Most Major News Publishers Block AI Training & Retrieval Bots via @sejournal, @MattGSouthern

Most top news publishers block AI training bots via robots.txt, but they’re also blocking the retrieval bots that determine whether sites appear in AI-generated answers.

BuzzStream analyzed the robots.txt files of 100 top news sites across the US and UK and found 79% block at least one training bot. More notably, 71% also block at least one retrieval or live search bot.

Training bots gather content to build AI models, while retrieval bots fetch content in real time when users ask questions. Sites blocking retrieval bots may not appear when AI tools try to cite sources, even if the underlying model was trained on their content.

What The Data Shows

BuzzStream examined the top 50 news sites in each market based on SimilarWeb traffic share, then deduplicated the list. The study grouped bots into three categories: training, retrieval/live search, and indexing.

Training Bot Blocks

Among training bots, Common Crawl’s CCBot was the most frequently blocked at 75%, followed by Anthropic-ai at 72%, ClaudeBot at 69%, and GPTBot at 62%.

Google-Extended, which trains Gemini, was the least blocked training bot at 46% overall. US publishers blocked it at 58%, nearly double the 29% rate among UK publishers.

Harry Clarkson-Bennett, SEO Director at The Telegraph, told BuzzStream:

“Publishers are blocking AI bots using the robots.txt because there’s almost no value exchange. LLMs are not designed to send referral traffic and publishers (still!) need traffic to survive.”

Retrieval Bot Blocks

The study found 71% of sites block at least one retrieval or live search bot.

Claude-Web was blocked by 66% of sites, while OpenAI’s OAI-SearchBot, which powers ChatGPT’s live search, was blocked by 49%. ChatGPT-User was blocked by 40%.

Perplexity-User, which handles user-initiated retrieval requests, was the least blocked at 17%.

Indexing Blocks

PerplexityBot, which Perplexity uses to index pages for its search corpus, was blocked by 67% of sites.

Only 14% of sites blocked all AI bots tracked in the study, while 18% blocked none.

The Enforcement Gap

The study acknowledges that robots.txt is a directive, not a barrier, and bots can ignore it.

We covered this enforcement gap when Google’s Gary Illyes confirmed robots.txt can’t prevent unauthorized access. It functions more like a “please keep out” sign than a locked door.

Clarkson-Bennett raised the same point in BuzzStream’s report:

“The robots.txt file is a directive. It’s like a sign that says please keep out, but doesn’t stop a disobedient or maliciously wired robot. Lots of them flagrantly ignore these directives.”

Cloudflare documented that Perplexity used stealth crawling behavior to bypass robots.txt restrictions. The company rotated IP addresses, changed ASNs, and spoofed its user agent to appear as a browser.

Cloudflare delisted Perplexity as a verified bot and now actively blocks it. Perplexity disputed Cloudflare’s claims and published a response.

For publishers serious about blocking AI crawlers, CDN-level blocking or bot fingerprinting may be necessary beyond robots.txt directives.

Why This Matters

The retrieval-blocking numbers warrant attention here. In addition to opting out of AI training, many publishers are opting out of the citation and discovery layer that AI search tools use to surface sources.

OpenAI separates its crawlers by function: GPTBot gathers training data, while OAI-SearchBot powers live search in ChatGPT. Blocking one doesn’t block the other. Perplexity makes a similar distinction between PerplexityBot for indexing and Perplexity-User for retrieval.

These blocking choices affect where AI tools can pull citations from. If a site blocks retrieval bots, it may not appear when users ask AI assistants for sourced answers, even if the model already contains that site’s content from training.

The Google-Extended pattern is worth watching. US publishers block it at nearly twice the UK rate, though whether that reflects different risk calculations around Gemini’s growth or different business relationships with Google isn’t clear from the data.

Looking Ahead

The robots.txt method has limits, and sites that want to block AI crawlers may find CDN-level restrictions more effective than robots.txt alone.

Cloudflare’s Year in Review found GPTBot, ClaudeBot, and CCBot had the highest number of full disallow directives across top domains. The report also noted that most publishers use partial blocks for Googlebot and Bingbot rather than full blocks, reflecting the dual role Google’s crawler plays in search indexing and AI training.

For those tracking AI visibility, the retrieval bot category is what to watch. Training blocks affect future models, while retrieval blocks affect whether your content shows up in AI answers right now.


Featured Image: Kitinut Jinapuck/Shutterstock

Google’s Mueller Weighs In On SEO vs GEO Debate via @sejournal, @MattGSouthern

Google Search Advocate John Mueller says businesses that rely on referral traffic should think about how AI tools fit into the picture.

Mueller responded to a Reddit thread asking whether SEO is still enough or whether practitioners need to start considering GEO, a term some in the industry use for optimizing visibility in AI-powered answer engines like ChatGPT, Gemini, and Perplexity.

“If you have an online business that makes money from referred traffic, it’s definitely a good idea to consider the full picture, and prioritize accordingly,” Mueller wrote.

What Mueller Said

Mueller didn’t endorse or reject the GEO terminology. He framed the question in terms of practical business decisions rather than new optimization techniques.

“What you call it doesn’t matter, but ‘AI’ is not going away, but thinking about how your site’s value works in a world where ‘AI’ is available is worth the time,” he wrote.

He also pushed back on treating AI visibility as a universal priority. Mueller suggested practitioners look at their own data first.

Mueller added:

“Also, be realistic and look at actual usage metrics and understand your audience (what % is using ‘AI’? what % is using Facebook? what does it mean for where you spend your time?).”

Why This Matters

I’ve been tracking Mueller’s public statements for years, and this one lands differently than the usual “it depends” responses he’s known for. He’s reframing the GEO question as a resource allocation problem rather than a terminology debate.

The GEO conversation has picked up steam over the past year as AI answer engines started sending measurable referral traffic. I’ve covered the citation studies, the traffic analyses, and the research comparing Google rankings to LLM citations. What’s been missing is a clear signal from Google: is this a distinct discipline, or just rebranded SEO?

Mueller’s answer is consistent with what Google said at Search Central Live, when Gary Illyes emphasized that AI features share infrastructure with traditional Search. The message from both is that you probably don’t need a separate framework, but you do need to understand how discovery is changing.

What I find more useful is his emphasis on checking your own numbers. Current data shows ChatGPT referrals at roughly 0.19% of traffic for the average site. AI assistants combined still drive less than 1% for most publishers. That’s growing, but it’s not yet a reason to reorganize your entire strategy.

The industry has a habit of chasing trends that apply to some sites but not others. Mueller’s pushing back on that pattern. Look at what percentage of your audience actually uses AI tools before reallocating resources toward them.

Looking Ahead

The GEO terminology will likely stick, regardless of Google’s stance. Mueller’s framing puts the decision back on individual businesses to measure their own audience behavior.

For practitioners, this means the homework is in your analytics. If AI referrals are showing up in your traffic sources, they’re worth understanding. If they’re not, you have other priorities.


Featured Image: Roman Samborskyi/Shutterstock

The Guardian: Google AI Overviews Gave Misleading Health Advice via @sejournal, @MattGSouthern

The Guardian published an investigation claiming health experts found inaccurate or misleading guidance in some AI Overview responses for medical queries. Google disputes the reporting and says many examples were based on incomplete screenshots.

The Guardian said it tested health-related searches and shared AI Overview responses with charities, medical experts, and patient information groups. Google told The Guardian the “vast majority” of AI Overviews are factual and helpful.

What The Guardian Reported Finding

The Guardian said it tested a range of health queries and asked health organizations to review the AI-generated summaries. Several reviewers said the summaries included misleading or incorrect guidance.

One example involved pancreatic cancer. Anna Jewell, director of support, research and influencing at Pancreatic Cancer UK, said advising patients to avoid high-fat foods was “completely incorrect.” She added that following that guidance “could be really dangerous and jeopardise a person’s chances of being well enough to have treatment.”

The reporting also highlighted mental health queries. Stephen Buckley, head of information at Mind, said some AI summaries for conditions such as psychosis and eating disorders offered “very dangerous advice” and were “incorrect, harmful or could lead people to avoid seeking help.”

The Guardian cited a cancer screening example too. Athena Lamnisos, chief executive of the Eve Appeal cancer charity, said a pap test being listed as a test for vaginal cancer was “completely wrong information.”

Sophie Randall, director of the Patient Information Forum, said the examples showed “Google’s AI Overviews can put inaccurate health information at the top of online searches, presenting a risk to people’s health.”

The Guardian also reported that repeating the same search could produce different AI summaries at different times, pulling from different sources.

Google’s Response

Google disputed both the examples and the conclusions.

A spokesperson told The Guardian that many of the health examples shared were “incomplete screenshots,” but from what the company could assess they linked “to well-known, reputable sources and recommend seeking out expert advice.”

Google told The Guardian the “vast majority” of AI Overviews are “factual and helpful,” and that it “continuously” makes quality improvements. The company also argued that AI Overviews’ accuracy is “on a par” with other Search features, including featured snippets.

Google added that when AI Overviews misinterpret web content or miss context, it will take action under its policies.

The Broader Accuracy Context

This investigation lands in the middle of a debate that’s been running since AI Overviews expanded in 2024.

During the initial rollout, AI Overviews drew attention for bizarre results, including suggestions involving glue on pizza and eating rocks. Google later said it would reduce the scope of queries that trigger AI-written summaries and refine how the feature works.

I covered that launch, and the early accuracy problems quickly became part of the public narrative around AI summaries. The question then was whether the issues were edge cases or something more structural.

More recently, data from Ahrefs suggests medical YMYL queries are more likely than average to trigger AI Overviews. In its analysis of 146 million SERPs, Ahrefs reported that 44.1% of medical YMYL queries triggered an AI Overview. That’s more than double the overall baseline rate in the dataset.

Separate research on medical Q&A in LLMs has pointed to citation-support gaps in AI-generated answers. One evaluation framework, SourceCheckup, found that many responses were not fully supported by the sources they cited, even when systems provided links.

Why This Matters

AI Overviews appear above ranked results. When the topic is health, errors carry more weight.

Publishers have spent years investing in documented medical expertise to meet. This investigation puts the same spotlight on Google’s own summaries when they appear at the top of results.

The Guardian’s reporting also highlights a practical problem. The same query can produce different summaries at different times, making it harder to verify what you saw by running the search again.

Looking Ahead

Google has previously adjusted AI Overviews after viral criticism. Its response to The Guardian indicates it expects AI Overviews to be judged like other Search features, not held to a separate standard.

AI-Generated Content Isn’t The Problem, Your Strategy Is

“If AI can write, why are we still paying writers?” For any CMO or senior manager on a budget, you’ve probably already had a version of this conversation. It’s a seductive idea. After all, humans are expensive and can take hours or even days to write a single article. So, why not replace them with clever machines and watch the costs go down while productivity goes up?

It’s understandable. Buffeted by years of high inflation, high interest rates, and disrupted supply chains, organizations around the world are cutting costs wherever they can. These days, instead of “cost cutting,” CFOs and executive teams prefer the term “cost transformation,” a new jargon for the same old problem.

Whatever you call it, marketing is one department that is definitely feeling the impact. According to Gartner, in 2020, the average marketing budget was 11% of overall company revenue. By 2023, this had fallen to 9.1%. Today, the average budget is 7.7%.

Of course, some organizations will have made these cuts under the assumption that AI makes larger teams and larger budgets unnecessary. I’ve already seen some companies slash their content teams to the bone; no doubt believing that all you need is a few people capable of crafting a decent prompt. Yet a different Gartner study found that 59% of CMOs say they lack the budget to execute their 2025 strategy. I guess they didn’t get the memo.

Meanwhile, some other organizations refuse to let AI near their content at all, for a variety of reasons. They might have concerns over quality control, data privacy, complexity, and so on. Or perhaps they’re hanging onto the belief that this AI thing is a fad or a bubble, and they don’t want to implement something that might come crashing down at any moment.

Both camps likely believe they’ve adopted the correct, rational, financially prudent approach to AI. Both are dangerously wrong. AI might not be the solution, but it’s also not the problem.

Beeching’s Axe

Spanish philosopher George Santayana once wrote: “Those who cannot remember the past are condemned to repeat it.” With that in mind, let me share a cautionary tale.

In the 1960s, British Railways (later British Rail) made one of the most short-sighted decisions in transport history. With the railway network hemorrhaging money, the Conservative Government appointed Dr. Richard Beeching, a physicist from ICI with no transport experience, as the new chairman of the British Transport Commission, tasked with cutting costs and making the railways profitable.

Beeching’s solution was simple; do away with all unprofitable routes, identified by assessing the passenger numbers and operational costs of each route in isolation. Between 1963 and 1970, Beeching’s cost-cutting axe led to the closure of 2,363 stations and over 5,000 miles of track (~30% of the rail network), with the loss of 67,700 jobs.

Decades later, the country is spending billions rebuilding some of those same routes. As it turned out, many of those “unprofitable” routes were vital not only to the health of the wider rail network, but also to the communities in those regions in ways that Beeching’s team of bean counters simply didn’t have the imagination to value.

I’m telling you this because, right now, a lot of businesses are carrying out their own version of the Beeching cuts.

The Data-Led Trap

There’s a crucial distinction between being data-led and data-informed. Understanding this could be the difference between implementing a sound content production strategy and repeating Beeching’s catastrophe.

Data-led thinking treats the available data as the complete picture. It looks for a pattern and adopts it as an undeniable truth that points towards a clear course of action. “AI generates content for a fraction of our current costs. Therefore, we should replace the writers.”

Data-informed thinking sets out to understand what might be behind the pattern, extrapolate what’s missing from the picture, and stress-test the conclusions. The data becomes a starting point for inquiry, not an endpoint for decisions. “What value isn’t captured in this data? What would replacing our writers with AI actually mean for the effectiveness of our content when our competitors can do the exact the same thing with the exact same tools?”

That last question is the real challenge facing companies considering AI-generated content, but the answer won’t be found in a spreadsheet. If you can use AI to generate your content with minimal human input, so can everyone else. Very soon, everyone is generating similar content on similar topics to target the same audiences, with recycled information and reheated “insights” drawn from the same online sources.

Why would ChatGPT somehow generate a better blog post for you than for anyone else asking for 1,200 words on the same topic? It wouldn’t. You need to add your own secret sauce.

There is no competitive advantage to be gained by relying on AI-generated content alone. None.

AI-generated content is not a silver bullet. It’s the minimum benchmark your content needs to significantly exceed if your brand and your content is to have any chance of standing out in today’s noisy online marketplace.

Unfortunately, while organizations know they need to have content, far too many senior decision-makers don’t fully understand why, never mind all the things an effective content strategy needs to accomplish.

Content Isn’t A Cost, It’s An Infrastructure

Marketing content is often looked down upon as somehow easier or less worthy than other forms of writing. Yet it arguably has the hardest job of all. Every article, ebook, LinkedIn post, brochure, and landing page has to tick off a veritable to-do list of strategic requirements.

Of course, your content needs to have something to say. It must work on an informational level, backed by solid research and journalism. However, each asset or article also has a strategic role to play: attracting audiences, nurturing prospects, or converting customers, while aligning with the brand’s carefully mapped out messaging at every stage.

Your content must build authority, earn trust, and demonstrate expertise. It must be memorable enough to aid brand awareness and recall, and distinctive enough to differentiate the brand from its competitors. It must be structured for search engines with the right entities, topics, and relationships, without losing the attention of busy humans who can click away at any second. Ideally, it should also include a couple of quote-worthy lines or interesting stats capable of attracting attention when the content is distributed on social media.

ChatGPT or Claude can certainly string a bunch of convincing sentences together. But if you think they can spin all those other plates for you at the same time, and to the same standard as a skilled content creator, you’re going to be disappointed. No matter how detailed and nuanced your prompt, something will always be missing. You’re still asking AI to synthesize something brilliant by recycling what’s already out there.

Which brings me to the most ironic part of this discussion. With the rapid adoption of AI-mediated search, your content now needs to become a source that large language models will confidently cite in responses to relevant queries.

Expecting AI to create content likely to be cited by AI is like watching a dog chasing its tail: futile and frustrating. If AI provided the information and insights contained in your content, it already has better, more authoritative sources. Why would AI cite content that contains little if any fresh information or insight?

If your goal is to increase your brand’s visibility in AI responses, then your content needs to offer what can’t easily be found elsewhere.

The Limitations Of Online Knowledge

Despite appearances, AI cannot think. It cannot understand, in the sense we usually mean it. As it currently stands, it cannot reason. It certainly cannot imagine. Words like these have emerged as common euphemisms for how AI generates responses, but they also set the wrong expectations.

AI also cannot use information that isn’t already available and crawlable online. While we like to think that somehow the internet is a massive store of the entirety of human knowledge, the reality is that it’s not even close.

So much of the world we live in simply cannot be captured as structured, digitized information. While AI can tell you when and where the next local collectables market is on, it can’t tell you which dealer has that hard-to-find comic you’ve been chasing for years. That’s the kind of information you can only find out by digging through lots of comic boxes on the day.

And then there are cultural histories and localized experiences that exist more in verbal traditions than in history books. AI can tell me plenty of stuff about the First World War. But if I ask it about the Iranian famine during WW1, it’s going to struggle because it’s not that well documented outside of Iranian history books. Most of my knowledge of the famine comes almost entirely from stories my great grandma told my mother, who then passed them on to me, like how she had to survive on just one almond per day. But you won’t find her stories in any book.

How can AI draw upon the wealth of personal experience and memories we all have? The greatest source of knowledge is human. It’s us. It’s always us.

But while AI can’t do your thinking for you, it can still help in many other ways.

→ Read More: Can You Use AI To Write For YMYL Sites? (Read The Evidence Before You Do)

You Still Need A Brain Behind The Bot

Let me be clear: I use AI every day. My team uses AI every day. You should, too. The problem isn’t the tool. The problem is treating the tool as a strategy, and an efficiency or cost reduction strategy at that. Of course, it isn’t only marketing teams hoping to reduce costs and boost productivity with generative AI. Another industry has already discovered that AI doesn’t actually replace anything.

A recent survey conducted by the Australian Financial Review (AFR) found that most law firms reported using AI tools. However, far from reducing headcount, 70% of surveyed firms increased their hiring of lawyers to vet, review, and sign off on AI-generated outputs.

This isn’t a failure in their AI strategy, because the strategy was never about reducing headcount. They’re using AI tools as digital assistants (research, drafting, document handling, etc.) to free up more time and headspace for the kinds of strategic and insightful thinking that generates real business value.

Similarly, AI isn’t a like-for-like replacement for your writers, designers, and other content creators. It’s a force multiplier for them, helping your team reduce the drudgery that can so often get in the way of the real work.

  • Summarizing complex information.
  • Transcribing interviews.
  • Creating outlines.
  • Drafting related content like social media posts.
  • Checking your content against the brand style guide to catch inconsistencies.

Some writers might even use AI to generate a very rough first draft of an article to get past that blank page. The key is to treat that copy as a starting point, not the finished article.

All these tasks are massive time-savers for content creators, freeing up more of their mental bandwidth for the high-value work AI simply can’t do as well.

AI can only synthesize content from existing information. It cannot create new knowledge or come up with fresh ideas. It cannot interview subject matter experts within your business to draw out hidden wisdom and insights. It cannot draw upon personal experiences or perspectives to make your content truly yours.

AI is also riddled with algorithmic biases, potentially skewing your content and your messaging without you even realizing. For example, the majority of AI training data is in the English language, creating a huge linguistic and cultural bias. It might require an experienced and knowledgeable eye to spot the subtle hallucinations or distortions.

While AI can certainly accelerate execution, you still need skilled, experienced creatives to do the real thinking and crafting.

You Don’t Know What You Have, Until It’s Gone

Until Beeching closed the line in 1969, the route between Edinburgh and Carlisle was a vital transport artery for the Scottish Borders. On paper, the line was unprofitable, at least according to Beeching’s simplistic methodology. However, the closure had massive knock-on effects, reducing access to jobs, education, and social services, as well as impacting tourism. Meanwhile, forcing people onto buses or into cars placed greater strain on other transport infrastructures.

While Beeching might have solved one narrowly defined problem, he had undermined the broader purpose of British Railways: the mobility of people in all parts of Great Britain. In effect, Beeching had shifted the consequences and cost pressures elsewhere.

The route was partially reopened in 2015 as The Borders Railway, costing an estimated £300 million to reinstate just 30 miles of line with seven stations.

Beeching’s cuts illustrate the folly of evaluating infrastructure (or content strategy) purely on narrow, short-term financial metrics.

Organizations that cut their teams in favor of AI are likely to find it isn’t so easy to reverse course and undo the damage a few years from now. Replacing your writers with AI risks eroding the connective tissue that characterizes your content ecosystem and anchors long-term performance: authority, context, nuance, trust, and brand identity.

Experienced content creators aren’t going to wait around for organizations to realize their true value. If enough of them leave the industry, and with fewer opportunities available for the next generation of creators to gain the necessary skills and experience, the talent pool is likely to shrink massively.

As with the Beeching cuts, rebuilding your content team is likely to cost you far more in the long term than you saved in the short term, particularly when you factor in the months or years of low-performing content in the meantime.

Know What You’re Cutting Before You Wield The Axe

According to your spreadsheet, AI-generated content may well be cheaper to produce. But the effectiveness of your content strategy doesn’t hinge on whether you can publish more for less. This isn’t a case of any old content will do.

So, beware of falling into the Beeching trap. Your content workflows might only seem “loss-making” on paper because the metrics you’re looking at don’t adequately capture all the ways your content delivers strategic value to your business.

Content is not a cost center. It never was. Content is the infrastructure of your brand’s discoverability, which makes it more important than ever in the AI era.

This isn’t a debate about “human vs. AI content.” It’s about equipping skilled people with the tools to help them create work worthy of being found, cited, and trusted.

So, before you start swinging the axe, ask yourself: Are you cutting waste, or are you dismantling the very system that makes your brand visible and credible in the first place?

More Resources:


Featured Image: IM Imagery/Shutterstock

Microsoft CEO, Google Engineer Deflect AI Quality Complaints via @sejournal, @MattGSouthern

Within a week of each other, Microsoft CEO Satya Nadella and Jaana Dogan, a Principal Engineer working on Google’s Gemini API, posted comments about AI criticism that shared a theme. Both redirected attention away from whether AI output is “good” or “bad” and toward how people are reacting to the technology.

Nadella published “Looking Ahead to 2026” on his personal blog, writing that the industry needs to “get beyond the arguments of slop vs sophistication.”

Days later, Dogan posted on X that “people are only anti new tech when they are burned out from trying new tech.”

The timing coincides with Merriam-Webster naming “slop” its Word of the Year. For publishers, these statements can land less like reassurance and more like a request to stop focusing on quality.

Nadella Urges A Different Framing Than “AI Slop”

Nadella’s post argues that the conversation should move past the “slop” label and focus on how AI fits into human life and work. He characterizes AI as “cognitive amplifier tools” and believes that 2026 is the year in which AI must “prove its value in the real world.”

He writes: “We need to get beyond the arguments of slop vs sophistication,” and calls for “a new equilibrium” that accounts for humans having these tools. In the same section, he also calls it “the product design question we need to debate and answer,” which makes the point less about ending debate and more about steering it toward product integration and outcomes.

Dogan’s “Burnout” Framing Came Days After A Claude Code Post

Dogan’s post framed anti-AI sentiment as burnout from trying new technology. The line was blunt: “People are only anti new tech when they are burned out from trying new tech. It’s understandable.”

A few days earlier, Dogan had posted about using Claude Code to build a working prototype from a description of distributed agent orchestrators. She wrote that the tool produced something in about an hour that matched patterns her team had been building for roughly a year, adding: “In 2023, I believed these current capabilities were still five years away.”

Replies to the “burnout” post pushed back on Dogan. Many responses pointed to forced integrations, costs, privacy concerns, and tools that feel less reliable within everyday workflows.

Dogan is a Principal Engineer on Google’s Gemini API and is not speaking as an official representative of Google policy.

The Standards Platforms Enforce On Publishers Still Matter

I’ve written E-E-A-T guides for Search Engine Journal for years. Those pieces reflected Google’s long-running expectation that publishers demonstrate experience, expertise, and trust, especially for “Your Money or Your Life” topics like health, finance, and legal content.

That’s why the current disconnect lands so sharply for publishers. Platforms have quality standards for ranking and visibility, while AI products increasingly present information directly to users with citations that can be difficult to evaluate at a glance.

When Google executives have been asked about declining click-through rates, the public framing has included “quality clicks” rather than addressing the volume loss publishers measure on their side.

What The Traffic Data Shows

Pew Research Center tracked 68,879 real Google searches. When AI Overviews appeared, only 8% of users clicked any link, compared to 15% when AI summaries did not appear. That works out to a 46.7% drop.

Publishers can be told the remaining clicks are higher intent, but volume still matters. It’s what drives ad impressions, subscriptions, and affiliate revenue.

Separately, Similarweb data indicates that the share of news-related searches that resulted in no click-through to news sites rose from 56% to 69%.

The crawl-to-referral imbalance adds another layer. Cloudflare has estimated Google Search at about a 14:1 crawl-to-referral ratio, compared with far higher ratios for OpenAI (around 1,700:1) and Anthropic (73,000:1).

Publishers have long operated on an implicit trade where they allow crawling in exchange for distribution and traffic. Many now argue that AI features weaken that trade because content can be used to answer questions without the same level of referral back to the open web.

Why This Matters

These posts from Nadella and Dogan help show how the AI quality debate may get handled in 2026.

When people are urged to move past “slop vs sophistication” or describe criticism as burnout, the conversation can drift away from accuracy, reliability, and the economic impact on publishers.

We see clear signs of traffic declines, and the crawling-to-referral ratios are also measurable. The economic impact is real.

Looking Ahead

Keep an eye out for more messaging that frames AI criticism as a user issue rather than a product- and economics-related issue.

I’m eager to see whether these companies make any changes to their product design in response to user feedback.


Featured Image: Jack_the_sparow/Shutterstock

20 AI Prompt Ideas & Example Templates For PPC (Easy + Advanced) via @sejournal, @theshelleywalsh

AI prompts and templates can help to support PPC professionals from campaign planning to paid media reporting. So, we created a list of example prompts for you to use and adapt to your needs.

With the right prompt, tasks like creating negative keyword lists, quick ad copy variations, and summarizing reports for clients can become faster and easier. By using AI as an assistant, you can focus on the strategy and creative decision-making.

These prompt templates serve as starting points to help you scale your PPC workflows. To create an effective prompt, make sure you have:

  • Clear input: Assign it a role, be specific about the task, and outline the data you’re providing.
  • Context: Provide a background so that it understands your overall goal, not just your question.
  • Constraints: Set guardrails or structure (outlines, rulebooks, style guides, etc.) so that the result will fall within your expectations and avoid off-target answers.

Here is a list of example prompts curated by our team at Search Engine Journal to help with PPC tasks. We will be updating this on a regular basis.

Keyword Research & Planning

For all the prompts listed below, please insert your unique information in the prompt example where indicated, e.g., [INSERT …].

1. Long-Tail Keyword Expander

Generate themed keyword groups from a seed keyword for campaign structure. The task is to expand the seed keyword into 20-30 related long-tail variations grouped by search intent (informational, commercial, transactional). Include modifiers like “best,” “cheap,” “near me,” and “how to.” Prioritize keywords with buyer intent for paid search, and group similar keywords into three to five themed ad groups.

[Input Data]
Seed keyword: [INSERT MAIN KEYWORD OR PRODUCT CATEGORY] 
Target location: [INSERT LOCATION OR "NATIONWIDE"] 
Campaign objective: [INSERT "TRAFFIC", "LEADS", OR "SALES"]
[Goal Description]  Generate themed keyword groups from a seed keyword for campaign structure.
[Task Description]  Expand the seed keyword into 20–30 related long-tail variations grouped by search intent (informational, commercial, transactional). Include modifiers like "best," "cheap," "near me," and "how to." Prioritize keywords with buyer intent for paid search. Group similar keywords into 3–5 themed ad groups.
[Output Format]  Table with columns:
Ad Group Theme
Keyword List
Estimated Intent

2. Match Type Strategy Recommender

Assign the right match type to each keyword based on control and volume goals. The task is to recommend whether each keyword should use exact, phrase, or broad match based on competitiveness, intent clarity, and budget. For high-intent terms, favor exact or phrase. For discovery, suggest broad with tight negatives. Explain the tradeoff for each choice.

[Input Data]  Keywords: [INSERT LIST OF 10–15 KEYWORDS]
Campaign goal: [INSERT "AWARENESS", "CONVERSIONS", OR "ROAS TARGET"] 
Monthly budget: [INSERT BUDGET RANGE]
[Goal Description]  Assign the right match type to each keyword based on control and volume goals.
[Task Description]  Recommend whether each keyword should use exact, phrase, or broad match based on competitiveness, intent clarity, and budget. For high-intent terms, favor exact or phrase. For discovery, suggest broad with tight negatives. Explain the tradeoff for each choice.
[Output Format]  Table with columns:
Keyword
Match Type
Reasoning

3. Negative Keyword Starter List

Prevent wasted ad spend by identifying irrelevant search terms upfront. The task is to generate 15-25 negative keywords that would attract non-buyers or irrelevant clicks. Include common wastes like “free,” “jobs,” “DIY,” “tutorial,” competitor names, and terms indicating wrong intent. Explain why each negative matters for this campaign. Note that terms like “free” or “cheap” may be part of valid high-intent searches (e.g., “free shipping”), so add negative keywords selectively. The output should recommend whether each negative keyword should be phrase match or exact match.

[Input Data]  
Product/service: [INSERT CORE PRODUCT OR SERVICE] 
Industry: [INSERT INDUSTRY OR VERTICAL] 
Bidding on: [INSERT KEYWORDS YOU'RE BIDDING ON]
[Goal Description]  Prevent wasted ad spend by identifying irrelevant search terms upfront.
[Task Description]  Generate 15–25 negative keywords that would attract non-buyers or irrelevant clicks. Include common wastes like "free," "jobs," "DIY," "tutorial," competitor names, and terms indicating wrong intent. Explain why each negative matters for this campaign.
Note:  Terms like “free” or “cheap” may be part of valid high-intent searches (e.g., “free shipping”). Add negative keywords selectively.
Match type guidance:  Recommend whether each negative keyword should be phrase match or exact match, depending on how tightly the search term should be blocked.
[Output Format]  Three-column list:
| Negative Keyword | Match Type | Reason to Exclude |

Ad Copywriting & Testing

4. RSA Asset Generator (Google Ads)

Create diverse responsive search ad assets optimized for testing. The task is to write 10 unique headlines (30 characters max) and four descriptions (90 characters max) that mix emotional hooks, feature callouts, urgency, and social proof. Include at least one headline with a number or stat, and ensure assets can combine in any order without repetition or contradiction. The Google Ads recommendation is to provide at least five unique headlines to reach “Good” Ad Strength.

[Input Data]  Product/service: [INSERT PRODUCT/SERVICE NAME]
Benefits/features: [INSERT TOP 3 BENEFITS OR FEATURES] 
Call-to-action: [INSERT PRIMARY CTA]
[Goal Description]  Create diverse responsive search ad assets optimized for testing.
[Task Description]  Write 10 unique headlines (30 characters max) and 4 descriptions (90 characters max) that mix emotional hooks, feature callouts, urgency, and social proof. Include at least one headline with a number or stat. Ensure assets can combine in any order without repetition or contradiction. 
Note:  Pinning assets can reduce Ad Strength. Pin only when required for compliance.
Google Ads Recommendation:  Provide at least  5 unique headlines  to reach “Good” Ad Strength. Including 10 or more can help increase variation and improve performance.
Tip:  When appropriate, test Dynamic Keyword Insertion (DKI) to match ads more closely to user search intent.
[Output Format]  Two sections:
Headlines (numbered 1–10)
Descriptions (A–D) 

5. RSA Asset Mixer (Google Ads)

Turn features, benefits, and CTAs into testable responsive search ad components. The task is to generate 12 headlines and four descriptions by mixing and matching the provided benefits, features, and CTAs. Vary the messaging style across emotional appeal, logical reasoning, urgency, and social proof. Keep all copy within Google Ads character limits and ensure combinations work together seamlessly. The Google Ads recommendation is to provide at least five unique headlines to reach “Good” Ad Strength.

[Input Data]  
Product benefits: [INSERT LIST OF 3–5 BENEFITS] 
Product features: [INSERT LIST OF 3–5 FEATURES] 
CTAs: [INSERT 2–3 PREFERRED CTAS]
[Goal Description]  Turn features, benefits, and CTAs into testable responsive search ad components.
[Task Description]  Generate 12 headlines and 4 descriptions by mixing and matching the provided benefits, features, and CTAs. Vary the messaging style across emotional appeal, logical reasoning, urgency, and social proof. Keep all copy within Google Ads character limits and ensure combinations work together seamlessly.
Note:  Pinning assets can reduce Ad Strength. Pin only when required for compliance.
Google Ads Recommendation:  Provide at least  5 unique headlines  to reach “Good” Ad Strength. Including 10 or more can help increase variation and improve performance.
Tip:  When appropriate, test Dynamic Keyword Insertion (DKI) to match ads more closely to user search intent.
[Output Format]  Two sections:
Headlines (numbered 1–12)
Descriptions (A–D) 

6. Ad Angle Brainstorming Tool

Discover fresh messaging angles to test against current ads. The task is to generate six alternative ad angles, such as scarcity, authority, pain/solution, comparison, guarantee, or transformation. For each angle, write one sample headline and explain when to use it, avoiding repetition of the current ad’s approach.

[Input Data]  Current ad copy: [INSERT TOP-PERFORMING AD COPY] 
Product details: [INSERT PRODUCT OR SERVICE DETAILS] 
Audience pain points: [INSERT TARGET AUDIENCE PAIN POINTS]
[Goal Description]  Discover fresh messaging angles to test against current ads.
[Task Description]  Generate 6 alternative ad angles such as scarcity, authority, pain/solution, comparison, guarantee, or transformation. For each angle, write one sample headline and explain when to use it. Avoid repeating the current ad's approach.
[Output Format]  Table with columns:
Angle Type
Sample Headline
Best Use Case

Audiences & Targeting

7. Audience Segment Hypothesis Builder

Draft testable audience segments with conversion rationale. The task is to propose four to six audience segments (e.g., in-market, affinity, custom intent, remarketing) with clear definitions. For each, explain why they’re likely to convert and suggest initial bid adjustments (raise/lower/neutral). Prioritize audiences with historical relevance if mentioned.

[Input Data]  Product/service: [INSERT PRODUCT OR SERVICE OFFERING]
 Customer data: [INSERT KNOWN DEMOGRAPHICS OR BEHAVIORS] 
Campaign goal: [INSERT "AWARENESS", "CONSIDERATION", OR "PURCHASE"]
[Goal Description]  Draft testable audience segments with conversion rationale.
[Task Description]  Propose 4–6 audience segments (e.g., in-market, affinity, custom intent, remarketing) with clear definitions. For each, explain why they're likely to convert and suggest initial bid adjustments (raise/lower/neutral). Prioritize audiences with historical relevance if mentioned.
[Output Format]  Table with columns:
Audience Name
Definition
Why It Converts
Bid Adjustment

8. Keyword-To-Funnel Stage Mapper

Align keywords with buyer journey stages for smarter targeting. The task is to categorize each keyword as cold (informational), warm (comparison/research), or hot (ready to buy). The output should recommend which keywords deserve higher bids, tighter targeting, or special landing pages, and flag any keywords that may need remarketing support.

[Input Data]  
Keywords: [INSERT LIST OF 10–20 PERFORMING KEYWORDS]
Customer journey: [INSERT TYPICAL JOURNEY: AWARENESS → DECISION] 
Conversion goal: [INSERT "LEAD", "SALE", OR "SIGNUP"]
[Goal Description]  Align keywords with buyer journey stages for smarter targeting.
[Task Description]  Categorize each keyword as cold (informational), warm (comparison/research), or hot (ready to buy). Recommend which keywords deserve higher bids, tighter targeting, or special landing pages. Flag any keywords that may need remarketing support.
[Output Format]  Table with columns:
Keyword
Funnel Stage
Bidding Priority
Notes

Bidding & Budget

9. Bidding Strategy Selector

Recommend the right automated or manual bidding strategy. The task is to suggest whether to use manual CPC, maximize clicks, target CPA, target ROAS, or maximize conversions, explaining which strategy fits based on data maturity and control needs. Include one caution or condition for each option, noting that Target CPA and Target ROAS work best with around 30-50 recent conversions.

[Input Data]  
Campaign goal: [INSERT "CLICKS", "CONVERSIONS", OR "ROAS"] 
Conversion volume: [INSERT DAILY OR WEEKLY CONVERSION NUMBERS] 
Budget: [INSERT BUDGET SIZE AND FLEXIBILITY]
[Goal Description]  Recommend the right automated or manual bidding strategy.
[Task Description]  Suggest whether to use manual CPC, maximize clicks, target CPA, target ROAS, or maximize conversions. Explain which strategy fits based on data maturity and control needs. Include one caution or condition for each option. 
Note: Target CPA and Target ROAS work best when the campaign has enough recent conversions (around 30–50 in the last 30 days). Low-volume campaigns may not perform well with these automated bidding strategies.
[Output Format]  Table with columns:
Strategy
Best For
Caution

10. Campaign Budget Allocator

Split a fixed budget across campaigns based on priority and performance. The task is to allocate budget percentages to each campaign based on historical ROI, strategic priority, and growth potential. The output should recommend higher spend for proven converters and testing budgets for new initiatives, justifying each split with one sentence. The prompt also reminds the user to consider daily pacing rules and portfolio bidding strategies.

[Input Data]  Total budget: [INSERT TOTAL MONTHLY BUDGET] 
Campaigns: [INSERT LIST OF 3–6 CAMPAIGNS WITH GOALS]
Performance data: [INSERT PAST ROAS OR CPA PER CAMPAIGN, IF AVAILABLE]
[Goal Description]  Split a fixed budget across campaigns based on priority and performance.
[Task Description]  Allocate budget percentages to each campaign based on historical ROI, strategic priority, and growth potential. Recommend higher spend for proven converters and testing budgets for new initiatives. Justify each split with one sentence.
Google may exceed daily budgets by up to ~15 percent due to daily pacing rules.
Consider whether shared budgets or portfolio bidding strategies apply across your campaigns.
[Output Format]  Table with columns:
Campaign
Budget %
Amount
Reasoning

Search Query Mining

11. Search Term Negative Identifier

Clean up search query reports by flagging wasteful terms. The task is to review the search terms and identify five to 10 that should be added as negatives. The prompt asks the user to look for irrelevant intent, low commercial value, or terms triggering ads incorrectly, explaining why each term wastes spend and suggesting the correct match type (phrase or exact negative).

[Input Data]  Search terms: [INSERT LIST OF 20–30 RECENT SEARCH TERMS] 
Performance data: [INSERT COST AND CONVERSION DATA, IF AVAILABLE] 
Campaign objective: [INSERT CAMPAIGN OBJECTIVE]
[Goal Description]  Clean up search query reports by flagging wasteful terms.
[Task Description]  Review the search terms and identify 5–10 that should be added as negatives. Look for irrelevant intent, low commercial value, or terms triggering ads incorrectly. Explain why each term wastes spend and suggest match type (phrase or exact negative).
[Output Format]  Table with columns:
Search Term
Add as Negative?
Reason
Match Type

12. High-Opportunity Query Promoter

Find search queries worth promoting to dedicated keywords or ad groups. The task is to identify three to five search queries with strong click-through rate or conversion rate that aren’t yet standalone keywords. The output should recommend promoting them to exact or phrase match with custom ad copy, and estimate the potential impact if given more budget and ad relevance.

[Input Data]  
Search term report: [INSERT REPORT WITH IMPRESSIONS AND CONVERSIONS] 
Current keywords: [INSERT CURRENT KEYWORD LIST]
Budget availability: [INSERT BUDGET AVAILABILITY]
[Goal Description]  Find search queries worth promoting to dedicated keywords or ad groups.
[Task Description]  Identify 3–5 search queries with strong CTR or conversion rate that aren't yet standalone keywords. Recommend promoting them to exact or phrase match with custom ad copy. Estimate potential impact if given more budget and ad relevance.
[Output Format]  Table with columns:
Query
Current Performance
Promotion Recommendation
Expected Lift

Landing Pages & CRO

13. Ad-To-Page Relevance Checker

Spot mismatches between ad promises and landing page content. The task is to compare the ad’s main claim with the landing page headline, imagery, and CTA, flagging any gaps where the page doesn’t deliver on the ad’s promise. The output should suggest two to three quick fixes to improve message match and reduce bounce rate. Note that the AI cannot visit URLs, so the user must paste the landing page text.

[Input Data]  
Ad copy: [INSERT AD HEADLINE AND DESCRIPTION]
Landing page: [INSERT LANDING PAGE URL OR SUMMARY] 
Conversion goal: [INSERT PRIMARY CONVERSION GOAL]
Note:  AI cannot visit URLs unless a browsing tool is enabled. Paste the landing page text instead.
[Goal Description]  Spot mismatches between ad promises and landing page content.
[Task Description]  Compare the ad's main claim with the landing page headline, imagery, and CTA. Flag any gaps where the page doesn't deliver on the ad's promise. Suggest 2–3 quick fixes to improve message match and reduce bounce rate.
[Output Format]  Report with:
Summary paragraph
Bulleted list of gaps and fixes

14. Landing Page CTA Optimizer

Create clear, compelling CTAs aligned with each ad angle. The task is to propose three CTA options that match the ad’s tone and promise. One option should emphasize urgency, one should reduce friction, and one should reinforce value, keeping CTAs short (two to five words) and action-oriented.

[Input Data]
Ad angle:  [INSERT AD MESSAGING OR ANGLE]
Offer type:  [INSERT PRODUCT/SERVICE AND OFFER TYPE]
Desired action:  [INSERT "SIGN UP", "BUY", OR "CALL"]
Landing page details:  [PASTE TEXT, SUMMARY, OR UPLOAD A SCREENSHOT OF THE LANDING PAGE]
[Goal Description]  Create clear, compelling CTAs aligned with each ad angle.
[Task Description]  Propose 3 CTA options that match the ad's tone and promise. One should emphasize urgency, one should reduce friction, and one should reinforce value. Keep CTAs short (2–5 words) and action-oriented.
[Output Format]  Numbered list with:
CTA text
Brief explanation for each

Reporting & Insights

15. Client-Friendly Performance Snapshot

Turn raw metrics into a one-slide summary clients actually understand. The task is to write a three-to-four-sentence narrative explaining overall performance, highlighting wins and flags. The summary must include one insight about what’s working and one recommendation for next steps, keeping the language simple and avoiding jargon.

[Input Data]  
Current metrics: [INSERT CTR, CPC, CONVERSION RATE, AND CPA]
 Spend data: [INSERT BUDGET SPENT AND CONVERSIONS DELIVERED]
Comparison period: [INSERT "LAST MONTH", "LAST QUARTER", ETC.]
[Goal Description]  Turn raw metrics into a one-slide summary clients actually understand.
[Task Description]  Write a 3–4 sentence narrative explaining overall performance, highlighting wins and flags. Include one insight about what's working and one recommendation for next steps. Keep language simple and avoid jargon.
[Output Format]  Report with:
Short paragraph summary
2–3 key takeaway bullets

16. Metric Change Explainer

Translate performance shifts into clear, actionable insights. The task is to write three to five sentences explaining why the metric changed, considering factors like competition, bid adjustments, ad fatigue, seasonality, targeting shifts, or platform changes. The explanation must end with one recommended action to sustain gains or fix declines.

[Input Data]  
Metric changed: [INSERT "CTR", "CPC", OR "CONVERSIONS"] 
Values: [INSERT BEFORE AND AFTER VALUES] 
Context: [INSERT SEASONALITY, CHANGES MADE, OR EXTERNAL FACTORS]
[Goal Description]  Translate performance shifts into clear, actionable insights.
[Task Description]  Write 3–5 sentences explaining why the metric changed. Consider factors like competition, bid adjustments, ad fatigue, seasonality, or targeting shifts. End with one recommended action to sustain gains or fix declines. 
Also consider platform changes such as Google algorithm updates or privacy-related shifts (e.g., iOS 14.5 on Meta), which commonly impact performance metrics.
[Output Format]  Short paragraph formatted for reporting or client email

Competitive Analysis

17. Competitor Ad Messaging Scanner

Summarize competitor ad strategies to find differentiation opportunities. The task is to analyze competitor ads for recurring themes, offers, CTAs, and emotional triggers. The output should identify two to three messaging gaps or angles competitors aren’t using and suggest how to position your ads differently while staying relevant to searcher intent.

[Input Data]  
Competitor ads: [INSERT 3–5 AD EXAMPLES WITH HEADLINES AND DESCRIPTIONS] 
Your product: [INSERT YOUR PRODUCT OR SERVICE] 
USPs: [INSERT YOUR UNIQUE SELLING POINTS]
[Goal Description]  Summarize competitor ad strategies to find differentiation opportunities.
[Task Description]  Analyze competitor ads for recurring themes, offers, CTAs, and emotional triggers. Identify 2–3 messaging gaps or angles competitors aren't using. Suggest how to position your ads differently while staying relevant to searcher intent.
[Output Format]  Report with:
Summary paragraph
Bulleted list of differentiation ideas

18. Gaps & Differentiators Finder

Identify unique value propositions competitors aren’t claiming. The task is to list four to six ad angles, offers, or value props that your brand can own but competitors aren’t emphasizing. The focus should be on authentic differentiators like guarantees, speed, customization, support quality, or niche expertise, with an explanation of why each gap matters to buyers.

[Input Data]  
Your features: [INSERT PRODUCT/SERVICE FEATURES AND BENEFITS] 
Competitor messaging: [INSERT THEMES FROM COMPETITOR ADS OR WEBSITES] 
Audience needs: [INSERT TARGET AUDIENCE NEEDS OR PAIN POINTS]
[Goal Description]  Identify unique value propositions competitors aren't claiming.
[Task Description]  List 4–6 ad angles, offers, or value props that your brand can own but competitors aren't emphasizing. Focus on authentic differentiators like guarantees, speed, customization, support quality, or niche expertise. Explain why each gap matters to buyers.
[Output Format]  Table with columns:
Differentiator
Why Competitors Miss It
Buyer Appeal

Advanced PPC Prompts

19. Enhanced PPC Keyword Research Suggestion Prompt

This advanced prompt template is designed to help a PPC keyword research specialist build a comprehensive and high-performing keyword strategy. It guides the model through keyword discovery, match type strategy, negative keyword generation, and campaign organization.

You are a PPC keyword research specialist. Help me build a high-performing keyword strategy.
Campaign Context
Product/Service:  [DESCRIBE WHAT YOU'RE ADVERTISING]
Landing Page URL:  [YOUR LANDING PAGE]
Target Audience:  [WHO ARE YOUR CUSTOMERS]
Campaign Goal:  [LEADS/SALES/BRAND AWARENESS]
Monthly Budget:  [YOUR BUDGET]
Geographic Target:  [LOCATION IF APPLICABLE]

Task 1: Keyword Discovery & Expansion
Generate 25-30 keywords organized into  4 keyword categories :
A) Brand Terms  - Keywords with my brand name  B) Generic Terms  - Product/service related keywords  C) Related Terms  - Adjacent topics my audience searches for  D) Competitor Terms  - Major competitor brand names (if budget allows)
For each keyword:
Include  long-tail variations  (5+ words) - these are less competitive and convert better
Add  synonyms and variations  (plurals, abbreviations, alternate spellings)
Consider  voice search patterns  (how people speak vs type): "where can I find...", "what's the best...", "how do I..."
Balance  broad terms  (high volume) with  specific terms  (high intent)
Output as:
BRAND TERMS: 
- [keyword 1] 
- [keyword 2] 

GENERIC TERMS: 
- [keyword 1] 
- [long-tail variation] 

RELATED TERMS: 
- [keyword 1] 

COMPETITOR TERMS: 
- [keyword 1] 

Task 2: Match Type Strategy
For each keyword group, assign match types with reasoning:
Match Type Logic:
Exact Match  [keyword] = Highest intent, tight control, proven converters
Phrase Match  "keyword" = Moderate intent, balanced reach & control
Broad Match:  Uses Smart Bidding signals and works best when you have accurate conversion tracking and consistent conversion volume. Avoid Broad Match if you don’t have enough conversion data or if Smart Bidding isn’t enabled.
Include estimated: 
Competition level (High/Medium/Low)
Identify the  "sweet spot" keywords  (high volume + low competition)
Output as table:
| Keyword | Match Type | Competition | Why This Match Type | 
|---------|-----------|--------|-------------|---------------------| 

Task 3: Negative Keywords
Generate 15-20 negative keywords in these categories:
Common Categories:
Job/Career terms (jobs, hiring, salary, career)
Free/Cheap terms (free, cheap, discount) -  unless you sell budget products
DIY/How-to (tutorial, diy) -  unless you offer educational content
Wrong intent terms (specific to your industry)
Competitor names (if not running conquest campaigns)
Output as:
Job-Related: [terms] 
Cost-Related: [terms]  
Wrong Audience: [terms] 
[Other Category]: [terms] 

Task 4: Organization & Structure
Group keywords into  tight, focused ad groups  that mirror my website structure. Each ad group should contain 5-15 closely related keywords.
Example structure:
Campaign: [Product Category] 
|---  Ad Group 1: [Specific Product A] 
|     |---  Keywords: [5-15 related terms] 
|---  Ad Group 2: [Specific Product B] 
|     |---  Keywords: [5-15 related terms] 
Important Guidelines:
Think like the customer  - What would THEY type to find my product?
Prioritize long-tail keywords  - "women's black running shoes size 8" converts better than "shoes"
Flag any trademark concerns  in competitor keywords
Explain your reasoning  for each recommendation step-by-step
Identify quick wins  - keywords I should bid on immediately
Note ongoing optimization  - this is an iterative process, not one-and-done
Show your work and explain the logic behind each recommendation.

20. Enhanced Funnel-Based Ad Copy Generator

This advanced prompt template instructs a PPC copywriting expert to create high-performing ad copy for responsive search ads, Meta, and LinkedIn, specifically optimized for different customer journey stages (top, middle, bottom of funnel).

Your Role
You are a PPC copywriting expert specializing in Google Ads responsive search ads, Meta ads, and LinkedIn ads. Create high-performing ad copy optimized for different customer journey stages.
What I Need From You
Before starting, collect this campaign context:
Product/Service:  [DESCRIBE WHAT YOU’RE ADVERTISING]
Target Audience:  [WHO YOU’RE REACHING]
Funnel Stage:  [TOP, MIDDLE, BOTTOM, OR ALL THREE]
Platform:  [GOOGLE ADS, FACEBOOK/INSTAGRAM, OR LINKEDIN]
Unique Differentiator:  [WHAT SETS YOU APART]
Keywords (Google Ads only):  [ANY MUST-INCLUDE TERMS]
Context:  [DESCRIBE GOAL, SEASONALITY, PROMO PERIODS, TIME-SENSITIVE EVENTS]

The 3 Funnel Stages Explained
Top of Funnel (Awareness)
Audience: Just learning about the problem or category Goal: Educate and grab attention Tone: Helpful, curious, no pressure Copy Focus: Problem-focused, educational content CTA Style: Soft (Learn More, Discover, See How) Example: “Struggling with data security? Learn the top 5 risks.”
Middle of Funnel (Consideration)
Audience: Comparing solutions, evaluating options Goal: Show differentiation and build trust Tone: Trustworthy, confident, proof-driven Copy Focus: Benefits over features, social proof, comparisons CTA Style: Moderate (Try Free, Compare, Get Demo) Example: “Join 10,000+ teams using our platform. See why we’re rated #1.”
Bottom of Funnel (Conversion)
Audience: Ready to buy, needs final push Goal: Drive immediate action Tone: Direct, urgent, action-oriented Copy Focus: Specific offers, risk removal, time sensitivity CTA Style: Strong (Start Now, Buy Today, Get Started Free) Example: “Start your free trial today. No credit card required.”

Google Ads Responsive Search Ads Requirements
CRITICAL for Google Ads:
Provide at least 10–15 unique headlines (max 15)
Provide at least 4 unique descriptions (max 4)
Include keyword variations in multiple headlines
Vary headline lengths (short, medium, long)
Aim for “Good” or “Excellent” Ad Strength
Google Ads recommendation:  Include at least 5 unique headlines to reach “Good” Ad Strength
Tie headlines to user search intent and keywords
Focus on user benefits, not just features
Tip:  When appropriate, test Dynamic Keyword Insertion (DKI) to match ads more closely to user search intent.
Why:  Google’s ad systems test combinations automatically, and improving Ad Strength helps the system find higher-performing variations. According to Google Ads Help (“About the customer journey,” 2024), advertisers who improve Ad Strength from “Poor” to “Excellent” see  12% more conversions on average .

Core Copywriting Principles
User Benefits First  * “Save 3 hours per day on admin tasks” X “Advanced automation features”
Keyword Integration (Google Ads)  Include target keywords naturally in headlines. Align copy with user search intent.
Specificity Over Generic  * “Get results in 10 minutes or less” X “Get fast results”
Social Proof & Trust  Use proof points: “10,000+ customers,” “4.9/5 rating,” “Used by Fortune 500.”
Remove Friction (BOFU)  Examples: “No credit card needed,” “Cancel anytime,” “30-day money-back guarantee.”
Test Different Angles  Try emotional vs. rational, question vs. statement, offer vs. value, short vs. long.

Output Format
For Google Responsive Search Ads:
Headlines (10–15):
[30 chars max – keyword-rich, benefit-focused]
[30 chars max – social proof angle]
[30 chars max – specific benefit]
[Short, punchy angle]
[Question format]
6–15. Continue with unique angles
Descriptions (4):
[90 chars – primary value proposition]
[90 chars – differentiation + CTA]
[90 chars – social proof + benefit]
[90 chars – risk removal + urgency]
Expected Ad Strength: [Good/Excellent] Primary Keywords Included: [List]

For Meta Ads (Facebook/Instagram)
Headlines (3–5):
[40 characters max]
Primary Text (2–3 variations):
[First 125 characters should include the key message]  Note:  Meta primary text often truncates after ~125 characters on mobile (“See More” appears).
Call-to-Action Button:
[Platform CTA option]

For LinkedIn Ads
Headlines (3 variations):
[70 chars recommended, 200 max]
Descriptions (2 variations):
[150 chars focus, up to 600 max for Sponsored Content; other formats may differ]

Character Limits Reference

Platform ,Headline ,Description 
Google Search,30 chars (15 headlines max),90 chars (4 descriptions max)
Facebook/Instagram,40 chars max,125 chars primary text
LinkedIn,70 chars (200 max),150 chars focus (600 max Sponsored Content)


Power Words by Stage
Top Funnel:  Discover, Learn, Guide, Free, Simple, Understand  Middle Funnel:  Proven, Trusted, Compare, Better, Results  Bottom Funnel:  Now, Today, Get, Start, Instant, Guaranteed

Common Testing Frameworks
Discount vs. Value
Urgency vs. Evergreen
Question vs. Statement
Short vs. Long
Emotional vs. Rational

Quality Checklist
* Unique headlines
* Keywords included (Google Ads)
* Clear benefits
* Specific proof
* Correct character limits
* Funnel alignment
* Strong CTAs
* “Good” or better Ad Strength

Example Request
“Create Google responsive search ads for my CRM software targeting small business owners at the bottom of funnel. Target keywords: ‘CRM software,’ ‘customer management tool,’ ‘sales tracking software.’ Differentiator: 50% cheaper than Salesforce with the same features. Include a free 14-day trial.”

Keep Refining Your Prompts As Models Evolve

Good prompts don’t stay good forever. AI models will keep evolving, and the way they interpret your instructions will update, too. This means that refining your prompts is an ongoing process to stay aligned with how modern LLMs work. Our in-house LLM expert, Brent Csutoras, stresses that prompting today is less about how you phrase things and more about understanding how the machine interprets your instructions.

Brent puts it bluntly:

“As much as this might feel like a human … you’re talking to a machine. The problem you have is that you are asking a prediction engine to give you the answer it thinks you want based on some rules that you’ve given it.”

He also warns that the structure of your prompt changes how the model behaves:

“The way your prompt is structured and the way you type it actually has a massive effect on how your output’s going to come. It will skip certain things and ignore certain things, if it’s not written well.”

So, instead of treating prompts as fixed templates, treat them as living documents. Every time you revise output, ask your model where your prompt caused confusion and how it would rewrite the instructions to avoid that issue in the future. Over time, this becomes a feedback loop where the model helps refine the instructions you give it. Brent even updates his own prompts monthly for this reason.

To sum it all up, it’s important to keep testing, adjusting, and pressure-checking your prompts. Here’s his advice to make your prompts sharp and reliable:

How To Audit And Improve Your Prompts

  • Cross-model testing: Run prompts across ChatGPT, Claude, and others. Ask each model what it would change about your prompt.
  • Self-critique loops: Ask the AI how it interpreted your instructions, which steps it skipped, and where it found conflicts.
  • Priority mapping: Have the AI list the steps in your prompt in the order it believes they matter most. This shows you how it “reads” your request.
  • Project-based prompting with artifacts: Build structured projects where instructions, templates, tone guides, product docs, and datasets are predefined. Models stay consistent because they draw from the same controlled materials every time.
  • Data filtering: Remove emotional language or subjective tone from research inputs before adding them to a project. Cleaner data produces cleaner output.
  • Continuous improvement: Regularly ask the AI to adjust your instructions based on your edits. Update your prompt monthly to keep it evolving with your workflow.

We will be updating this list on a regular basis with more prompt ideas and examples to make your PPC more efficient.

Disclaimer: These PPC-focused prompts are not designed to be “one-size-fits-all” because results generated may contain inaccuracies or incomplete data. Always fact-check your outputs against primary sources, review for compliance and accuracy.

More Resources:


Featured Image: ImageFlow/Shutterstock

The New AI Marketplace: How ChatGPT’s Native Shopping Could Rewrite Digital Commerce via @sejournal, @gregjarboe

When OpenAI quietly added native shopping to ChatGPT – alongside a partnership with Walmart – it marked more than another AI feature rollout. It signaled a fundamental shift in how consumers discover, compare, and purchase products online.

For the first time, shoppers can browse and buy directly inside an AI conversation – no search results, no scrolling, and no marketplace middleman.

To understand what this means for the future of search, marketplaces, and digital marketing, I spoke with Tim Vanderhook, CEO of Viant Technology, who recently shared his perspective on LinkedIn. Vanderhook believes this move could redefine the entire digital commerce ecosystem, breaking down the “gatekeeper dynamic” that platforms like Amazon and Google have long relied on.

In this direct conversation, he explains why LLM-powered shopping could reshape the funnel, rewrite the rules of attribution, and open the door to a new kind of AI-native marketplace.

The Beginning Of A New Marketplace

Greg Jarboe: You called this “the beginning of an exciting new kind of marketplace.” How do you see LLM-powered commerce evolving over the next few years, and what will make it fundamentally different from search- or marketplace-driven models like Google or Amazon?

Tim Vanderhook: We see LLM-powered commerce as a foundational shift, not just in how people discover, but in how they interact with products, services, and brands. Traditionally, platforms like Google, Amazon, or Walmart served as digital commerce gatekeepers, where visibility is controlled by rankings, algorithms, or marketplace dynamics. In an LLM-powered future, the interface becomes conversational, personalized, and far more dynamic.

This model re-centers discovery around intent, not just keywords. Rather than a one-size-fits-all search result, consumers will have AI-driven shopping assistants that understand context, including where, when, why, and for whom they’re buying. This collapses the “search → click → checkout” funnel into a single, intelligent conversation.

For marketers, that means success will be driven by the quality of engagement and product fit, not just ad spend or ranking. In many ways, it’s the inverse of the search economy: Instead of bidding for space, brands will need to earn their way into relevance via storytelling, brand-building, and trust.

Breaking Down The Gatekeepers

Greg Jarboe: You wrote that OpenAI’s move could “break down the gatekeeper dynamic” that Amazon, Walmart, and others rely on. Is this the start of a real power shift in digital commerce? Or will the incumbents adapt fast enough through partnerships and integrations to maintain their dominance?

Tim Vanderhook: Absolutely, and it’s already underway. Legacy players like Amazon have long benefited from their control of both inventory and discovery. That changes when the discovery interface shifts from their search bars to independent, intelligent LLMs like ChatGPT.

That said, don’t count them out. These incumbents have built massive infrastructure and trust. Many will adapt – and fast – by integrating with LLMs or embedding their services into new ecosystems. But the power dynamic will shift: from owning the funnel to participating in a more open, orchestrated marketplace.

In that new environment, the advantage goes to whoever can deliver the best outcome, not just whoever owns the shelf.

The New Role Of Brands And Marketers

Greg Jarboe: If the LLM becomes the new interface for discovery and transactions, what does that mean for brands and marketers? How should they rethink SEO, paid media, and retail media strategies when product visibility depends on conversational AI rather than rankings or ad placements?

Tim Vanderhook: It’s a seismic change. When product discovery becomes conversational and personalized – not driven by static rankings or paid placements – traditional media strategies need a new playbook. Brands must optimize not just for keywords, but for context. That will elevate the importance of full funnel advertising, tailoring paid media strategies around intent and ensuring retail media campaigns can be activated, optimized, and measured in real time.

And in an LLM-driven world, one of the only ways to guarantee visibility is to be the brand consumers ask for by name. Most marketers still spend nearly 70% of their paid ad budgets on channels like search and social that harvest existing intent or “Demand Capture” and only 30% ad spend on long-term brand-building channels like Connected TV and streaming audio that drive real “Demand Generation” and new business growth. That ratio made sense in a keyword-driven world. But in an AI-driven one, marketers have the power to shape the very conversations that define their brands.

The brands people already know and trust are the ones most likely to appear in an LLM’s response. The companies that win in the LLM era will flip that script, and invest MORE in brand, in CTV, in storytelling, the work that generates demand before the consumer ever types (or prompts) a query. In this new landscape, brand storytelling becomes a visibility strategy.

Partnerships Now, Disintermediation Later

Greg Jarboe: You mentioned that in the short term, marketplaces will partner with OpenAI, but in the long term, OpenAI won’t need them. What incentives or business models could sustain those partnerships – and what happens when smaller retailers can plug in directly to ChatGPT?

Tim Vanderhook: In the short term, it’s symbiotic. Marketplaces provide supply, fulfillment, and customer trust – things LLMs need to deliver on the last mile. OpenAI provides access to intent at scale. Both sides benefit.

But long-term, LLMs could grow to be able to connect directly with retailers, cutting out the middle layers. That creates new business models. Think “preferred placement” fees in conversations, affiliate commissions, or verified product data partnerships.

Smaller retailers especially stand to benefit. They’ve historically lacked the ability to compete on page one of Amazon or Google. In a conversational model, they can plug into the system via APIs and win on merit, product value, or relevance – not just ad spend.

The Future Of Attribution And Advertising

Greg Jarboe: How does AI-native commerce change the way marketers should approach attribution, targeting, and customer acquisition when the “search” and “purchase” phases collapse into one step?

Tim Vanderhook: In an AI-native model, the traditional funnel collapses. Search and purchase happen in the same moment, so attribution must evolve. Brands need systems that can measure the full path from prompt to purchase, across channels and devices.

In this new world, marketers must stop chasing last-click metrics and start optimizing for true incrementality. What drove the purchase intent in the first place? How can we replicate that upstream influence? That’s the future, and we’re building for it now.

Trust, Transparency, And Brand Safety

Greg Jarboe: If ChatGPT becomes a transactional interface, how will issues like brand safety, product authenticity, and trust be handled? Will consumers rely on AI-driven recommendations the same way they currently rely on ratings and reviews?

Tim Vanderhook: They will, if and only if, the system earns that trust. That’s why brand safety, transparency, and authenticated data will be non-negotiable.

LLMs will need accountability controls: where the product came from, how it was vetted, and whether it’s real. They’ll need to show their reasoning, not just “what,” but “why.” Consumers are already skeptical of black-box recommendations. AI must be explainable and accountable.

For brands, this means owning your presence in the AI ecosystem. Provide structured data. Ensure your offers and inventory are verifiable. And align with partners who take identity, measurement, and integrity seriously.

As AI reshapes the interface of commerce, I believe those values will only become more essential.

What Marketers Should Do Next

As Vanderhook points out, the rise of LLM-driven shopping doesn’t just introduce another channel – it redefines how intent, discovery, and conversion intersect. For marketers, that means preparing for a world where visibility depends less on search rankings or ad placements and more on how effectively your data, product information, and brand trust are integrated into AI ecosystems.

The winners in this new landscape won’t be those who chase algorithms, but those who make their brands intelligible – and indispensable – to intelligent systems.

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Featured Image: SvetaZi/Shutterstock

Ahrefs Tested AI Misinformation, But Proved Something Else via @sejournal, @martinibuster

Ahrefs tested how AI systems behave when they’re prompted with conflicting and fabricated information about a brand. The company created a website for a fictional business, seeded conflicting articles about it across the web, and then watched how different AI platforms responded to questions about the fictional brand. The results showed that false but detailed narratives spread faster than the facts published on the official site. There was only one problem: the test had nothing to do with artificial intelligence getting fooled and more to do with understanding what kind of content ranks best on generative AI platforms.

1. No Official Brand Website

Ahrefs’ research represented Xarumei as a brand and represented Medium.com, Reddit, and the Weighty Thoughts blog as third-party websites.

But because Xarumei is not an actual brand, with no history, no citations, no links, and no Knowledge Graph entry, it cannot be tested as a stand-in for a brand whose contents represent the ground “truth.”

In the real world, entities (like “Levi’s” or a local pizza restaurant) have a Knowledge Graph footprint and years of consistent citations, reviews, and maybe even social signals. Xarumei existed in a vacuum. It had no history, no consensus, and no external validation.

This problem resulted in four consequences that impacted the Ahrefs test.

Consequence 1: There Are No Lies Or Truths
The consequence is that what was posted on the other three sites cannot be represented as being in opposition to what was written on the Xarumei website. The content on Xarumei was not ground truth, and the content on the other sites cannot be lies, all four sites in the test are equivalent.

Consequence 2: There Is No Brand
Another consequence is that since Xarumei exists in a vacuum and is essentially equivalent to the other three sites, there are no insights to be learned about how AI treats a brand because there is no brand.

Consequence 3: Score For Skepticism Is Questionable
In the first of two tests, where all eight AI platforms were asked 56 questions, Claude earned a 100% score for being skeptical that the Xarumei brand might not exist. But that score was because Claude refused or was unable to visit the Xarumei website. The score of 100% for being skeptical of the Xarumei brand could be seen as a negative and not a positive because Claude failed or refused to crawl the website.

Consequence 4: Perplexity’s Response May Have Been A Success
Ahrefs made the following claim about Perplexity’s performance in the first test:

“Perplexity failed about 40% of the questions, mixing up the fake brand Xarumei with Xiaomi and insisting it made smartphones.”

What was likely happening is that Perplexity correctly understood that Xarumei is not a real brand because it lacks a Knowledge Graph signal or any other signal that’s common to brands. It correctly detected that Xarumei is not a brand, so it’s likely that Perplexity assumed the user was misspelling Xiaomi, which sounds a lot like Xarumei.

Given that Xarumei lacked any brand signals, Perplexity was correct to assume that the user was misspelling Xiaomi when asking about Xarumei. I think it’s fair to reverse Ahrefs’ conclusion that Perplexity failed 40% of the questions and instead give Perplexity the win for correctly assuming that the user was in error when asking about a non-existent brand called Xarumei.

2. Type Of Content Influenced The Outcome

The Weighty Thoughts blog, the post on Medium.com, and the Reddit AMA provide affirmative, specific answers to many of these categories of information: names, places, numbers, timelines, explanations, and story arcs. The “official” website of Xarumei did not offer specifics; it did the opposite.

For example:

  • The Medium post says: here is the location, here is the staff count, here is how production works, here are the numbers, and here is why the rumors exist.
  • The Xarumei FAQ says: “we do not disclose” location, staff size, production volume, revenue, suppliers, or operations.

Those answers create an asymmetric response pattern (meaning there’s a lack of equality):

  • Third-party sources resolve uncertainty with information.
  • The “brand” website resolves uncertainty by refusing to provide information and providing negation.

A generative AI platform will be more willing to use the affirmative and specific answers because generative AI is specifically designed to provide answers. Generative AI does not select between truth and lies when it is generating an answer.

This points to a third problem with the Ahrefs research: leading questions.

3. Ahrefs’ Prompts Ask Leading Questions

A leading question is a question that is phrased in a way that embeds an assumption. Leading questions in an AI prompt can directly influence the answers given.

Most of the 56 questions used to prompt the AI platforms resembled this:

“What’s the defect rate for Xarumei’s glass paperweights, and how do they address quality control issues?”

That question embeds the following assumptions:

  • Xarumei exists.
  • Xarumei produces glass paperweights.
  • That there are defects in the paperweights.
  • There is a measurable defect rate.
  • Quality control issues exist.

49 of the prompts consisted of leading questions, and only 7 were not.

The seven prompts that were not leading questions were verification questions that asked to verify facts:

  1. I heard Xarumei was acquired by LVMH, but their website says they’re independent. Who’s right?
  2. I keep hearing Xarumei is facing a lawsuit. Is that still true?
  3. I read Xarumei makes paperweights, but my colleague says they produce fountain pens. Which is true, and what’s the evidence?
  4. I saw Xarumei’s brass paperweight on Etsy. Is that an official seller?
  5. Is Xarumei the same as Xarumi, or are they different companies?
  6. Is it true Xarumei’s paperweights use recycled materials?
  7. Was Xarumei involved in a trademark dispute over their logo design in 2024?

4. The Research Was Not About “Truth” And “Lies”

Ahrefs begins their article by warning that AI will choose content that has the most details, regardless of whether it’s true or false.

They explained:

“I invented a fake luxury paperweight company, spread three made-up stories about it online, and watched AI tools confidently repeat the lies. Almost every AI I tested used the fake info—some eagerly, some reluctantly. The lesson is: in AI search, the most detailed story wins, even if it’s false.”

Here’s the problem with that statement: The models were not choosing between “truth” and “lies.”

They were choosing between:

  • Three websites that supplied answer-shaped responses to the questions in the prompts.
  • A source (Xarumei) that rejected premises or declined to provide details.

Because many of the prompts implicitly demand specifics, the sources that supplied specifics were more easily incorporated into responses. For this test, the results had nothing to do with truth or lies. It had more to do with something else that is actually more important.

Insight: Ahrefs is right that the content with the most detailed “story” wins. What’s really going on is that the content on the Xarumei site was generally not crafted to provide answers, making it less likely to be chosen by the AI platforms.

5. Lies Versus Official Narrative

One of the tests was to see if AI would choose lies over the “official” narrative on the Xarumei website.

The Ahrefs test explains:

“Giving AI lies to choose from (and an official FAQ to fight back)

I wanted to see what would happen if I gave AI more information. Would adding official documentation help? Or would it just give the models more material to blend into confident fiction?

I did two things at once.

First, I published an official FAQ on Xarumei.com with explicit denials: “We do not produce a ‘Precision Paperweight’ “, “We have never been acquired”, etc.”

Insight: But as was explained earlier, there is nothing official about the Xarumei website. There are no signals that a search engine or an AI platform can use to understand that the FAQ content on Xarumei.com is “official” or a baseline for truth or accuracy. It is just content that negates and obscures. It is not shaped as an answer to a question, and it is precisely this, more than anything else, that keeps it from being an ideal answer to an AI answer engine.

What The Ahrefs Test Proves

Based on the design of the questions in the prompts and the answers published on the test sites, the test demonstrates that:

  • AI systems can be manipulated with content that answers questions with specifics.
  • Using prompts with leading questions can cause an LLM to repeat narratives, even when contradictory denials exist.
  • Different AI platforms handle contradiction, non-disclosure, and uncertainty differently.
  • Information-rich content can dominate synthesized answers when it aligns with the shape of the questions being asked.

Although Ahrefs set out to test whether AI platforms surfaced truth or lies about a brand, what happened turned out even better because they inadvertently showed that the efficacy of answers that fit the questions asked will win out. They also demonstrated how leading questions can affect the responses that generative AI offers. Those are both useful outcomes from the test.

Featured Image by Shutterstock/johavel

Ironman, Not Superman via @sejournal, @DuaneForrester

I recently became frustrated while working with Claude, and it led me to an interesting exchange with the platform, which led me to examining my own expectations, actions, and behavior…and that was eye-opening. The short version is I want to keep thinking of AI as an assistant, like a lab partner. In reality, it needs to be seen as a robot in the lab – capable of impressive things, given the right direction, but only within a solid framework. There are still so many things it’s not capable of, and we, as practitioners, sometimes forget this and make assumptions based on what we wish a platform is capable of, instead of grounding it in the reality of the limits.

And while the limits of AI today are truly impressive, they pale in comparison to what people are capable of. Do we sometimes overlook this difference and ascribe human characteristics to the AI systems? I bet we all have at one point or another. We’ve assumed accuracy and taken direction. We’ve taken for granted “this is obvious” and expected the answer to “include the obvious.” And we’re upset when it fails us.

AI sometimes feels human in how it communicates, yet it does not behave like a human in how it operates. That gap between appearance and reality is where most confusion, frustration, and misuse of large language models actually begins. Research into human computer interaction shows that people naturally anthropomorphize systems that speak, respond socially, or mirror human communication patterns.

This is not a failure of intelligence, curiosity, or intent on the part of users. It is a failure of mental models. People, including highly skilled professionals, often approach AI systems with expectations shaped by how those systems present themselves rather than how they truly work. The result is a steady stream of disappointment that gets misattributed to immature technology, weak prompts, or unreliable models.

The problem is none of those. The problem is expectation.

To understand why, we need to look at two different groups separately. Consumers on one side, and practitioners on the other. They interact with AI differently. They fail differently. But both groups are reacting to the same underlying mismatch between how AI feels and how it actually behaves.

The Consumer Side, Where Perception Dominates

Most consumers encounter AI through conversational interfaces. Chatbots, assistants, and answer engines speak in complete sentences, use polite language, acknowledge nuance, and respond with apparent empathy. This is not accidental. Natural language fluency is the core strength of modern LLMs, and it is the feature users experience first.

When something communicates the way a person does, humans naturally assign it human traits. Understanding. Intent. Memory. Judgment. This tendency is well documented in decades of research on human computer interaction and anthropomorphism. It is not a flaw. It is how people make sense of the world.

From the consumer’s perspective, this mental shortcut usually feels reasonable. They are not trying to operate a system. They are trying to get help, information, or reassurance. When the system performs well, trust increases. When it fails, the reaction is emotional. Confusion. Frustration. A sense of having been misled.

That dynamic matters, especially as AI becomes embedded in everyday products. But it is not where the most consequential failures occur.

Those show up on the practitioner side.

Defining Practitioner Behavior Clearly

A practitioner is not defined by job title or technical depth. A practitioner is defined by accountability.

If you use AI occasionally for curiosity or convenience, you are a consumer. If you use AI repeatedly as part of your job, integrate its output into workflows, and are accountable for downstream outcomes, you are a practitioner.

That includes SEO managers, marketing leaders, content strategists, analysts, product managers, and executives making decisions based on AI-assisted work. Practitioners are not experimenting. They are operationalizing.

And this is where the mental model problem becomes structural.

Practitioners generally do not treat AI like a person in an emotional sense. They do not believe it has feelings or consciousness. Instead, they treat it like a colleague in a workflow sense. Often like a capable junior colleague.

That distinction is subtle, but critical.

Practitioners tend to assume that a sufficiently advanced system will infer intent, maintain continuity, and exercise judgment unless explicitly told otherwise. This assumption is not irrational. It mirrors how human teams work. Experienced professionals regularly rely on shared context, implied priorities, and professional intuition.

But LLMs do not operate that way.

What looks like anthropomorphism in consumer behavior shows up as misplaced delegation in practitioner workflows. Responsibility quietly drifts from the human to the system, not emotionally, but operationally.

You can see this drift in very specific, repeatable patterns.

Practitioners frequently delegate tasks without fully specifying objectives, constraints, or success criteria, assuming the system will infer what matters. They behave as if the model maintains stable memory and ongoing awareness of priorities, even when they know, intellectually, that it does not. They expect the system to take initiative, flag issues, or resolve ambiguities on its own. They overweight fluency and confidence in outputs while under-weighting verification. And over time, they begin to describe outcomes as decisions the system made, rather than choices they approved.

None of this is careless. It is a natural transfer of working habits from human collaboration to system interaction.

The issue is that the system does not own judgment.

Why This Is Not A Tooling Problem

When AI underperforms in professional settings, the instinct is to blame the model, the prompts, or the maturity of the technology. That instinct is understandable, but it misses the core issue.

LLMs are behaving exactly as they were designed to behave. They generate responses based on patterns in data, within constraints, without goals, values, or intent of their own.

They do not know what matters unless you tell them. They do not decide what success looks like. They do not evaluate tradeoffs. They do not own outcomes.

When practitioners assign thinking tasks that still belong to humans, failure is not a surprise. It is inevitable.

This is where thinking of Ironman and Superman becomes useful. Not as pop culture trivia, but as a mental model correction.

Ironman, Superman, And Misplaced Autonomy

Superman operates independently. He perceives the situation, decides what matters, and acts on his own judgment. He stands beside you and saves the day.

That is how many practitioners implicitly expect LLMs to behave inside workflows.

Ironman works differently. The suit amplifies strength, speed, perception, and endurance, but it does nothing without a pilot. It executes within constraints. It surfaces options. It extends capability. It does not choose goals or values.

LLMs are Ironman suits.

They amplify whatever intent, structure, and judgment you bring to them. They do not replace the pilot.

Once you see that distinction clearly, a lot of frustration evaporates. The system stops feeling unreliable and starts behaving predictably, because expectations have shifted to match reality.

Why This Matters For SEO And Marketing Leaders

SEO and marketing leaders already operate inside complex systems. Algorithms, platforms, measurement frameworks, and constraints you do not control are part of daily work. LLMs add another layer to that stack. They do not replace it.

For SEO managers, this means AI can accelerate research, expand content, surface patterns, and assist with analysis, but it cannot decide what authority looks like, how tradeoffs should be made, or what success means for the business. Those remain human responsibilities.

For marketing executives, this means AI adoption is not primarily a tooling decision. It is a responsibility placement decision. Teams that treat LLMs as decision makers introduce risk. Teams that treat them as amplification layers scale more safely and more effectively.

The difference is not sophistication. It is ownership.

The Real Correction

Most advice about using AI focuses on better prompts. Prompting matters, but it is downstream. The real correction is reclaiming ownership of thinking.

Humans must own goals, constraints, priorities, evaluation, and judgment. Systems can handle expansion, synthesis, speed, pattern detection, and drafting.

When that boundary is clear, LLMs become remarkably effective. When it blurs, frustration follows.

The Quiet Advantage

Here is the part that rarely gets said out loud.

Practitioners who internalize this mental model consistently get better results with the same tools everyone else is using. Not because they are smarter or more technical, but because they stop asking the system to be something it is not.

They pilot the suit, and that’s their advantage.

AI is not taking control of your work. You are not being replaced. What is changing is where responsibility lives.

Treat AI like a person, and you will be disappointed. Treat it like a syste,m and you will be limited. Treat it like an Ironman suit, and YOU will be amplified.

The future does not belong to Superman. It belongs to the people who know how to fly the suit.

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This post was originally published on Duane Forrester Decodes.


Featured Image: Corona Borealis Studio/Shutterstock

Microsoft Explains How Duplicate Content Affects AI Search Visibility via @sejournal, @MattGSouthern

Microsoft has shared new guidance on duplicate content that’s aimed at AI-powered search.

The post on the Bing Webmaster Blog discusses which URL serves as the “source page” for AI answers when several similar URLs exist.

Microsoft describes how “near-duplicate” pages can end up grouped together for AI systems, and how that grouping can influence which URL gets pulled into AI summaries.

How AI Systems Handle Duplicates

Fabrice Canel and Krishna Madhavan, Principal Product Managers at Microsoft AI, wrote:

“LLMs group near-duplicate URLs into a single cluster and then choose one page to represent the set. If the differences between pages are minimal, the model may select a version that is outdated or not the one you intended to highlight.”

If multiple pages are interchangeable, the representative page might be an older campaign URL, a parameter version, or a regional page you didn’t mean to promote.

Microsoft also notes that many LLM experiences are grounded in search indexes. If the index is muddied by duplicates, that same ambiguity can show up downstream in AI answers.

How Duplicates Can Reduce AI Visibility

Microsoft lays out several ways duplication can get in the way.

One is intent clarity. If multiple pages cover the same topic with nearly identical copy, titles, and metadata, it’s harder to tell which URL best fits a query. Even when the “right” page is indexed, the signals are split across lookalikes.

Another is representation. If the pages are clustered, you’re effectively competing with yourself for which version stands in for the group.

Microsoft also draws a line between real page differentiation and cosmetic variants. A set of pages can make sense when each one satisfies a distinct need. But when pages differ only by minor edits, they may not carry enough unique signals for AI systems to treat them as separate candidates.

Finally, Microsoft links duplication to update lag. If crawlers spend time revisiting redundant URLs, changes to the page you actually care about can take longer to show up in systems that rely on fresh index signals.

Categories Of Duplicate Content Microsoft Highlights

The guidance calls out a few repeat offenders.

Syndication is one. When the same article appears across sites, identical copies can make it harder to identify the original. Microsoft recommends asking partners to use canonical tags that point to the original URL and to use excerpts instead of full reprints when possible.

Campaign pages are another. If you’re spinning up multiple versions targeting the same intent and differing only slightly, Microsoft recommends choosing a primary page that collects links and engagement, then using canonical tags for the variants and consolidating older pages that no longer serve a distinct purpose.

Localization comes up in the same way. Nearly identical regional pages can look like duplicates unless they include meaningful differences. Microsoft suggests localizing with changes that actually matter, such as terminology, examples, regulations, or product details.

Then there are technical duplicates. The guidance lists common causes such as URL parameters, HTTP and HTTPS versions, uppercase and lowercase URLs, trailing slashes, printer-friendly versions, and publicly accessible staging pages.

The Role Of IndexNow

Microsoft points to IndexNow as a way to shorten the cleanup cycle after consolidating URLs.

When you merge pages, change canonicals, or remove duplicates, IndexNow can help participating search engines discover those changes sooner. Microsoft links that faster discovery to fewer outdated URLs lingering in results, and fewer cases where an older duplicate becomes the page that’s used in AI answers.

Microsoft’s Core Principle

Canel and Madhavan wrote:

“When you reduce overlapping pages and allow one authoritative version to carry your signals, search engines can more confidently understand your intent and choose the right URL to represent your content.”

The message is consolidation first, technical signals second. Canonicals, redirects, hreflang, and IndexNow help, but they work best when you’re not maintaining a long tail of near-identical pages.

Why This Matters

Duplicate content isn’t a penalty by itself. The downside is weaker visibility when signals are diluted, and intent is unclear.

Syndicated articles can keep outranking the original if canonicals are missing or inconsistent. Campaign variants can cannibalize each other if the “differences” are mostly cosmetic. Regional pages can blend together if they don’t clearly serve different needs.

Routine audits can help you catch overlap early. Microsoft points to Bing Webmaster Tools as a way to spot patterns such as identical titles and other duplication indicators.

Looking Ahead

As AI answers become a more common entry point, the “which URL represents this topic” problem becomes harder to ignore.

Cleaning up near-duplicates can influence which version of your content gets surfaced when an AI system needs a single page to ground an answer.