The ROI Problem With AI Traffic Nobody Is Measuring Correctly via @sejournal, @DuaneForrester

Search engines were designed to do several things at once: Rank a field of options, route the user to one of them, and keep the human inside the decision so the engine never owned the choice. That last part was not an accident. It was the liability architecture. Large language models were built without any of it. They were built to answer the question directly, which is a different job entirely, and the design choices that follow from it change what visibility looks like, what risk looks like, and what the word ROI can honestly mean when the thing sending you traffic was never built to send traffic in the first place.

Two Systems, Two Jobs

A search engine’s job description is long. It crawls the web, indexes it, ranks a pool of candidate results against a query, presents them as a ranked list, and then waits for the human to make a click decision. The SERP itself has been drifting toward retention for years now, with galleries, rich snippets, answer boxes, local maps, video carousels, and AI Overviews all layering in features that keep the user on the page longer and route fewer of them to third-party sites. But the underlying contract was always the same. The engine offers options. The user selects one. The user owns the choice.

An LLM does not offer options. It produces an answer. The citation, when it appears, is not functioning as a routing instrument. It is closer to a grounding artifact produced by a retrieval pipeline, or in some framings, a confidence hedge, or both at the same time. Whichever read you prefer, none of them describe a system designed to send traffic somewhere else. The system was designed to resolve the question in place.

That distinction sits beneath every metric conversation in this space. When practitioners ask what the LLM referral rate is, what the attributed traffic number looks like, what the click-through from an AI answer is, they are asking questions that assume a routing mechanism that is not actually part of the architecture. Whatever traffic does come through is a byproduct, not a design goal, and confusing the two is the first mistake in almost every conversation about AI visibility ROI.

The Liability Surface Moved

The human in the click decision was the SERP’s shield. If the link the user selected led somewhere harmful, misleading, or defamatory, the engine could point to the list of options and the user’s own agency in choosing one. The engine had not published the claim. It had surfaced 10 candidate sources, the user had chosen one, and whatever happened next was not the engine’s editorial output. That is not a small feature. That is the reason Section 230 protections were structured the way they were, and why algorithmic ranking has traditionally been treated as something other than direct speech.

LLMs have no equivalent shield to stand behind. The system is producing the answer directly, in its own voice, without a field of options or a user-selected source. The liability surface that the SERP was designed to offload sits with the model producing the output, and the cases that have already moved through courts are starting to sketch the edges of that surface.

Walters v. OpenAI was dismissed on summary judgment in May 2025, and the decision leaned heavily on OpenAI’s disclaimers and a sophisticated reader who reasonably knew the chatbot could hallucinate. That reading protects general-purpose consumer chatbots in a very specific kind of case. It does not protect every product that uses a language model. In a separate matter, Air Canada was held liable for its customer service chatbot’s false statements about its own bereavement fare policy, because a customer could reasonably rely on an airline’s branded support agent for accurate information about that airline’s policies. Reasonable reliance is the key legal term, and the more specialized and authoritative the chatbot appears, the harder the disclaimer defense becomes to run.

The active litigation is still mapping the frontier. OpenAI is currently facing multiple lawsuits tied to allegations that ChatGPT drove users toward suicide or harmful delusions, several involving minors. The New York Times copyright case against OpenAI was allowed to proceed by a federal judge in March 2025, and Anthropic settled with book authors in August 2025 for a reported sum well into the billions. European GDPR complaints continue to move through Noyb. Battle v. Microsoft is still live. None of these outcomes are settled, and some will be dismissed on the same disclaimer grounds that resolved Walters. The point is not that LLM operators will lose every case. The point is that the liability surface now sits with the system producing the output, whether the individual plaintiff wins or loses, and every brand building against an LLM inherits some version of that surface when it uses the system’s output in its own customer-facing work.

The Denominator Problem

The most common argument against investing in AI visibility work sounds decisive until you look closely at what it is measuring. The argument runs roughly: ChatGPT and the others send a tiny sliver of referral traffic, somewhere in the low single digits of total inbound, so why reallocate budget toward a channel that barely moves the needle? Conductor’s research pegs the combined AI referral share at about 1% of publisher traffic. That number is real. At first read, it seems to close the ROI question cleanly.

It closes nothing. The problem is the denominator.

While the AI share of traffic holds roughly steady, the absolute volume of search-driven traffic has collapsed across most publisher categories. Similarweb data shows organic traffic to news publishers fell from about 2.3 billion monthly visits in mid-2024 to under 1.7 billion by May 2025, a loss of more than 600 million visits in under a year. Business Insider’s search traffic dropped 55% between April 2022 and April 2025, HuffPost lost roughly half of its search referrals, and The New York Times saw search’s share of its desktop and mobile traffic slide from 44% to 37%. Zero-click searches climbed from 56% to 69% between May 2024 and May 2025 as AI Overviews expanded across the SERP. A Reuters Institute survey of 280 media leaders in late 2025 found they expect another 43% decline on average over the next three years.

Read against that backdrop, a stable percentage share of a shrinking pie is not stable. It is a loss. The skeptics who point at the 1% number are measuring relative share of a traffic base that is contracting underneath them, and they are treating a falling absolute as if it were a steady state. The real question is not whether LLMs are sending meaningful traffic yet. The real question is whether the channel that used to send meaningful traffic is still doing what it used to do, and the answer is visibly no. The denominator is moving, and any ROI calculation anchored to the old denominator is a calculation of the previous environment, not the current one.

What The Billions Say

If the design-intent and liability and denominator arguments still leave room for doubt, the last place to look is revealed preference. What are the companies with the most complete internal data on user behavior actually doing with their capital?

The answer is unambiguous. The five largest U.S. cloud and AI infrastructure providers have committed between 660 and 690 billion dollars in 2026 capital expenditure, nearly doubling 2025 levels. Alphabet alone is guiding to between 175 and 185 billion for 2026, more than doubling its 2025 spend of 91 billion. Microsoft, Amazon, Meta, and Oracle are all running similarly aggressive curves. The number that matters most, and that defuses the usual counter-argument, comes from Bank of America credit strategists who estimate AI capex will reach 94% of operating cash flows in 2025 and 2026, up from 76% in 2024.

That is not the shape of a defensive hedge. A hedge is a fraction of the cash flow, deployed to avoid being caught flat-footed if a competitor’s bet pays off. Companies do not put 94% of operating cash flow into a category for two consecutive years unless the leadership genuinely believes the category is the business. And those leadership teams have access to data that the rest of us do not. They can see inside their own products, their own user behavior shifts, their own cohort analyses, their own enterprise pipeline conversations. They are legally bound to deploy shareholder capital in a way that reflects what they actually see, and what they are deploying it toward is the architecture that produces direct answers rather than ranked lists of options. To believe search-as-we-knew-it remains the gold standard, you have to believe that dozens of CEOs, boards, and senior leadership teams with decades of internal-only data are reading their own numbers wrong, while an external industry with none of that data is reading the market correctly. That does not pencil.

The human-behavior side of the equation makes the same point in a different register. Every labor-saving technology that has ever been introduced has reshaped the status quo faster than its skeptics predicted, because cognitive efficiency is not a preference. It is a survival behavior, wired in through long periods when calories were scarce, and shortcuts mattered. When a new tool appears that makes some task meaningfully easier, adoption is not a matter of whether. It is a matter of how fast and along what curve. ChatGPT is now at roughly 900 million weekly active users, up from 200 million 18 months earlier, and the full category is past a billion active users across platforms. The behavior has already shifted. The money has already shifted. The only thing that has not fully shifted is the measurement frame most practitioners are still using to evaluate the channel.

Which brings the question back to the one that is actually worth asking. What do you do if there is no ROI by the old definition, and you still cannot ignore the channel? The honest answer is that brands will need to invest in visibility work whose return is not expressed in clicks or referral traffic, because clicks and referral traffic are artifacts of the previous design. Being the cited source, the grounded source, the trusted source inside the answer is a different kind of visibility, and it will need a different kind of measurement. The teams that figure that out first will not be doing it because they found an ROI case that convinced their CFO. They will be doing it because they looked at the capex curves, the behavioral curves, and the liability curves, and concluded that the channel is the future, regardless of whether the spreadsheet knows how to score it yet.

If this lands somewhere real in your work, or if it reads wrong from where you are sitting, I would like to hear about it. The shift happening right now is too large for any one practitioner’s vantage point, and the best signal I get comes from the conversations that start after the article ends.

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This post was originally published on Duane Forrester Decodes.

Featured Image: Krot_Studio/Shutterstock; Paulo Bobita/Search Engine Journal