: get your website to show up in Google search results. That’s still important, but people are also finding businesses through AI chatbots like ChatGPT and Claude, through YouTube and TikTok, and through AI-generated summaries that appear right at the top of a Google search (often called “AI Overviews”).
Here’s a quick translation of a few terms you’ll start seeing more often:
LLM (Large Language Model): the technology behind AI chatbots like ChatGPT. When someone asks ChatGPT “who’s a good marketing consultant in Fort Worth,” an LLM is generating that answer.
AI Overview: the AI-written summary Google now shows at the top of many search results, often before any actual website links.
Citation: when an AI tool mentions your business by name as part of its answer — this is quickly becoming as valuable as ranking #1 in Google used to be.
EEAT: short for Experience, Expertise, Authoritativeness, and Trustworthiness. It’s Google’s (and increasingly AI’s) way of judging whether content is written by someone who actually knows what they’re talking about.
Why your website traffic might be down but that’s not necessarily bad news
One of the more interesting findings: while a lot of businesses are seeing fewer clicks to their website, the traffic they do get is converting better. Why? Because AI tools and search summaries are now answering simple questions directly, so the person who still clicks through to a website is usually further along and more serious about buying or hiring.
The lesson here isn’t “traffic doesn’t matter.” It’s that traffic alone is an incomplete picture. If your website visits are down but your phone is still ringing and your inquiry form still has submissions, you’re probably fine. The old habit of judging your marketing purely by pageviews needs an update.
What should you actually track instead?
The SEO professionals surveyed pointed to a few newer things worth watching alongside your regular website traffic:
Are people searching for your business by name? (Called “branded search” — it’s a sign people already know and trust you.)
Are you showing up when someone asks an AI chatbot a relevant question? You can test this yourself — ask ChatGPT or Gemini a question a potential customer might ask, and see if your business comes up.
Are you getting mentioned by other reputable sites, publications, or directories? These mentions increasingly matter more than traditional backlinks did.
Google rankings still matter — they’re just not the whole story anymore
Just over half of the SEO professionals surveyed said rankings are about as important as they’ve always been. The report’s take, which I agree with: rankings are still a useful signal that your site is doing something right, but they’re a means to an end, not the goal itself. Being ranked on page one and being chosen as the answer an AI gives someone are two different (if related) things.
A simple starting checklist
If you want to get a head start on all this without needing to become an SEO expert:
Check how your business shows up in AI tools. Ask ChatGPT, or any LLM, a question your ideal customer might ask, and see what comes back.
Make sure your “About” page and author bios are specific and credible. Vague bios don’t build trust — specific credentials and real experience do.
Keep publishing genuinely helpful content. Not just content for content’s sake. AI tools are noticeably less forgiving of filler than Google used to be.
Don’t panic over a traffic dip alone. Look at inquiries, calls, and conversions before assuming something’s wrong.
Revisit this every few months. This space is moving fast, and what’s true today may shift again by next year.
SEO in 2026 isn’t dying — it’s just gotten bigger.
The core work (good content, a well-built site, real expertise) still counts, but now it needs to hold up across Google, AI chatbots, and beyond. For a small business, that mostly means widening what you pay attention to, not starting over.
If you’re curious whether your own business is currently showing up in AI-driven search — or invisible to it — that’s exactly the kind of gap I help clients uncover with an AI visibility audit. One recent client went from a visibility score of 48 to 90 after we worked through her site together. If you’d like to know where you stand, reach out and let’s take a look.
