AINEWS
Auto Dealers Are Paying to Show Up in ChatGPT Answers
A new vendor service called Generative Engine Optimization promises to put dealership names inside ChatGPT and Google AI answers. Independent evidence that the pitch works is still scarce.
A new vendor service called Generative Engine Optimization promises to put dealership names inside ChatGPT and Google AI answers. Independent evidence that the pitch works is still scarce.
A car shopper in 2026 might open ChatGPT and ask for "the best hybrid SUV under $35,000 near me" instead of typing keywords into Google. The answer that comes back is a synthesized paragraph, sometimes citing dealers by name, sometimes not. A new vendor category has formed to make sure those AI-generated answers mention their clients, and auto dealerships are the latest group being asked to pay for it.
The pitch is called Generative Engine Optimization, or GEO, the AI-era cousin of search engine optimization. Where traditional SEO aimed to push a website up Google's blue-link rankings, GEO is sold as the practice of getting a dealership cited inside the answers produced by large language models such as ChatGPT and Google's AI Overviews, the AI-generated answer boxes that now sit above traditional search results.
On June 15, 2026, Chicago-based automotive ad tech firm L2T became the latest vendor to formally package GEO for dealerships, announcing new products in a press release distributed via PR Newswire. The release describes three offerings: an "advanced LLM reporting" dashboard, a unified SEO plus GEO package, and a standalone GEO product. Target platforms, according to the release, are ChatGPT and Google AI Overviews.
The LLM reporting dashboard claims to track things like which keywords trigger AI Overviews, how often a dealership gets cited, how much referral traffic comes from major language models, and how sentiment about the brand shifts across AI answers. The company is also pitching what it calls "llms.txt," a file meant to guide AI crawlers the way robots.txt once guided Google, and "GEO-optimized schema," or structured data that vendors say helps language models parse a dealer's inventory.
David Weisman, L2T's vice president of operations, framed the announcement in the release as a way for dealers to be "found, cited, and trusted" in AI answers. The press release is the only public artifact of the announcement. There is no independent dealer case study attached, no third-party benchmark, and no adoption data.
That absence is the story. AI-driven search is genuinely reshaping how some car shoppers research vehicles, and the dealership marketing stack is being asked to evolve with it. The L2T release is one of several similar announcements from vendors angling for a share of that new budget, and it is a useful marker of where the category sits today. What is missing is the evidence that any of it moves metal.
Vendor materials in this space often use language borrowed directly from the SEO playbook, repackaged for the AI era. The underlying mechanics, including structured data, citation building, content optimization, and authority signals, are not new. What is new is the claim that they translate into visibility inside an LLM's answer rather than a search result page. Whether language models weight those signals the way vendors promise is not established in public research the way Google's ranking factors have been for two decades.
Dealers being pitched GEO packages should treat the offering the way they would treat any new marketing channel: ask what specific inputs the vendor changes, what measurable output shifts, and what independent verification exists. A "dashboard" that mostly tracks whether a dealership appears in an AI answer at all is measuring presence, not sales.
The watch items are concrete. Look for the first dealership group to publish before-and-after lead or sales data tied to a GEO spend. Look for competing vendors, including established SEO platforms adding AI-search modules, to disclose what share of their dealer clients have actually purchased the new tier. Look for Google or OpenAI to publish guidance on whether structured-data files like llms.txt influence AI Overview or ChatGPT citations at all. Until one of those moves, the category is being built on announcements rather than results.