A tragic irony.

And you can view it now.

SpongeBall DeathPants.

Incredible!

The evidence is growing.

Our weekly science news roundup.

More than one way to sequence a cat.

The first time this has ever been seen.

"I obviously wasn't aware of the dangers."

A profile of this mysterious condition is emerging.

May you gaze into clear skies.

Dec. 11—ROCHESTER — Dan Maidl's teams took their lumps at times.

Many of those times, one of his daughters, Liz or Kaitlyn, was the one literally taking the lumps.

Nearly 20 years ago, in 2006, Maidl became the first head coach of the Century High School girls hockey team. The city's schools had mostly co-oped up until that point, but Maidl and the players on his inaugural team worked hard to bring more players into the program in an attempt to build it up.

In some cases, girls joined the varsity who hadn't played organized hockey or even skated before.

Maidl was more than happy to welcome them.

The Panthers' relative youth and inexperience in those initial seasons of flying solo led to goalie Liz Maidl seeing 60, 70, 80 or sometimes 90-plus shots in a game.

In fact, after Century's second-ever game, on Nov. 17, 2006, when they lost 7-0 to Albert Lea as Liz Maidl made 63 saves, Dan Maidl said: "We played very well tonight, much better than our first game. Everybody got a shift or two tonight. We play everyone because they have to learn."

Maidl's positive nature was evident both on the ice as a head coach and in life in general.

And it's what his family and friends will remember most about the guy who made sure Century's girls hockey program got off the ground, then didn't fade away.

Dan Maidl died at his home on Saturday, Dec. 6, at age 67.

He is survived by he and his wife Laurie's three children — Derrick (Jess), Liz (John), and Kaitlyn (Blake); his grandson, Brooks; his siblings Glenda (Terry) Lind and Charlie (Tina Snesrud) Maidl; his in-laws, Larry and Donna Radke, Scott (Jan) Radke, Linda (Oz) Osmundson, Brenda (Tim) Plummer, and Steve (Sheila Michels) Radke, and many nieces and nephews. He was preceded in death by his wife, Laurie, and his parents, Glendon and Ione Maidl.

Maidl coached the Century girls for nine seasons.

Many of his players likely remember him for his perseverance and positivity.

He took a brief hiatus from coaching, but returned in November of 2011, seven months after having his right leg amputated six inches below the knee due to diabetic neuropathy.

Maidl taught himself how to skate all over again, often taking to the ice at the Rochester Recreation Center late in the evening, after all other activities were done and the building was nearly empty.

"I wasn't a great skater before, so that should be established," Maidl told the Post Bulletin, with a laugh, in December of 2011. "Hockey stops are kind of an adventure right now. I go down every once in a while at practice, and as soon as I do the girls swarm around and they all want to help me up. But I tell them I'm OK and I have to be able to get up on my own."

He did, over and over again.

He and Laurie were married on Sept. 10, 1983, starting a marriage that spanned more than 40 years.

After Dan retired from coaching, he and Laurie bought a cabin on Woman Lake, southeast of Walker, Minn., which became and remains a gathering place for their kids and grandkids.

A memorial service will be held on Saturday, December 13th at Hosanna Lutheran Church in Rochester. Visitation will start at 11 a.m. and the service will begin at 3 p.m.

In lieu of flowers, Maidl's family asks that donations be made to Project Purple, an organization whose mission is to find a cure for pancreatic cancer.

Dec. 11—BIRMINGHAM, Ala. — Tennessee wide receiver Braylon Staley was named the SEC Freshman of the Year on Wednesday as voted on by the league's head coaches.

Staley, an Aiken native, becomes the third player in program history to earn SEC Freshman of the Year honors, joining running back Jamal Lewis (1997) and quarterback Peyton Manning (1994). He is also the fourth player in the Josh Heupel era to earn an individual SEC postseason award, joining Dylan Sampson (SEC Offensive Player of the Year — 2024), Hendon Hooker (SEC Offensive Player of the Year — 2022) and Velus Jones Jr. (SEC Co-Special Teams Player of the Year — 2021).

The redshirt-freshman had a breakout season during his second year with the program, ranking sixth in the SEC in receiving yards (806) and receiving yards per game (67.2). Staley led all SEC freshmen in both of those categories and also finished tied for the league lead among freshmen with six receiving touchdowns.

Staley, who started his high school career at Aiken High before graduating from Strom Thurmond, was tabbed as the SEC Freshman of the Week twice this season following wins over Syracuse (4 rec., 95 yds, 1 TD) and Arkansas (6 rec., 109 yds). Staley also posted a 100-plus yard performance in the Vols' road victory at Kentucky ( 6 rec., 105 yds) and hauled in five catches for 75 yards and a career-best two touchdowns the following week against No. 18 Oklahoma.

Additionally, Staley was recognized as one of 14 semifinalists for the Shaun Alexander Freshman of the Year Award and was named to The Athletic Midseason All-Freshman Team earlier this season.

It's not always just plastic.

It's still very early days.

One of the major things we talk about with large language models (LLMs) is content creation at scale, and it’s easy for that to become a crutch. 

We’re all time poor and looking for ways to make our lives easier – so what if you could use tools like Claude and ChatGPT to frame your processes in a way that humanizes your website work and eases your day, rather than taking the creativity out of it?

This article tackles how to:

• Analyze customer feedback and questions at scale.
• Automate getting detailed and unique information from subject matter experts.
• Analyze competitors.

These are all tasks we could do manually, and sometimes still might, but they’re large-scale, data-based efforts that lend themselves well to at least some level of automation. 

And having this information will help ground you in the customer, or in the market, rather than creating your own echo chamber.

Analyzing customer feedback at scale

One of the fantastic features of LLMs is their ability to:

• Process data at scale.
• Find patterns.
• Uncover trends that might otherwise take a human hours, days, or weeks. 

Unless you’re at a global enterprise, it’s unlikely you’d have a data team with that capability, so the next best thing is an LLM.

And for this particular opportunity, we’re looking at customer feedback – because who wants to read through 10,000 NPS surveys or free text feedback forms? 

Not me. Probably not you, either.

You could upload the raw data directly into the project knowledge and have your LLM of choice analyze the information within its own interface.

However, my preference is to upload all the raw data into BigQuery (or similar if you have another platform you prefer) and then work with your LLM to write relevant SQL queries to slice and analyze your raw data.

I do this for two reasons: 

• It gives me a peek behind the curtain, offering me the opportunity to learn a bit of the base language (here, SQL) by osmosis.
• It’s another barrier or failsafe for hallucinations.

When raw data is uploaded directly into an LLM and analysis questions are asked directly into the interface, I tend to trust the analysis less. 

It’s much more likely it could just be making stuff up. 

When you have the raw data separated out and are working with the LLM to create queries to interrogate the data, it’s more likely to end up real and true with insights that will help your business rather than lead you on a wild goose chase.

Practically, unless you’re dealing with terrifyingly large datasets, BigQuery is free (though to set up a project, you might need to add a credit card). 

And no need to fear SQL either when you’re pair programming with an LLM – it will be able to give you the full query function. 

My workflow in this tends to be:

• Use SQL function from LLM.
• Debug and check data.
• Input results from SQL query into LLM.
• Generate visualizations either in an LLM or with SQL query.
• Rinse and repeat.

Dig deeper: 7 focus areas as AI transforms search and the customer journey in 2026

Automating subject matter expert interviews

It seems to be a common trait among subject matter experts that they’re time poor. 

They really don’t want to spend an hour talking with the marketing person about a new feature they’ve already discussed with the manufacturer for the last eight months. 

And who could blame them? They’ve probably talked it to death. 

And yet we still need that information, as marketing folk, to strategize how we present that feature on the website and give customers helpful detail that isn’t on the spec sheet.

So how do we get ahold of our experts? 

Create a custom GPT that acts as an interviewer. 

Fair warning, to get the most out of this process, you’ll want a unique version for each launch, product, or service you’re working on. 

It may not need to be as granular as per the article, but it may end up being that specific.

To do this, you’ll need at least a ChatGPT Plus subscription. 

Instructions will depend on your industry and the personality of your subject matter experts or sales team. 

They should include:

• Role and tone: How the “interviewer” should come across.
• Context: What you’re trying to learn and why.
• Interview structure: How to open, topics, how to probe more deeply.
• Pacing: Single question, wait for response, expanding questions.
• Closing: how to wrap and what to deliver at the end.

Once we do that, we’ll want to test it ourselves and pretend to be an SME. Then we refine the instructions from there.

This way, you’ll be able to reach your SMEs in the five minutes they have between calls. 

And you can use an LLM to extrapolate the major points, or even an article draft, from their answers.

Dig deeper: SEO personas for AI search: How to go beyond static profiles

Analyzing competitors for strategic insights

This one may be a bit sneaky and may require a bit of gray thinking. 

But there are a few things you can do with competitive data at scale that can help you understand the competitive landscape and your gaps within it, like:

• If you were able to gather your competitors’ reviews, you could see themes such as benefits, values, common complaints, and weaknesses.
• If you were able to gather their website copy, you could identify their positioning, implied audience, and any claims they may be making, as well as the industries they might be targeting, extrapolated through case studies.
  • With their website copy and support from Wayback Machine, you’d be able to identify with an LLM how their messaging has shifted over time.
  • Job postings could tell you what their strategic priorities are or where they may be looking to test.
  • Once we have their positioning, we’d be able to compare us and them. Where are we saying the same thing, and where are we differentiating?
• If you were able to gather their social interactions and engagement, we might be able to understand, again at scale, where they’re able to answer customer needs and where they might be falling down. What questions aren’t they able to answer?

Dig deeper: How to use competitive audits for AI SERP optimization

Scaling research without losing the human thread

Pair programming with an LLM to ground yourself in your customer with large data sets can be an endless opportunity to get actionable, specific information relatively quickly. 

These three opportunities are solid places to start, but they’re by no means the end. 

To extrapolate further, think about other data sources you own or have access to, like:

• Sales call transcripts.
• Google Search Console query data.
• On-site search.
• Heatmapping from user journey tools.

While it may be tempting to include Google Analytics or other analytics data in this, err on the side of caution and stick with qualitative or specifically customer-led data rather than quantitative data. 

Happy hunting!

It's important to know.

It's vital for almost every aspect of our well-being.

Most brands don’t realize how much traffic they lose each day to unauthorized bidding, affiliate violations, and ad hijacking. 

Industry data shows ad fraud reached an estimated $84 billion of global digital ad spend in 2023. 

If your branded CPCs keep rising or competitors keep appearing above you in searches for your own name, this PPC brand protection guide can help you understand why – and what to do next.

What is brand protection in PPC?

Brand protection is the practice of defending your brand from unauthorized use of your branded search terms in PPC and from deceptive or fraudulent ad placements. 

The goal: make sure people searching for your brand or product name land on your official pages – not a competitor’s, affiliate’s, or reseller’s. 

When done well, brand protection safeguards traffic while strengthening your brand image and customer loyalty.

Without a brand protection strategy, you’ll face steep losses – higher CPCs, rising affiliate costs, and a drop in customer acquisition.

Activities tied to PPC brand protection include:

• Monitoring who bids on your branded keywords.
• Spotting unusual spikes in CPCs or impression share.
• Identifying unauthorized trademark use in paid search.
• Detecting hidden, geo-targeted ads meant to avoid detection.
• Enforcing compliance rules for affiliates and partners.

Core threats and risks

The three main sources of threats are:

• Competitors: Targeting your branded searches is an easy way for them to tap into high-intent traffic and intercept your audience.
• Affiliates: If you miss dishonest tactics, you end up paying for leads you would have won on your own – driving up costs without adding customers.
• Fraudsters: Their increasingly opaque tactics can cause serious financial and reputational damage to your brand.

If you don’t protect your brand in paid search, you’re likely to face these common risks:

• Brand bidding: Others bid on your branded queries to capture high-intent searches, drive up CPCs, and cut into your impression share. Over time, you’re forced to spend more to regain position, lowering your ROI.
• Ad hijacking: Competitors or fraudsters mimic your messaging, ad structure, or landing pages to make users think they’re clicking your official ad.
• Malicious redirects: Clicks on “brand-looking” ads lead to phishing, malware, or low-quality pages.
• Ad copy misalignment: Affiliates use unapproved messaging, outdated claims, or promotions you’re not running, which erodes trust and harms your brand image.
• Comparative or misleading ad copies: Copy that positions another product as a direct replacement for yours to divert conversions.

These risks demand a dedicated PPC protection strategy. Left unchecked, they drive up acquisition costs and cause you to lose customers at the final decision stage.

Why you need to protect your brand in today’s PPC landscape

Failing to protect your brand in PPC erodes trust, skews attribution, and weakens your marketing over time. As a result, conversions drop, ROI slips, and your paid media becomes less effective.

Key facts:

• Global ad fraud costs are projected to grow to $172 billion by 2028 (Statista)
• 69.7% of marketers reported problems with “spam or fake lead submissions” coming from their paid media campaigns (Lunio)
• Cross-industry anti-fraud initiatives saved U.S. advertisers $10.8 billion in 2023 (TAG)

Essential components of a strong brand protection strategy

PPC tactics for effective PPC protection

When your campaigns are organized clearly and systematically, you can control risks more easily and respond faster to unauthorized activity.

Key elements of a well-planned brand protection strategy include:

• Account structure: Keep your campaigns clearly segmented. Separate branded ads so you can spot anomalies in CPCs and impression share.
• Negative keyword strategy: Use targeted negatives—partner names, resellers, and irrelevant variations—to cut noise and prevent unwanted triggers around your brand.
• Rules for affiliates: Set clear policies to prevent most violations and make it easier to detect risks and enforce compliance.

Monitoring and automation for PPC brand protection

Manual monitoring can’t keep up with competitors and fraudsters who constantly rotate tactics. A strong brand protection strategy relies on automated monitoring to catch threats early and resolve them before they affect your budget, CPCs, or conversions.

Core components of effective automation include:

• Monitoring systems: Continuously track and surface unauthorized bidders, affiliate violations, and unusual competitor activity.
• Real-time alerts: Get notified the moment issues appear so you can respond quickly.

Key metrics to measure your brand protection strategy

You can measure the effectiveness of your PPC brand protection efforts by tracking metrics that show the scale of violations and how efficiently you respond to them.

Key metrics include:

• Violations count: How many unauthorized activities were detected across branded searches during a set timeframe.
• Enforcement rate: How effectively you acted on those violations.
• Cost savings: The budget you recovered by reducing CPC inflation and stopping commission leakage.
• Branded CTR recovery: How much your visibility and click-through rate improved after removing violators.
  

Together, these metrics provide a clear view of how well your brand protection strategy is performing and where you may need to make improvements.

Industry cases of effective PPC brand protection

Automotive: Car.co.uk

UAWC agency shared a use case involving a car company that was losing branded traffic in paid search. The source of the problem turned out to be competitors’ aggressive brand bidding tactics. 

To recover the losses, the brand had to employ UAWC to audit competitors, identify branded keyword conflicts, restructure ad campaigns, and closely monitor auction dynamics. 

As a result, branded impression share rose to 95%, protecting high-intent traffic and stabilizing CPCs. 

iGaming: Rhino Affiliates

Rhino was grappling with affiliate fraud and unauthorized brand bidding on its flagship brand. With the help of Bluepear, they uncovered 105 violators. 

Using reports and screenshots as evidence, Rhino successfully disputed payments – ultimately saving €131,000 and restoring their branded search visibility. 

How Bluepear helps you protect your brand automatically

Monitoring is the operational backbone of brand protection – that’s exactly where Bluepear delivers the most impact.

After signing up: You create an account and fully customise it with the help of a built-in AI-assistant – it only takes 10 minutes. From there, you get instant access to automated brand monitoring. Bluepear reveals every violation, including: 

• Brand bidding: Identifies advertisers targeting your branded keywords across markets and devices.
• Affiliate violations: Flags partners who break program rules by bidding on brand terms, using unapproved messaging, or redirecting traffic.
• Hidden ads: Detects ads that are visible only in specific regions or time periods – a common tactic used by violators to evade detection.

Bluepear alerts you to every violation and backs each one with clear evidence and screenshots. This gives you airtight proof for fast escalation and cuts the time you spend disputing payments with affiliates and PPC platforms.

Impact: After removing unauthorized bidders, you gain cleaner attribution, lower acquisition costs, and stronger efficiency across all paid channels.

Final recommendations for scalable PPC protection

• Continuous monitoring: New violators can appear at any moment. Ongoing monitoring ensures you catch issues before they inflate CPCs or drain conversions.
• Strict affiliate rules: Well-defined rules reduce ambiguity and improve long-term compliance.
• Automation-first approach: Automation speeds detection, supports faster decisions, and scales protection across markets and campaigns.
• Consistent enforcement: Fast, repeatable enforcement maintains deterrence and keeps branded auctions clean.

Most of the damage to your branded traffic happens out of sight – hidden ads, affiliate rule breaks, and impersonation fraud. Bluepear uncovers it all instantly, starting at just $169 a month after a free trial.

See what’s been slipping through:

Try Bluepear’s solution for brand protection and detect hidden brand bidding in minutes.

Sudden silence after 11 years in orbit.

Any questions?

It's not just a disease.

Adorable.

Instagram launched Your Algorithm in the U.S. today, a tool that lets people see – and directly edit – the topics shaping their Reels recommendations.

Why we care. This could reshape how users discover content. When people signal interest in specific niches, hobbies, or brands – from running shoes to vintage clothing to home organizers – Instagram may surface more of that content, boosting reach for brands that publish relevant Reels.

How it works. A new Reels icon opens a personalized list of topics (e.g., sports, thrifting, horror movies, pop music, chess, day in the life, college football, skateboarding) Instagram believes “you’ve been into” lately, generated by Meta’s AI. You can:

• Tap to see more or less of any topic, or add your own.
• Share your algorithm snapshot to Stories.

What’s next. The tool will expand to Explore, the search tab, and other surfaces, with a global English rollout planned, Instagram said. These controls will extend beyond Reels in the future.

What Instagram is saying. Tessa Lyons, Instagram’s vice president of product, told Fast Company:

• “We’re always trying to show people the best possible reels for them. I think we do a pretty good job today, but we don’t always get it right, and we know that people’s interests change. What we really want to do is give people control over the experience that they have on Instagram.”

Similar to TikTok’s feature. TikTok introduced a Manage Topics tool last year, but its controls are broader and less personalized. Users choose from generic categories like travel or current affairs, while Instagram’s list is individualized and driven by each person’s recent activity.

The announcement. Adam Mosseri, head of Instagram, shared the news via Instagram.

Sparking our evolution.

The art of ambush.

With generative AI tools attracting hundreds of millions of users and AI-enhanced results appearing in more search experiences, the way people discover brands is changing. Traditional SEO metrics alone no longer capture this full picture.

Welcome to the era of generative engine optimization (GEO). If you aren’t tracking your brand’s visibility across AI search engines, you’re flying blind.

The AI search revolution is already here

The numbers are striking: 

• 58% of consumers have replaced traditional search engines with generative AI tools for product recommendations, according to Capgemini research.
• Traditional organic search traffic is expected to decline by 50% by 2028, per Gartner. 
• ChatGPT referrals now drive 10% of its new user sign-ups – up from less than 1% just six months prior, Vercel reported.

Unlike traditional search, where you fight for spots on a results page, AI search engines like ChatGPT, Claude, Gemini, and Perplexity deliver direct answers and cite only a few sources. If your brand isn’t mentioned, you may be invisible to users who rely on AI-generated answers.

This is where a GEO rank tracker becomes essential. Tools like Geoptie’s free GEO Rank Tracker show you exactly where your brand stands across major AI platforms.

What is a GEO rank tracker?

A GEO rank tracker measures how often your brand appears, gets cited, and is recommended across AI-powered search platforms. Unlike traditional rank trackers that focus on your position on search engine results pages, GEO tracking zeroes in on these metrics that actually matter in the AI era:

• Brand mention frequency: How often AI engines reference your brand when answering relevant queries.
• Citation rates: Whether your website appears as a source in AI-generated responses.
• Share of voice: Your visibility compared to competitors within AI answers.
• Cross-platform performance: Your visibility across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews.

In traditional SEO, you optimize for where you appear in a list of search results. In GEO, you optimize for whether AI mentions you at all – and what it says when it does. Geoptie helps brands navigate this shift with a full suite of GEO tools.

Why traditional rank tracking falls short

If you still rely on traditional rank tracking tools, you’re measuring yesterday’s game:

• Different discovery mechanisms: Traditional search engines rank pages using a variety of signals, such as backlinks and keywords. AI engines use retrieval-augmented generation (RAG) to pull from multiple sources and synthesize a single answer.
• Answers over clicks: Users are increasingly relying on AI-generated summaries without ever clicking through. A top-ranking page means nothing if AI doesn’t cite it.
• Variable outputs: AI responses shift from query to query. Tracking them requires consistent monitoring and statistical sampling – exactly what Geoptie’s GEO Rank Tracker is built to handle.
• Multi-platform fragmentation: Your brand might show up in ChatGPT yet be invisible in Perplexity. Each AI platform pulls from different data sources and uses its own retrieval methods.

Key metrics every GEO rank tracker should measure

When evaluating your AI search visibility, focus on these core metrics:

• Citation Frequency: How often your site or content gets cited in AI-generated answers. This is the GEO version of earning a backlink – only it directly shapes what millions of users see.
• Brand Visibility Score: A composite metric showing how prominently your brand appears across AI platforms for your target keywords and topics. High visibility means the AI reliably recognizes and references your brand as relevant.
• AI Share of Voice: Your brand’s mention rate compared to competitors in AI-generated answers. If a competitor shows up in 60% of relevant responses and you appear in only 15%, that gap represents a lost opportunity.
• Sentiment and Positioning: AI platforms don’t just mention brands – they characterize them. Understanding how AI describes your business helps identify perception gaps and optimization opportunities.
• Geographic Performance: AI responses can vary by location. A GEO rank tracker with location-based analysis shows how your visibility differs across markets.

How to track your brand’s AI search rankings

Getting started with GEO tracking requires a systematic approach:

Step 1: Identify your core prompts

Start by mapping the questions your potential customers ask at each stage of their journey. Unlike keyword research, prompt research focuses on the natural language questions people type into AI chatbots.

Step 2: Monitor across all major platforms

AI search is fragmented across multiple platforms, each with different strengths and user bases:

• ChatGPT: The dominant player with 800+ million weekly users.
• Google AI Overviews: Appearing on billions of Google searches.
• Claude: Growing rapidly with integration into Safari.
• Perplexity: Gaining traction for research-oriented queries.
• Gemini: Google’s standalone AI assistant is growing fast.

Step 3: Track by location and language

AI responses vary by geography. If you serve multiple markets, track your visibility in each target country.

Step 4: Benchmark against competitors

Understanding your share of voice against competitors shows whether you’re gaining or losing ground in AI search visibility.

For brands looking to get started fast, Geoptie’s free GEO Rank Tracker offers an easy entry point. Add your domain, target country, and keyword, and the tool shows your rankings across Gemini, ChatGPT, Claude, and Perplexity – giving you an instant snapshot of your AI search presence.

Interpreting your GEO Rank Tracker results

Understanding what your AI visibility data means is crucial for taking action:

High visibility, low citations

If AI platforms often mention your brand but rarely cite your site, your content may not have the structured, authoritative format AI engines prefer. Strengthen it with statistics, expert quotes, and clear source attribution.

Strong on one platform, weak on others

Each AI platform draws from different data sources. If you’re visible in ChatGPT but absent in Perplexity, investigate which sources each platform favors and adjust your distribution strategy to match.

Declining visibility over time

AI systems continually retrain on new content. If your visibility slips, competitors may be creating more citation-worthy material, or your content may simply be getting stale. Regular updates and fresh publishing are essential.

Competitor visibility gaps

When you spot queries where competitors appear, but you don’t, you’ve found optimization opportunities. Analyze what makes their content citation-worthy, then create competing assets.

From tracking to optimization: Building your GEO strategy

A GEO rank tracker gives you the data, but turning those insights into stronger visibility takes strategic action:

• Expand your semantic footprint: Cover your core topics thoroughly, including adjacent concepts and the related questions users are likely to ask.
• Increase fact density: AI platforms prefer content packed with statistics and verifiable details. Research from Princeton University, Georgia Tech, and the Allen Institute for AI suggested that adding citations and quotes boosted AI visibility by more than 40%.
• Optimize for structure: Use clear headers, TL;DR summaries, and FAQ sections. AI engines often pull structured content directly into their answers.
• Build entity authority: Keep your brand’s information consistent across trusted, authoritative sources that AI platforms rely on.

For comprehensive AI search optimization beyond rank tracking, Geoptie’s GEO dashboard offers tools for content analysis, competitive intelligence, technical GEO audits, and ongoing performance monitoring.

The cost of ignoring GEO tracking

The shift to AI search is already here. Brands that ignore their AI visibility risk:

• Losing discovery opportunities as more users rely on AI for recommendations.
• Falling behind competitors who are already building their AI visibility.
• Misallocating resources without knowing whether content investments are paying off.
• Missing brand-perception blind spots in how AI describes and positions them.

Getting started today

The barrier to entry for GEO tracking is lower than you might expect. Here’s a simple plan to get started:

1. Run an initial visibility check: Use a free tool like Geoptie’s GEO Rank Tracker to see where you stand across major AI platforms.
2. Document your baseline: Record your mention rates, citation frequencies, and competitor comparisons.
3. Identify quick wins: Look for queries where you’re close to visibility or where small content improvements could earn citations.
4. Establish ongoing monitoring: AI visibility changes faster than traditional rankings. Geoptie’s comprehensive dashboard helps you spot trends early.
5. Integrate GEO into your broader strategy: The strongest approach pairs traditional SEO with GEO.

The future of AI search visibility

We’re still in the early days of GEO. Brands that start understanding and optimizing for AI search now will gain advantages that compound over time.

Key trends to watch:

• Multimodal search: AI platforms are starting to process images, voice, and video alongside text.
• Real-time integration: AI systems are connecting to live data sources for fresher, more accurate answers.
• Platform fragmentation: More AI search options are emerging and competing for user attention.

Your visibility in AI-generated answers will increasingly determine whether customers discover your brand. A reliable GEO rank tracker is becoming core infrastructure for modern marketing.

Key takeaways

• Traditional rank tracking doesn’t capture AI search visibility. Dedicated GEO tools like Geoptie are essential.
• Core GEO metrics include citation frequency, brand visibility score, AI share of voice, and geographic performance
• AI search varies by platform, so meaningful tracking requires monitoring ChatGPT, Claude, Gemini, and Perplexity.
• GEO data fuels smarter optimization – expanding your semantic footprint, increasing fact density, and building entity authority.
• Start with Geoptie’s free GEO Rank Tracker and scale to the full GEO dashboard for comprehensive optimization

Mind-blowing.

Humans are amazing.

"The myth that gout is caused by lifestyle or diet needs to be busted."

How do you like being cat called?

An abandoned construction site had the answer.

Generative systems like ChatGPT, Gemini, Claude, and Perplexity are quietly taking over the early parts of discovery – the “what should I know?” stage that once sent millions of people to your website. 

Visibility now isn’t just about who ranks. It’s about who gets referenced inside the models that guide those decisions.

The metrics we’ve lived by – impressions, sessions, CTR – still matter, but they no longer tell the full story. 

Mentions, citations, and structured visibility signals are becoming the new levers of trust and the path to revenue.

This article pulls together data from Siege Media’s two-year content performance study, Grow and Convert’s conversion findings, Seer Interactive’s AI Overview research, and what we’re seeing firsthand inside generative platforms. 

Together, they offer a clearer view of where visibility, engagement, and buying intent are actually moving as AI takes over more of the user journey – and has its eye on even more.

Content type popularity and engagement trends

In a robust study, the folks at Siege Media analyzed two years of performance across various industry blogs, covering more than 7.2 million sessions. It’s an impressive dataset, and kudos to them for sharing it publicly.

A disclaimer worth noting: the data focuses on blog content, so these trends may not map directly to other formats such as videos, documentation, or landing pages.

With that in mind, here’s a run-through of what they surfaced.

TL;DR of the Siege Media study

Pricing and cost content saw the strongest growth over the past two years, while top-of-funnel guides and “how-to” posts declined sharply.

They suggest that pricing pages gained ground at the expense of TOFU content. I interpret this differently. 

Pricing content didn’t simply replace TOFU because the relationship isn’t zero-sum. 

As user patterns evolve, buyers increasingly start with generative research, then move to high-intent queries like pricing or comparisons as they get closer to a decision.

That distinction – correlation vs. causation – matters a lot in understanding what’s really changing.

The data shows major growth in pricing pages, calculators, and comparison content. 

Meanwhile, guides and tutorials – the backbone of legacy SEO – took a sharp hit. 

Keep that drop in mind. We’ll circle back to it later.

Interestingly, every major content category saw an increase in engagement. That makes sense. 

As users complete more of their research inside generative engines, they reach your site later in the journey or for additional details, when they’re already motivated and ready to act.

If you’re a data-driven SEO, this might sound like a green light to focus exclusively on bottom-of-funnel content. 

Why bother with top-of-funnel “traffic” that doesn’t convert? 

Leave that for the suckers chasing GEO visibility metrics for vanity, right?

But of course, this is SEO, so I have to say it …

Did you expect me to say, “It depends?”

Here’s a question instead: when that high-intent user typed the query that surfaced a case study, pricing page, or comparison page, where did they first learn the brand existed?

Dig deeper: AI agents in SEO: What you need to know

Don’t forget the TOFU!

I can’t believe I’m saying this, but you’ll have to keep making TOFU content. 

You might need to make even more of it.

Let’s think about legacy SEO.

If we look back – waaaaay back – to 2023 and a study from Grow and Convert, we see that while there is far more TOFU traffic…

…it converts far worse.

Note: They only looked at one client, so take it with a grain of salt. However, the direction still aligns with other studies and our instincts.

This pattern also shows up across channels like PPC, which is why TOFU keywords are generally cheaper than BOFU.

The conversion rate is higher at the bottom of the funnel.

Now we’re seeing this shift carry over to generative engines, except that generative engines cover the TOFU journey almost entirely. 

Rather than clicking through a series of low-conversion content pieces as they move through the funnel, users stay inside the generative experience through TOFU and often MOFU, then click through or shift to another channel (search or direct) only when it’s time to convert.

For example, when I asked ChatGPT to help me plan a trip to the Outer Banks:

After a dozen back-and-forths planning a trip and deciding what to eat, I wanted to find out where to stay.

That journey took me through many steps and gave me multiple chances to encounter different brands and filtering or refinement options. 

I eventually landed on my BOFU prompt, “Some specific companies would be great.” 

From there, I might click the links or search for the company names on Google.

What matters about this journey – apart from the fact that my final query would be practically useless as insight in something like Search Console – is that throughout the TOFU and MOFU stages, I was seeing citations and encountering brands I would rely on later. 

Once I switched into conversion mode, I wanted help making decisions. That’s where I’m likely to click through to a few companies to find a rental.

So, when we read statistics like Pew’s finding that AI Overviews reduce CTR by upwards of 50%, and then consider what happens when AI Mode hits the browser, it’s easy to worry about where your traffic goes. Add to that ChatGPT’s 700 million weekly active users (and growing):

And according to their research on how users engage with it:

We can see a clear TOFU hit and very little BOFU usage.

So, on top of the ~50% hit you may be taking from AI Overviews, 700+ million people are going to ChatGPT and other generative platforms for their top-of-funnel needs. 

I did exactly that above with my trip planning to the OBX.

Dig deeper: 5 B2B content types AI search engines love

But wait!

The good news is that while that vacation rental company or blue widget manufacturer might not see me on their site when I’m figuring out what to do – or what a blue widget even is – I’m still going to take the same number of holidays and buy the same number of products I would have without AI Overviews or ChatGPT, Claude, Perplexity, etc.

Unless you’re a publisher or make money off impressions, you’ll still have the same amount of money to be made. 

It just might take fewer website visits to do it.

More about TOFU

Traffic at the bottom of the funnel is holding steady for now (more on that below), but the top of the funnel is being replaced quickly by generative conversations rather than visits. 

The question is whether being included in those conversations affects your CTR further down the funnel.

The folks at Seer Interactive found that organic clicks rose from 0.6% to 1.08% when a site was cited in AI Overviews. 

And while the traffic was far lower, ChatGPT had a conversion rate of 16% compared with Google organic’s 1.8%.

If we look at the conversion rate for organic traffic at the bottom of the funnel – which we saw above – it was 4.78%. 

Users who engage with generative engines clearly get further into their decision-making than users who reach BOFU queries through organic search. 

But why?

While I can’t be certain, I agree with Seer’s conclusion that AI-driven users are pre-sold during the TOFU stage. 

They’ve already encountered your brand and trust the system to interpret their needs. When it’s time to convert, they’re almost ready with their credit card.

Why bottom-funnel stability won’t last much longer

Above, I noted that “traffic at the bottom of the funnel is holding steady for now.”

It’s only fair to warn you that through 2026 and 2027, we’ll likely see this erode. 

The same number of people will still travel and still buy blue widgets. 

They just won’t book or buy them themselves. And at best, attribution will be even worse than it is today.

I spoke at SMX Advanced last spring about the rise of AI agents. 

I won’t get into all the gory details here, but the Cliff Notes are this:

Agents are AI systems with some autonomy that complete tasks humans otherwise would. 

They’re rising quickly – it’s the dominant topic for those of us working in AI – and that growth isn’t slowing anytime soon. You need to be ready.

A few concepts to familiarize yourself with, if you want to understand what’s coming, are:

• AP2 (Agent Payments Protocol): A standard that allows agents to securely execute payments on your behalf. Think of it as a digital letter of credit that ensures the agent can only buy the specific “blue widget” you approved within the price limit you set. Before you say, “But I’d never send a machine to do a human’s job,” let me tell you, you will. And if you somehow prove me wrong individually out of spite, your customers will.
• Gemini Computer Use Model API: A model with reasoning and image understanding that can navigate and engage with user interfaces like websites. While many agentic systems access data via APIs, this model (OpenAI has one too, as do others) lets the agent interact with visual interfaces to access information it normally couldn’t – navigating filters, logins, and more if given the power.
• MCP (Model Context Protocol): An emerging standard acting as a universal USB port for AI apps. It lets agents safely connect to your internal data (like checking your calendar or reading your emails) to make purchasing decisions with full context and to work interactively with other agents. Hat tip to Ahrefs for building an awesome MCP server.

Dig deeper: How Model Context Protocol is shaping the future of AI and search marketing

Why do these protocols matter to a content strategist?

Because once AP2 and Computer Use hit critical mass, the click – that sacred metric we’ve optimized for two decades – changes function. 

It stops being a navigation step for a human exploring a website and becomes a transactional step for a machine executing a task.

If an agent uses Computer Use to navigate your pricing page and AP2 to pay for the subscription, the human user never sees your bottom-of-the-funnel content. 

So in that world, who – or rather, what – are you optimizing for?

This brings us back to the Siege Media data. 

Right now, pricing pages and calculators are winning because humans are using AI to research (TOFU and MOFU) and then manually visiting sites to convert (BOFU). 

But as agents take over execution, that manual visit disappears. The “traffic” to your pricing page may be bots verifying costs, not humans persuaded by your copy.

The 2026 strategy

This reality pushes value back up the funnel. 

If the agent handles the purchase, the human decision – the “moment of truth” – happens entirely inside the chat interface or agentic system during the research phase.

In this world, you don’t win by having the flashiest pricing page. 

You win by being the brand the LLM recommends when the user asks, “Who should I trust?”

Your strategy for 2026 requires a two-pronged approach:

• For the agent (the execution): Ensure your BOFU content is technically flawless. Use clean schema, accessible APIs, and clear data structures so that when an agent arrives via MCP or Computer Use to execute a transaction, it encounters no friction.
• For the human (the selection): Double down on TOFU. Focus on mentions and citations. You need to be the entity referenced in the generative answer so that users – and agents – trust you.

As we move toward 2026 and then 2027 (it’ll be here sooner than you think), the “click” will become a commodity more often handled by machines. 

The mention, however, remains the domain of human trust. And in my opinion, that’s where your next battle for visibility will be fought.

Time to start – or hopefully keep – making the TOFU.

Jewel thieves walked right past it.

In 1997, Apple launched a campaign that became cultural gospel. “Think Different” celebrated the rebels, the misfits, the troublemakers. The ones who saw things differently. The ones who changed the world. 

Apple understood something fundamental: the constraints that limited imagination weren’t real. They were inherited. Accepted. Assumed. And the people who broke through weren’t smarter or more talented. They simply refused to believe the constraints applied to them. 

Twenty-eight years later, marketing faces its own Think Different moment. 

The constraints are gone. Technology has removed them. AI can generate infinite variants. Data platforms deliver real-time insights. Orchestration tools coordinate across every channel instantly. The infrastructure that once required armies of specialists, weeks of coordination and endless approvals now exists in platforms accessible to any marketer willing to learn them. 

Yet most marketers still operate as if the box exists. 

They wait for the data team to run the analysis. They wait for creative to deliver the assets. They wait for engineering to build the integration. They operate within constraints that technology has already eliminated, not because they must, but because assembly-line marketing taught them that’s how it worked. 

Creative waits for data. Campaigns wait for creative. Launch waits for engineering. Move from station to station. Hand off to the next department. That was the assembly line. That was the box. 

And that box is gone. But the habits remain.  

Here’s to the marketers who refuse to wait for approval

The ones who see a customer signal at 3 p.m. and launch a personalized journey by 4 p.m., not because they asked permission but because the customer needed it now. 

The ones who don’t send briefs to three different teams. They access the data, generate the creative and orchestrate the campaign themselves. Not because they’re trying to eliminate specialists, but because waiting days for what they can deliver in hours wastes the moment. 

The ones who run experiments constantly, not occasionally. Who test 10 variants instead of two. Who measure lift instead of clicks. Who know that perfect insight arrives through iteration, not through analysis paralysis. 

Here’s to the ones who see campaigns where others see dependencies 

They don’t see a handoff to the analytics team. They see customer data they can access instantly to understand behavior, predict intent and target precisely. 

They don’t see a creative approval process. They see AI tools that generate channel-ready assets in minutes, allowing them to personalize at scale rather than compromise for efficiency. 

They don’t see an engineering backlog. They see orchestration platforms that automate journeys, test variations and optimize outcomes without a single ticket. 

They’re not reckless. They’re not cowboys  

They’re simply operating at the speed technology now enables, constrained only by strategy and judgment rather than structure and process.  

This is what Positionless Marketing means: Wielding Data Power, Creative Power and Optimization Power simultaneously. Not because you’ve eliminated everyone else, but because technology eliminated the dependencies that once made those handoffs necessary. 

And here’s what most people miss: This isn’t just about speed. It’s about potential 

When marketers were constrained by assembly-line marketing infrastructure, their job was to manage the line. Write the brief. Coordinate the teams. Navigate the approvals. Wait for each station to finish its work. The marketer’s skill was project management. Their value was orchestrating others. 

Now? Your job in marketing has changed entirely 

Your job is no longer to manage process. Your job is to enable potential. To help every person on your team (and yourself) realize what they’re capable of when the constraints disappear. To show them that the data they’ve been waiting for is accessible now. That the creative they’ve been briefing can be generated instantly. That the campaigns they’ve been coordinating can be orchestrated autonomously.  

Teach people to think outside the box by showing them there is no longer a box 

The data analyst who only ran reports can now build predictive models and operationalize them in real time. The campaign manager who only coordinated handoffs can now design, test and optimize end-to-end journeys independently. The creative strategist who only wrote briefs can now generate and deploy assets across every channel. 

This is the revolution: not that technology does the work, but that technology removes the barriers that prevented people from doing work they were always capable of. 

The misfits and rebels of 1997 saw possibilities where others saw limitations. They refused to accept that things had to be done the way they’d always been done. 

The Positionless Marketers of today are doing the same thing 

They’re refusing to wait when customers need action now. They’re refusing to accept that insight takes weeks when platforms deliver it in seconds. They’re refusing to operate within constraints that technology has already eliminated. 

They’re thinking differently. Not because they’re trying to be difficult. But because the old way of thinking no longer matches the new reality of what’s possible. 

In 1997, Apple told us: “The people who are crazy enough to think they can change the world are the ones who do.”  

In 2025, the people crazy enough to think they can deliver personalized experiences at scale, launch campaigns in hours instead of weeks, and operate without dependencies are the ones who will. 

The constraints are gone. 

The assembly-line marketing box can no longer exist. 

WHOOSH!

25 years after it was published.

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Like volcanoes erupting.