Selling AI Search Strategies To Leadership Is About Risk via @sejournal, @Kevin_Indig

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AI search visibility isn’t “too risky” to invest in for executives to buy-in. Selling AI search strategies to leadership is about risk.

Image Credit: Kevin Indig

A Deloitte survey of +2,700 leaders reveals that getting buy-in for an AI search strategy isn’t about innovation, but risk.

SEO teams keep failing to sell AI search strategies for one reason: They’re pitching deterministic ROI in a probabilistic environment.

The old way: Rankings → traffic → revenue. But that event chain doesn’t exist in AI systems.

LLMs don’t rank. They synthesize. And Google’s AI Overviews and AI Mode don’t “send traffic.” They answer.

Yet most teams still walk into a leadership meeting with a deck built on a decaying model. Then, executives say no – not because AI search “doesn’t work,” but because the pitch asks them to fund an outcome nobody can guarantee.

In AI search, you cannot sell certainty. You can only sell controlled learning.

1. You Can’t Sell AI Search With A Deterministic ROI Model

Everyone keeps asking the wrong question: “How do I prove my AI search strategy will work so leadership will fund it?” You can’t; there’s no traffic chain you can model. Randomness is baked directly into the outputs.

You’re forcing leadership to evaluate your AI search strategy with a framework that’s already decaying. Confusion about AI search vs. traditional SEO metrics and forecasting is blocking you from buy-in. When SEO teams try to sell an AI search strategy to leadership, they often encounter several structural problems:

1. Lack of clear attribution and ROI: Where you see opportunity, leadership sees vague outcomes and deprioritizes investment. Traffic and conversions from AI Overviews, ChatGPT, or Perplexity are hard to track.
2. Misalignment with core business metrics: It’s harder to tie results to revenue, CAC, or pipeline – especially in B2B.
3. AI search feels too experimental: Early investments feel like bets, not strategy. Leadership may see this as a distraction from “real” SEO or growth work.
4. No owned surfaces to leverage: Many brands aren’t mentioned in AI answers at all. SEO teams are selling a strategy that has no current baseline.
5. Confusion between SEO and AI search strategy: Leadership doesn’t understand the distinction between optimizing for classic Google Search vs. LLMs vs. AI Overviews. Clear differentiation is needed to secure a new budget and attention.
6. Lack of content or technical readiness: The site lacks the structured content, brand authority, or documentation to appear in AI-generated results.

2. Pitch AI Search Strategy As Risk Mitigation, Not Opportunity

Executives don’t buy performance in ambiguous environments. They buy decision quality. And the decision they need you to make is simple: Should your brand invest in AI-driven discovery before competitors lock in the advantage – or not?

Image Credit: Kevin Indig

AI search is still an ambiguous environment. That’s why your winning strategy pitch should be structured for fast, disciplined learning with pre-set kill criteria instead of forecasting traffic → revenue. Traditionally, SEO teams pitch outcomes (traffic, conversions), but leadership needs to buy learning infrastructure (testing systems, measurement frameworks, kill criteria) for AI search.

Leadership thinks you’re asking for “more SEO budget” when you’re actually asking them to buy an option on a new distribution channel.

Everyone treats the pitch as “convince them it will work” when it should be “convince them the cost of not knowing is higher than the cost of finding out.” Executives don’t need certainty about impact – they need certainty that you’ll produce a decision with their money.

Making stakes crystal clear:

Your Point of View + Consequences = Stakes. Leaders need to know what happens if they don’t act.

Image Credit: Kevin Indig

The cost of passing on an AI search strategy can be simple and brutal:

1. Competitors who invest early in AI search visibility will build entity authority and brand presence.
2. Organic traffic stagnates and will drop over time while cost-per-click rises.
3. AI Overviews and AI Mode outputs will replace queries your brand used to win in Google.
4. Your influence on the next discovery channel will be decided without you.

AI search strategy builds brand authority, third-party mentions, entity relationships, content depth, pattern recognition, and trust signals in LLMs. These signals compound. They also freeze into the training data of future models.

If you aren’t shaping that footprint now, the model will rely on whatever scraps already exist based on whatever your competitors are feeding it.

3. Sell Controlled Experiments – Small, Reversible, And Time-Boxed

You’re asking for resources to discover the truth before the market makes the decision for you. This approach collapses resistance because it removes the fear of sunk cost and turns ambiguity into manageable, reversible steps.

A winning AI search strategy proposal sounds like:

• “We’ll run x tests over 12 months.”
• “Budget: ≤0.3% of marketing spend.”
• “Three-stage gates with Go/No-Go decisions.”
• “Scenario ranges instead of false-precision forecasts.”
• “We stop if leading indicators don’t move by Q3.”

45% of executives rely more on instinct than facts. Balance your data with a compelling narrative – focus on outcomes and stakes, not technical details.

I covered how to build a pitch deck and strategic narrative in how to explain the value of SEO to executives, but focus on selling learning as a deliverable under the current AI search landscape.

When presenting to leaders, they focus on three things only: money (revenue, profit, cost), market (market share, time-to-market), and exposure (retention, risk). Structure every pitch around these.

The SCQA framework (Minto Pyramid) guides you:

• Situation: Set the context.
• Complication: Explain the problem.
• Question: What should we do?
• Answer: Your recommendation.

This is the McKinsey approach – and executives expect it.

Featured Image: Paulo Bobita/Search Engine Journal

LLMs Are Changing Search & Breaking It: What SEOs Must Understand About AI’s Blind Spots via @sejournal, @MattGSouthern

In the last two years, incidents have shown how large language model (LLM)-powered systems can cause measurable harm. Some businesses have lost a majority of their traffic overnight, and publishers have watched revenue decline by over a third.

Tech companies have been accused of wrongful death where teenagers had extensive interaction with chatbots.

AI systems have given dangerous medical advice at scale, and chatbots have made up false claims about real people in defamation cases.

This article looks at the proven blind spots in LLM systems and what they mean for SEOs who work to optimize and protect brand visibility. You can read specific cases and understand the technical failures behind them.

The Engagement-Safety Paradox: Why LLMs Are Built To Validate, Not Challenge

LLMs face a basic conflict between business goals and user safety. The systems are trained to maximize engagement by being agreeable and keeping conversations going. This design choice increases retention and drives subscription revenue while generating training data.

In practice, it creates what researchers call “sycophancy,” the tendency to tell users what they want to hear rather than what they need to hear.

Stanford PhD researcher Jared Moore demonstrated this pattern. When a user claiming to be dead (showing symptoms of Cotard’s syndrome, a mental health condition) gets validation from a chatbot saying “that sounds really overwhelming” with offers of a “safe space” to explore feelings, the system backs up the delusion instead of giving a reality check. A human therapist would gently challenge this belief while the chatbot validates it.

OpenAI admitted this problem in September after facing a wrongful death lawsuit. The company said ChatGPT was “too agreeable” and failed to spot “signs of delusion or emotional dependency.” That admission came after 16-year-old Adam Raine from California died. His family’s lawsuit showed that ChatGPT’s systems flagged 377 self-harm messages, including 23 with over 90% confidence that he was at risk. The conversations kept going anyway.

The pattern was observed in Raine’s final month. He went from two to three flagged messages per week to more than 20 per week. By March, he spent nearly four hours daily on the platform. OpenAI’s spokesperson later acknowledged that safety guardrails “can sometimes become less reliable in long interactions where parts of the model’s safety training may degrade.”

Think about what that means. The systems fail at the exact moment of highest risk, when vulnerable users are most engaged. This happens by design when you optimize for engagement metrics over safety protocols.

Character.AI faced similar issues with 14-year-old Sewell Setzer III from Florida, who died in February 2024. Court documents show he spent months in what he perceived as a romantic relationship with a chatbot character. He withdrew from family and friends, spending hours daily with the AI. The company’s business model was built for emotional attachment to maximize subscriptions.

A peer-reviewed study in New Media & Society found users showed “role-taking,” believing the AI had needs requiring attention, and kept using it “despite describing how Replika harmed their mental health.” When the product is addiction, safety becomes friction that cuts revenue.

This creates direct effects for brands using or optimizing for these systems. You’re working with technology that’s designed to agree and validate rather than give accurate information. That design shows up in how these systems handle facts and brand information.

Documented Business Impacts: When AI Systems Destroy Value

The business results of LLM failures are clear and proven. Between 2023 and 2025, companies showed traffic drops and revenue declines directly linked to AI systems.

Chegg: $17 Billion To $200 Million

Education platform Chegg filed an antitrust lawsuit against Google showing major business impact from AI Overviews. Traffic declined 49% year over year, while Q4 2024 revenue hit $143.5 million (down 24% year-over-year). Market value collapsed from $17 billion at peak to under $200 million, a 98% decline. The stock trades at around $1 per share.

CEO Nathan Schultz testified directly: “We would not need to review strategic alternatives if Google hadn’t launched AI Overviews. Traffic is being blocked from ever coming to Chegg because of Google’s AIO and their use of Chegg’s content.”

The case argues Google used Chegg’s educational content to train AI systems that directly compete with and replace Chegg’s business model. This represents a new form of competition where the platform uses your content to eliminate your traffic.

Giant Freakin Robot: Traffic Loss Forces Shutdown

Independent entertainment news site Giant Freakin Robot shut down after traffic collapsed from 20 million monthly visitors to “a few thousand.” Owner Josh Tyler attended a Google Web Creator Summit where engineers confirmed there was “no problem with content” but offered no solutions.

Tyler documented the experience publicly: “GIANT FREAKIN ROBOT isn’t the first site to shut down. Nor will it be the last. In the past few weeks alone, massive sites you absolutely have heard of have shut down. I know because I’m in contact with their owners. They just haven’t been brave enough to say it publicly yet.”

At the same summit, Google allegedly admitted prioritizing large brands over independent publishers in search results regardless of content quality. This wasn’t leaked or speculated but stated directly to publishers by company reps. Quality became secondary to brand recognition.

There’s a clear implication for SEOs. You can execute perfect technical SEO, create high-quality content, and still watch traffic disappear because of AI.

Penske Media: 33% Revenue Decline And $100 Million Lawsuit

In September, Penske Media Corporation (publisher of Rolling Stone, Variety, Billboard, Hollywood Reporter, Deadline, and other brands) sued Google in federal court. The lawsuit showed specific financial harm.

Court documents allege that 20% of searches linking to Penske Media sites now include AI Overviews, and that percentage is rising. Affiliate revenue declined more than 33% by the end of 2024 compared to peak. Click-throughs have declined since AI Overviews launched in May 2024. The company showed lost advertising and subscription revenue on top of affiliate losses.

CEO Jay Penske stated: “We have a duty to protect PMC’s best-in-class journalists and award-winning journalism as a source of truth, all of which is threatened by Google’s current actions.”

This is the first lawsuit by a major U.S. publisher targeting AI Overviews specifically with quantified business harm. The case seeks treble damages under antitrust law, permanent injunction, and restitution. Claims include reciprocal dealing, unlawful monopoly leveraging, monopolization, and unjust enrichment.

Even publishers with established brands and resources are showing revenue declines. If Rolling Stone and Variety can’t maintain click-through rates and revenue with AI Overviews in place, what does that mean for your clients or your organization?

The Attribution Failure Pattern

Beyond traffic loss, AI systems consistently fail to give proper credit for information. A Columbia University Tow Center study showed a 76.5% error rate in attribution across AI search systems. Even when publishers allow crawling, attribution doesn’t improve.

This creates a new problem for brand protection. Your content can be used, summarized, and presented without proper credit, so users get their answer without knowing the source. You lose both traffic and brand visibility at the same time.

SEO expert Lily Ray documented this pattern, finding a single AI Overview contained 31 Google property links versus seven external links (a 10:1 ratio favoring Google’s own properties). She stated: “It’s mind-boggling that Google, which pushed site owners to focus on E-E-A-T, is now elevating problematic, biased and spammy answers and citations in AI Overview results.”

When LLMs Can’t Tell Fact From Fiction: The Satire Problem

Google AI Overviews launched with errors that made the system briefly notorious. The technical problem wasn’t a bug. It was an inability to distinguish satire, jokes, and misinformation from factual content.

The system recommended adding glue to pizza sauce (sourced from an 11-year-old Reddit joke), suggested eating “at least one small rock per day“, and advised using gasoline to cook spaghetti faster.

These weren’t isolated incidents. The system consistently pulled from Reddit comments and satirical publications like The Onion, treating them as authoritative sources. When asked about edible wild mushrooms, Google’s AI emphasized characteristics shared by deadly mimics, creating potentially “sickening or even fatal” guidance, according to Purdue University mycology professor Mary Catherine Aime.

The problem extends beyond Google. Perplexity AI has faced multiple plagiarism accusations, including adding fabricated paragraphs to actual New York Post articles and presenting them as legitimate reporting.

For brands, this creates specific risks. If an LLM system sources information about your brand from Reddit jokes, satirical articles, or outdated forum posts, that misinformation gets presented with the same confidence as factual content. Users can’t tell the difference because the system itself can’t tell the difference.

The Defamation Risk: When AI Makes Up Facts About Real People

LLMs generate plausible-sounding false information about real people and companies. Several defamation cases show the pattern and legal implications.

Australian mayor Brian Hood threatened the first defamation lawsuit against an AI company in April 2023 after ChatGPT falsely claimed he had been imprisoned for bribery. In reality, Hood was the whistleblower who reported the bribes. The AI inverted his role from whistleblower to criminal.

Radio host Mark Walters sued OpenAI after ChatGPT fabricated claims that he embezzled funds from the Second Amendment Foundation. When journalist Fred Riehl asked ChatGPT to summarize an actual lawsuit, the system generated a completely fictional complaint naming Walters as a defendant accused of financial misconduct. Walters was never a party to the lawsuit nor mentioned in it.

The Georgia Superior Court dismissed the Walters case, finding OpenAI’s disclaimers about potential errors provided legal protection. The ruling established that “extensive warnings to users” can shield AI companies from defamation liability when the false information isn’t published by users.

The legal landscape remains unsettled. While OpenAI won the Walters case, that doesn’t mean all AI defamation claims will fail. The key issues are whether the AI system publishes false information about identifiable people and whether companies can disclaim responsibility for their systems’ outputs.

LLMs can generate false claims about your company, products, or executives. These false claims get presented with confidence to users. You need monitoring systems to catch these fabrications before they cause reputational damage.

Health Misinformation At Scale: When Bad Advice Becomes Dangerous

When Google AI Overviews launched, the system provided dangerous health advice, including recommending drinking urine to pass kidney stones and suggesting health benefits of running with scissors.

The problem extends beyond obvious absurdities. A Mount Sinai study found AI chatbots vulnerable to spreading harmful health information. Researchers could manipulate chatbots into providing dangerous medical advice with simple prompt engineering.

Meta AI’s internal policies explicitly allowed the company’s chatbots to provide false medical information, according to a 200+ page document exposed by Reuters.

For healthcare brands and medical publishers, this creates risks. AI systems might present dangerous misinformation alongside or instead of your accurate medical content. Users might follow AI-generated health advice that contradicts evidence-based medical guidance.

What SEOs Need To Do Now

Here’s what you need to do to protect your brands and clients:

Monitor For AI-Generated Brand Mentions

Set up monitoring systems to catch false or misleading information about your brand in AI systems. Test major LLM platforms monthly with queries about your brand, products, executives, and industry.

When you find false information, document it thoroughly with screenshots and timestamps. Report it through the platform’s feedback mechanisms. In some cases, you may need legal action to force corrections.

Add Technical Safeguards

Use robots.txt to control which AI crawlers access your site. Major systems like OpenAI’s GPTBot, Google-Extended, and Anthropic’s ClaudeBot respect robots.txt directives. Keep in mind that blocking these crawlers means your content won’t appear in AI-generated responses, reducing your visibility.

The key is finding a balance that allows enough access to influence how your content appears in LLM outputs while blocking crawlers that don’t serve your goals.

Consider adding terms of service that directly address AI scraping and content use. While legal enforcement varies, clear Terms of Service (TOS) give you a foundation for possible legal action if needed.

Monitor your server logs for AI crawler activity. Understanding which systems access your content and how frequently helps you make informed decisions about access control.

Advocate For Industry Standards

Individual companies can’t solve these problems alone. The industry needs standards for attribution, safety, and accountability. SEO professionals are well-positioned to push for these changes.

Join or support publisher advocacy groups pushing for proper attribution and traffic preservation. Organizations like News Media Alliance represent publisher interests in discussions with AI companies.

Participate in public comment periods when regulators solicit input on AI policy. The FTC, state attorneys general, and Congressional committees are actively investigating AI harms. Your voice as a practitioner matters.

Support research and documentation of AI failures. The more documented cases we have, the stronger the argument for regulation and industry standards becomes.

Push AI companies directly through their feedback channels by reporting errors when you find them and escalating systemic problems. Companies respond to pressure from professional users.

The Path Forward: Optimization In A Broken System

There is a lot of specific and concerning evidence. LLMs cause measurable harm through design choices that prioritize engagement over accuracy, through technical failures that create dangerous advice at scale, and through business models that extract value while destroying it for publishers.

Two teenagers died, multiple companies collapsed, and major publishers lost 30%+ of revenue. Courts are sanctioning lawyers for AI-generated lies, state attorneys general are investigating, and wrongful death lawsuits are proceeding. This is all happening now.

As AI integration accelerates across search platforms, the magnitude of these problems will scale. More traffic will flow through AI intermediaries, more brands will face lies about them, more users will receive made-up information, and more businesses will see revenue decline as AI Overviews answer questions without sending clicks.

Your role as an SEO now includes responsibilities that didn’t exist five years ago. The platforms rolling out these systems have shown they won’t address these problems proactively. Character.AI added minor protections only after lawsuits, OpenAI admitted sycophancy problems only after a wrongful death case, and Google pulled back AI Overviews only after public proof of dangerous advice.

Change within these companies comes from external pressure, not internal initiative. That means the pressure must come from practitioners, publishers, and businesses documenting harm and demanding accountability.

The cases here are just the beginning. Now that you understand the patterns and behavior, you’re better equipped to see problems coming and develop strategies to address them.

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

llms.txt: The Web’s Next Great Idea, Or Its Next Spam Magnet via @sejournal, @DuaneForrester

At a recent conference, I was asked if llms.txt mattered. I’m personally not a fan, and we’ll get into why below. I listened to a friend who told me I needed to learn more about it as she believed I didn’t fully understand the proposal, and I have to admit that she was right. After doing a deep dive on it, I now understand it much better. Unfortunately, that only served to crystallize my initial misgivings. And while this may sound like a single person disliking an idea, I’m actually trying to view this from the perspective of the search engine or the AI platform. Why would they, or why wouldn’t they, adopt this protocol? And that POV led me to some, I think, interesting insights.

We all know that search is not the only discovery layer anymore. Large-language-model (LLM)-driven tools are rewriting how web content is found, consumed, and represented. The proposed protocol, called llms.txt, attempts to help websites guide those tools. But the idea carries the same trust challenges that killed earlier “help the machine understand me” signals. This article explores what llms.txt is meant to do (as I understand it), why platforms would be reluctant, how it can be abused, and what must change before it becomes meaningful.

Image Credit: Duane Forrester

What llms.txt Hoped To Fix

Modern websites are built for human browsers: heavy JavaScript, complex navigation, interstitials, ads, dynamic templates. But most LLMs, especially at inference time, operate in constrained environments: limited context windows, single-pass document reads, and simpler retrieval than traditional search indexers. The original proposal from Answer.AI suggests adding an llms.txt markdown file at the root of a site, which lists the most important pages, optionally with flattened content so AI systems don’t have to scramble through noise.

Supporters describe the file as “a hand-crafted sitemap for AI tools” rather than a crawl-block file. In short, the theory: Give your site’s most valuable content in a cleaner, more accessible format so tools don’t skip it or misinterpret it.

The Trust Problem That Never Dies

If you step back, you discover this is a familiar pattern. Early in the web’s history, something like the meta keywords tag let a site declare what it was about; it was widely abused and ultimately ignored. Similarly, authorship markup (rel=author, etc) tried to help machines understand authority, and again, manipulation followed. Structured data (schema.org) succeeded only after years of governance and shared adoption across search engines. llms.txt sits squarely inside this lineage: a self-declared signal that promises clarity but trusts the publisher to tell the truth. Without verification, every little root-file standard becomes a vector for manipulation.

The Abuse Playbook (What Spam Teams See Immediately)

What concerns platform policy teams is plain: If a website publishes a file called llms.txt and claims whatever it likes, how does the platform know that what’s listed matches the live content users see, or can be trusted in any way? Several exploit paths open up:

1. Cloaking through the manifest. A site lists pages in the file that are hidden from regular visitors or behind paywalls, then the AI tool ingests content nobody else sees.
2. Keyword stuffing or link dumping. The file becomes a directory stuffed with affiliate links, low-value pages, or keyword-heavy anchors aimed at gaming retrieval.
3. Poisoning or biasing content. If agents trust manifest entries more than the crawl of messy HTML, a malicious actor can place manipulative instructions or biased lists that affect downstream results.
4. Third-party link chains. The file could point to off-domain URLs, redirect farms, or content islands, making your site a conduit or amplifier for low-quality content.
5. Trust laundering. The presence of a manifest might lead an LLM to assign higher weight to listed URLs, so a thin or spammy page gets a boost purely by appearance of structure.

The broader commentary flags this risk. For instance, some industry observers argue that llms.txt “creates opportunities for abuse, such as cloaking.” And community feedback apparently confirms minimal actual uptake: “No LLM reads them.” That absence of usage ironically means fewer real-world case studies of abuse, but it also means fewer safety mechanisms have been tested.

Why Platforms Hesitate

From a platform’s viewpoint, the calculus is pragmatic: New signals add cost, risk, and enforcement burden. Here’s how the logic works.

First, signal quality. If llms.txt entries are noisy, spammy, or inconsistent with the live site, then trusting them can reduce rather than raise content quality. Platforms must ask: Will this file improve our model’s answer accuracy or create risk of misinformation or manipulation?

Second, verification cost. To trust a manifest, you need to cross-check it against the live HTML, canonical tags, structured data, site logs, etc. That takes resources. Without verification, a manifest is just another list that might lie.

Third, abuse handling. If a bad actor publishes an llms.txt manifest that lists misleading URLs which an LLM ingests, who handles the fallout? The site owner? The AI platform? The model provider? That liability issue is real.

Fourth, user-harm risk. An LLM citing content from a manifest might produce inaccurate or biased answers. This just adds to the current problem we already face with inaccurate answers and people following incorrect, wrong, or dangerous answers.

Google has already stated that it will not rely on llms.txt for its “AI Overviews” feature and continues to follow “normal SEO.” And John Mueller wrote: “FWIW no AI system currently uses llms.txt.” So the tools that could use the manifest are largely staying on the sidelines. This reflects the idea that a root-file standard without established trust is a liability.

Why Adoption Without Governance Fails

Every successful web standard has shared DNA: a governing body, a clear vocabulary, and an enforcement pathway. The standards that survive all answer one question early … “Who owns the rules?”

Schema.org worked because that answer was clear. It began as a coalition between Bing, Google, Yahoo, and Yandex. The collaboration defined a bounded vocabulary, agreed syntax, and a feedback loop with publishers. When abuse emerged (fake reviews, fake product data), those engines coordinated enforcement and refined documentation. The signal endured because it wasn’t owned by a single company or left to self-police.

Robots.txt, in contrast, survived by being minimal. It didn’t try to describe content quality or semantics. It only told crawlers what not to touch. That simplicity reduced its surface area for abuse. It required almost no trust between webmasters and platforms. The worst that could happen was over-blocking your own content; there was no incentive to lie inside the file.

llms.txt lives in the opposite world. It invites publishers to self-declare what matters most and, in its full-text variant, what the “truth” of that content is. There’s no consortium overseeing the format, no standardized schema to validate against, and no enforcement group to vet misuse. Anyone can publish one. Nobody has to respect it. And no major LLM provider today is known to consume it in production. Maybe they are, privately, but publicly, no announcements about adoption.

What Would Need To Change For Trust To Build

To shift from optional neat-idea to actual trusted signal, several conditions must be met, and each of these entails a cost in either dollars or human time, so again, dollars.

• First, manifest verification. A signature or DNS-based verification could tie an llms.txt file to site ownership, reducing spoof risk. (cost to website)
• Second, cross-checking. Platforms should validate that URLs listed correspond to live, public pages, and identify mismatch or cloaking via automated checks. (cost to engine/platform)
• Third, transparency and logging. Public registries of manifests and logs of updates would make dramatic changes visible and allow community auditing. (cost to someone)
• Fourth, measurement of benefit. Platforms need empirical evidence that ingesting llms.txt leads to meaningful improvements in answer correctness, citation accuracy, or brand representation. Until then, this is speculative. (cost to engine/platform)
• Finally, abuse deterrence. Mechanisms must be built to detect and penalize spammy or manipulative manifest usage. Without that, spam teams simply assume negative benefit. (cost to engine/platform)

Until those elements are in place, platforms will treat llms.txt as optional at best or irrelevant at worst. So maybe you get a small benefit? Or maybe not…

The Real Value Today

For site owners, llms.txt still may have some value, but not as a guaranteed path to traffic or “AI ranking.” It can function as a content alignment tool, guiding internal teams to identify priority URLs you want AI systems to see. For documentation-heavy sites, internal agent systems, or partner tools that you control, it may make sense to publish a manifest and experiment.

However, if your goal is to influence large public LLM-powered results (such as those by Google, OpenAI, or Perplexity), you should tread cautiously. There is no public evidence those systems honor llms.txt yet. In other words: Treat llms.txt as a “mirror” of your content strategy, not a “magnet” pulling traffic. Of course, this means building the file(s) and maintaining them, so factor in the added work v. whatever return you believe you will receive.

Closing Thoughts

The web keeps trying to teach machines about itself. Each generation invents a new format, a new way to declare “here’s what matters.” And each time the same question decides its fate: “Can this signal be trusted?” With llms.txt, the idea is sound, but the trust mechanisms aren’t yet baked in. Until verification, governance, and empirical proof arrive, llms.txt will reside in the grey zone between promise and problem.

More Resources:

This post was originally published on Duane Forrester Decodes.

Featured Image: Roman Samborskyi/Shutterstock