Data scientists at the New Jersey Institute of Technology (NJIT) have delivered a blunt verdict on the ongoing disputes over Google's AI-summarized search results: it's not you, it's Google.

The finding, reported by NJIT News, comes as publishers, SEO professionals and everyday users continue to report inconsistencies and confusion surrounding AI-generated summaries that appear above traditional organic listings in Google Search.

What the researchers concluded

The NJIT data science team examined the behavior of Google's AI-generated search summaries and concluded that responsibility for the errors, inconsistencies and user frustration lies with the search engine itself — not with users misunderstanding the results or querying them incorrectly.

The framing — "It's Not You, It's Google" — is a direct rebuttal to any suggestion that the problems users encounter with AI-summarized answers stem from poor prompting, unclear queries or unrealistic expectations. According to the researchers, the machine-generated summaries Google surfaces are themselves the source of the trouble.

The source material does not specify the exact methodology, sample size, or date range of the analysis, so the strength of the underlying evidence cannot be independently verified from the report alone. What is confirmed: NJIT-affiliated data scientists publicly attributed the problems with AI-summarized search results to Google's systems rather than to user behavior.

Why this matters for search professionals

The statement lands amid a period of intense scrutiny for AI-generated answers in search. Google's AI Overviews and similar summarization features rewrite the traditional search experience: instead of presenting ten blue links, the engine synthesizes an answer drawn from multiple sources, often with citations, sometimes without clear attribution.

That shift has measurable consequences across site types. Publishers and content sites report losing click-through when a summary satisfies the user's question directly. E-commerce and service businesses face a different risk: if the AI summary mischaracterizes a product, price or fact drawn from their pages, the error appears with Google's authority attached to it. Local businesses depending on branded queries can see AI-generated descriptions that drift from what their own sites state.

The NJIT researchers' conclusion reinforces the concern that these summaries are not a neutral repackaging of the web's content. When the summarization layer introduces errors, the site that supplied the original information rarely bears the visible blame — Google does, and so does the source it misquoted.

Confirmed statements vs. open questions

To separate what is established from what remains speculative:

Confirmed: NJIT data scientists publicly stated that problems with AI-summarized Google search results originate with Google, not with users. The report frames user frustration as a justified response to system-level behavior.

Not confirmed by the source: The specific technical causes the researchers identified, the scale of the errors they observed, whether the findings have been published in a peer-reviewed venue, and any response from Google itself. The available report does not include direct quotes from the researchers, so their exact wording cannot be reproduced here.

Speculative (analytical, not from the source): If the researchers' attribution holds up under broader scrutiny, it could shift the accountability conversation — from advising users to "search better" toward demanding greater accuracy, transparency and citation discipline from Google's summarization systems themselves. That is an inference, not a finding.

The accountability question

The researchers' intervention matters because it addresses a debate that has played out mostly in anecdote. Users who spot wrong or absurd AI summaries have often been told the problem lies in how they phrased a query. SEO practitioners, meanwhile, have documented cases where AI summaries contradict the very pages they cite. A data science team stepping in to say the fault sits with the system gives that frustration an analytical anchor.

For site owners, the practical implication is sobering. You cannot prompt-engineer your way around a summarization layer you do not control. The levers available — structured data, clear on-page facts, consistent entity information — reduce the chance your content is misread, but they cannot guarantee the summary layer reproduces it faithfully.

What to watch next

Watch for the full publication of the NJIT team's methodology and findings, any quantification of error rates in AI-summarized results, and whether Google responds publicly to the researchers' attribution. How the company addresses accuracy complaints in its AI summaries — through citation changes, source controls or summary withdrawal — will indicate whether the fault line the researchers identified gets repaired or simply rebranded.