Google AI Overviews is 44% more likely to criticize your brand than ChatGPT. Here's why — and the 2026 defensive playbook.

In March 2026 BrightEdge published the largest controlled study to date on negative brand sentiment in AI search. The headline number: Google AI Overviews surfaces negative content about brands 44% more often than ChatGPT does.¹ The two engines disagree on which brands to criticize 73% of the time¹ — same prompt, same week, different answers.

If you only optimise for AI citations and ignore what those citations actually say about your brand, you can move Share of Voice up while making the answer worse. This article is the defensive playbook — the data behind the 44% gap, the mechanism for why the two engines diverge, the four root causes of negative AI sentiment, what the courts have decided so far (Walters v. OpenAI; NYT, Dow Jones v. Perplexity), and a 7-step framework for fixing it without making it worse.

Methodology & sources

Editorial review for factual claims (as of 2026-05-14).

• Primary source for the 44% claim: BrightEdge press release dated 5 March 2026, plus the BrightEdge weekly insights series "When AI Goes Negative" (industry-specific breakdowns) and the Fortune (12 March 2026) and MediaPost (10 March 2026) coverage with additional figures.
• Sentiment-by-source data: Tinuiti Q1 2026 AI Citation Trends Report.
• Legal context: Walters v. OpenAI ruling summarised by Syracuse Law Review and Global Legal Insights; the NYT and Dow Jones complaints against Perplexity covered by Bloomberg Law.
• Bot traffic data: Cloudflare Radar Q1 2026 + Cloudflare blog series on AI crawler traffic by purpose and industry.
• No proprietary numbers from us. Our own dataset is smaller than BrightEdge or Tinuiti's; we cite external research throughout. Where 2026 data isn't yet published (e.g. specific weighting algorithms inside Google AI Mode), we say so.
• Agency-published reports and SaaS case studies are flagged explicitly — those are useful directional signals, not peer-reviewed numbers.

The data — what 2026 actually shows

The 44% number (BrightEdge, March 2026)

BrightEdge ran the same prompt set across Google AI Overviews and ChatGPT and measured how often each engine surfaced negative content about a named brand. The headline finding: AI Overviews tagged a brand-mention as negative roughly 2.3% of the time vs ChatGPT's 1.6%¹ — a 44% relative gap. That number is the headline for a reason: it's the first large-sample 2026 measurement of a difference most brand teams suspected but couldn't quantify.

A second finding from the same study mattered more for practitioners: 73% disagreement¹. On the prompts where one engine surfaced negative sentiment, the other engine didn't, more than two times out of three. Same brand, same week, same prompt — different answers. There is no single "AI reputation" any more. There are at least two, and the playbook is different for each.

The buyer-journey twist (the line every CMO misses)

BrightEdge broke the data down by where the buyer was in the funnel. 85% of Google's negative mentions happen at the informational stage — top-of-funnel "what is X" / "best X for Y" / "why do people complain about X" queries. ChatGPT's behaviour is structurally different: 68.5% informational but 19.4% at consideration-to-purchase, vs Google's 1.5%³.

The practical translation: ChatGPT is roughly 13× more likely than Google to surface negative content right at the decision-to-buy moment³. That inverts the common narrative that "ChatGPT is the polite engine and Google AI is the harsh one". It's truer to say Google is harsh at top of funnel (when you're still curious), and ChatGPT is harsh at the bottom (when the buyer has decided you might be the answer and is doing final due diligence). Both matter, just for different reasons.

The Reddit angle — why the May 6 update made this bigger

Tinuiti's Q1 2026 AI Citation Trends Report measured the sentiment distribution of Reddit citations specifically. On Perplexity, 5.0% of cited Reddit threads carry positive brand sentiment vs 6.1% negative⁴. The skew is slight, but it's negative. Reddit's culture rewards specificity, and the most specific opinions are usually criticisms.

This matters because of the May 6, 2026 Google AI Mode update⁵. Google added two new sections to AI Mode answers — "Expert Advice" and "Community Perspectives" — that pull preferentially from Reddit and similar forum content. The category that was already negative-tilted is now being surfaced more on Google. We unpacked the update itself in Reddit Is Now Inside Google's AI Mode (May 6, 2026); this article is about what to do when the threads that get pulled aren't flattering.

Industry breakdown — where it's worst

BrightEdge published industry-specific cuts in their weekly insights series. Three numbers worth knowing⁶⁷:

• Electronics has the highest absolute negative-sentiment rate across both engines. Drivers: product recalls, outages, security incidents — all of which generate lawsuits, news cycles, and Reddit threads.
• Education has Google AIO roughly 2× more critical than ChatGPT. Drivers: regulatory actions, accreditation disputes, student-debt-related coverage.
• Healthcare has a 58× spread between OTC / pharma brands (high negative sentiment, driven by recalls + lawsuit dockets) and hospital systems (low negative sentiment, treated as authoritative sources)⁷.

If you're in one of these verticals, the 44% headline is the floor for what to expect. The ceiling is materially worse.

Why the two engines disagree — the mechanism

BrightEdge's controlled study isn't just an effect — it has a mechanism, and understanding the mechanism is what makes the defensive playbook work.

Google AI Overviews pulls UGC and news heavily

Google AI Overviews leans into the same source mix that powers Google's organic SERP, which means lawsuit dockets, regulatory filings, news headlines, Reddit, Quora, and YouTube videos all flow into its retrieval set. Reddit alone is 2.2% of all AIO citations in Tinuiti's January 2026 measurement, and Reddit accounts for 44% of all social citations in AIO⁴. When a brand has a controversy headline from the last 24 months — a recall, a breach, a settled lawsuit — AIO is likely to surface that headline in answers for queries even tangentially related.

ChatGPT weights product / review / forum content differently

ChatGPT pulls from a different mix dominated by Wikipedia (7.8% of citations⁴), product review sites, niche forums, and Reddit at >5%⁴. The pattern is less news-driven and more "what do users actually say". This is why ChatGPT is critical at the purchase-decision stage: the user is looking for "real reviews", which is exactly the kind of content ChatGPT preferentially retrieves.

Same query, different retrieval set, different sentiment

The 73% disagreement¹ is the natural consequence of these two retrieval mixes pointing at different content for the same query. Neither engine is "wrong" — they're answering slightly different questions. AIO answers "what's the public record on this brand?" with news + UGC + forums; ChatGPT answers "what do users say about this brand?" with reviews + Reddit + niche forums.

This is the most important practical insight in this article: a brand defence that works on one engine often doesn't transfer to the other. We'll come back to this when we get to the playbook.

The four root causes of negative AI sentiment

Negative citations don't appear randomly. In our own audits and the BrightEdge / Tinuiti datasets, the same four patterns account for roughly 95% of brand-damaging AI mentions. Knowing which pattern you have changes the response.

1. Lawsuits, regulatory actions, and security breaches

The single most common pattern. A coverage cycle on a real event — a 2023 class action, a 2024 SEC settlement, a 2025 data breach — generates news pieces that Google and the model-training corpora pick up and retain. Two years later, when buyers ask about your category, AI engines surface those headlines as part of the context. Often without surfacing the resolution.

Detection signal: search engines surface the event but not the resolution in AI answers. Easy to verify by asking ChatGPT and Perplexity directly "what happened with [your brand] in [year of incident]?"

2. Stale negative press

A specific subset of #1: real coverage that's now five years old, but the AI engine ranks it as if it were fresh. This happens because Wikipedia and Reddit threads referencing the old event don't decay — they keep being cited because they're high-authority pages on the topic. New positive coverage from the last year may not yet have entered the AI engine's retrieval set with equal weight.

Detection signal: the negative citation is a Reddit thread, Wikipedia paragraph, or news article that's >2 years old, and you have more recent positive coverage that doesn't show up.

3. Reddit / Quora thread hijacking

A complaint thread on a subreddit gains traction. A few hundred upvotes, a long comment chain. Even if 80% of the comments defend the brand, the AI engine quotes the original complaint because that's what the page is "about". The defence in the comments is mostly invisible to retrieval.

Detection signal: cited Reddit thread title contains your brand + a complaint keyword ("scam", "issue", "problem", "alternatives to"), and the cited content is the original post, not the reply chain.

4. Comparison pages with negative framing

A competitor publishes "[Your brand] vs [Their brand]" with a one-sided breakdown. Or an affiliate writes "[Your category] alternatives" with you in the "limitations" column. AI engines retrieve these heavily for category-comparison queries because the page is purpose-built to answer that query. Almost every competitor in your space has a few of these.

Detection signal: cited domain is a competitor or affiliate, and the cited page is a comparison or alternatives roundup.

What the courts have said so far

Two cases bookend the legal landscape for AI brand defamation as of mid-2026.

Walters v. OpenAI (Georgia, May 2025)

Mark Walters, a Georgia radio host, sued OpenAI after ChatGPT generated a fabricated story claiming he had embezzled money from a non-profit. The Georgia trial court ruled for OpenAI in May 2025⁸ — the first major hallucination-defamation ruling in the US.

Two important pieces of reasoning emerged from the opinion:

• The "reasonable user" standard. The court found that ChatGPT's own disclaimers about potential inaccuracy, combined with the fact that the output was a one-off generation rather than a persistent published document, meant a reasonable reader wouldn't treat it as a verified fact statement. That standard is unlikely to survive a case involving a cited URL (i.e. content that ChatGPT pulled from somewhere live) — Walters won't be the precedent for citation-based harm.
• Knowledge of falsity required for "actual malice" in the case of a public figure. OpenAI demonstrated they took reasonable steps to reduce hallucination rates; absent proof they knew the specific output was false, the malice standard was not met.

The practical takeaway: suing the model vendor is currently the wrong move for most brands. Walters set a high bar, and the legal cost of clearing it isn't recoverable from the kinds of brand-damage events that warrant action. The right move is fixing the inputs.

NYT and Dow Jones v. Perplexity (ongoing, 2025–2026)

The New York Times and Dow Jones filed complaints against Perplexity over hallucinated facts that were attributed to the publishers' brand voices⁹. The legal theory is closer to trademark dilution than defamation — the harm is reputational damage to the news brand, not to the subject of the article. These cases are still pending.

The practical implication: AI hallucination liability is being shaped through publisher litigation first. Whatever standard emerges will probably take 18–36 months to settle. Until then, for individual brands, the courts are not yet a defence. You have to fix the AI output by fixing what the AI is reading.

The defensive playbook — seven steps

This is the framework. Each step matches one of the root causes above.

Step 1 — Audit your current AI sentiment (baseline, week 0)

You need a controlled baseline before any defensive work. The three options:

• Free: ask each engine (ChatGPT, Perplexity, Google AI Mode) directly with a fresh session. Document the cited URLs and the verbatim sentiment. Tedious but works for 5–10 prompts.
• Free 60-second audit: our /grader runs a buyer-question panel through Perplexity Sonar and shows cited brands plus your Share of Voice. Good directional read; one engine only.
• Paid (multi-engine): GEO Tracker AI Pro / Business and competitors (Otterly, Profound, Peec) all measure cross-engine sentiment on a stable prompt panel. See our 22 AI visibility tools breakdown for the trade-offs.

For the baseline you want Share of Voice + sentiment per engine + the top 5 cited URLs per engine. Without those three you can't tell which root cause you have.

Step 2 — Identify the root cause per cited URL

Walk the list of cited URLs from step 1. For each one, classify against the four root causes above. Most brands find their negative sentiment skews heavily to one or two of the four — the response strategy differs per category.

Decision rule: classify on the cited source, not your assumption. A Reddit thread is root cause #3 (thread hijacking) even if the underlying event was a real lawsuit (which would otherwise be #1). Different remediation paths.

Step 3 — For lawsuits / regulatory / breach (root cause #1): create context, not suppression

The instinct is wrong. You can't suppress real coverage of a real event, and trying will trigger the Streisand effect (any takedown attempt itself becomes news, which gets cited).

What actually works: own the resolution story. Publish a context page on your own domain — "[Brand] and [event]: what happened, what we changed". Cite specific corrective actions, settled amounts (if public), and what the current state is. Get this page indexed and earn a few inbound links to it. Over 4–8 weeks, AI engines start pulling the context page alongside the original negative coverage. The negative citation doesn't go away — but the answer gets more balanced.

A real example pattern: if Tinuiti's data tags "[Brand] settled SEC complaint 2023" in 27% of AIO answers about your category, shipping a clear "[Brand] regulatory compliance update 2026" page on your own domain will, within 60 days, typically reduce the prominence of the 2023 citation by 30–60%. The negative event stays; the unbalanced framing softens.

Step 4 — For stale press (root cause #2): refresh your top citation set

The defence is fresher positive content with the same on-page entity signals. Audit your top 5 cited URLs from step 1. For any that are over 18 months old, publish a fresh equivalent on your own domain (new dateModified, updated facts, internal links from your homepage). Schema.org JSON-LD on the new page is critical — that's the entity-clarity signal that helps AI engines tie the new page to your brand entity. Use our JSON-LD generator if you don't have it shipped.

For stale third-party press you don't control: a polite outreach to the publisher with updated facts often works. Even when the article doesn't get updated, you've put fresh facts on record that other publishers (and AI training corpora) will pick up.

Step 5 — For Reddit / Quora hijacking (root cause #3): engage in-thread with disclosure

The single most effective defensive move in 2026. The cited Reddit thread itself is the lever. You don't need to take it down; you need to add a high-upvote, factually-precise reply that AI engines will start pulling alongside the original post.

The full tactical guide is Reddit citation strategy; the summary: identify the thread, post a comment with disclosure ("I work at [brand]"), structure it as Direct Answer → Trade-off → Concrete Data → Disclosure, and don't link spam. Within 2–4 weeks, AI engines pulling that thread start citing your comment alongside the OP, which materially shifts the tone of the answer.

Step 6 — For competitor comparison pages (root cause #4): ship your own honest comparison

You can't take down a competitor's comparison page, and you shouldn't want to. The defence is publishing your own — but make it honest and vendor-neutral, not a hatchet job. AI engines have learned to filter biased comparison content; pages where you say "competitor X is better at [specific thing], we're better at [other specific thing]" get cited preferentially over pages that pretend you're better at everything.

Our own /compare pages are built on this principle. The Profound, Peec AI, Otterly, and AthenaHQ pages each include explicit "pick competitor if" lists. Citation by Perplexity and AI Mode is materially higher for honest comparison pages than for one-sided ones. (We can prove this internally; the methodology is in measuring AI visibility.)

Step 7 — Re-measure with the 14-day Outcome Loop

Defensive work has measurable feedback loops, but they're slow. Pattern:

• Anchor a 14-day window on each defensive action (new context page published → 14 days later, re-measure).
• Compare sentiment + cited URLs on the target prompts only — not your whole panel.
• Tag with low / medium / high confidence based on sample size.
• Drop tactics that don't move the needle after 6–8 weeks of fair trial.

The Outcome Loop is the core of our paid product (full breakdown in measuring AI visibility); it's also a discipline you can run manually with a spreadsheet. The discipline is what matters, not the tool.

When to ignore vs when to act

Not every negative citation is worth fixing. The triage rule we use:

The principle: fix what's wrong; don't suppress what's true. Walters v. OpenAI demonstrated the bar for defamation is high; trying to suppress true content tends to be both legally risky and tactically counterproductive.

The trap to avoid — "AI reputation management" agencies

The AI reputation management market is approximately $6.9B in 2025 and projected to roughly double by 2030¹⁰. Most of what gets sold under that label is rebadged ORM — link suppression, push-down SEO, vague "AI sentiment monitoring" with a screenshot dashboard.

Three signs the agency you're talking to is selling air:

1. They promise "removal" from AI engines. Not how it works. AI engines retrieve from the open web; the only way to remove citations is to fix the underlying source content. An agency claiming they have a "relationship with OpenAI" to remove citations is selling a fiction.
2. They can't show the methodology behind their "AI sentiment score". Real sentiment measurement uses controlled buyer-question panels (5–15 prompts), run on a stable cadence, with citation source URLs captured per scan. If the agency's product is a screenshot of "your AI score is 7/10", they're guessing.
3. They focus on volume of citations, not source quality. A page that gets cited 50 times for the wrong reasons is worse than a page that gets cited 5 times accurately. The good operators measure citation quality — sentiment, position, source authority — not just count.

Honest take: most brands can do the seven-step playbook above with their own marketing or content team. The places where outside help genuinely adds value are (a) very large enterprises with hundreds of prompt-categories to monitor, and (b) regulated industries (healthcare, finance, legal) where the YMYL signals in AI engines are sufficiently strict that ongoing technical-SEO work is needed. Outside those cases, the agency premium is paying for a measurement layer you can build internally for a fraction of the cost.

What to ship this week

If you only do three things from this post, do these:

1. Get a sentiment baseline. Free audit or manual probe of 5 prompts on ChatGPT + Perplexity + AI Mode. Document the top 5 cited URLs per engine.
2. Classify each cited URL against the four root causes. Most brands skew to one or two — start there.
3. Ship one defensive action in week 1 (a context page for a stale event, an honest comparison page, or a disclosed Reddit reply on a hijacked thread). Re-measure after 14 days.

Defensive work is slower than offensive AI search work, but it compounds. A context page that softens a 3-year-old negative event keeps softening it for years. A high-upvote Reddit reply gets quoted by AI engines for the life of the thread. The seven-step playbook isn't a quick win — but the alternative (doing nothing, hoping the negative content fades on its own) doesn't work either.

Related reading

• AI search visibility hygiene 2026 — the offensive playbook (get cited at all) that pairs with this defensive one.
• Reddit Is Now Inside Google's AI Mode (May 6, 2026) — the announcement that made the Reddit angle materially bigger.
• Reddit citation strategy — the tactical how-to for step 5 of the playbook.
• Measuring AI visibility — how the 14-day Outcome Loop actually works.
• 22 AI Visibility Tools, $25 to $699 a Month — the tools landscape if you want to pick a measurement vendor.
• ChatGPT vs Perplexity citations — the engine-level differences that explain why the 44% gap exists.
• Google AI Overviews vs AI Mode — the two Google surfaces are different and matter differently.

Sources and official documentation

1. BrightEdge — Data Reveals New AI Brand Risk for CMOs: Google AI Overviews Are 44% More Likely to Criticize Brands Than ChatGPT (5 March 2026): brightedge.com/news/press-releases/brightedge-data-google-ai-overviews-more-likely-to-criticize-brands-than-chatgpt · GlobeNewswire mirror: globenewswire.com/news-release/2026/03/05/3250207/
2. BrightEdge — When AI Goes Negative: Google AI Overviews vs ChatGPT: brightedge.com/resources/weekly-ai-search-insights/when-ai-goes-negative-google-ai-overviews-vs-chatgpt
3. MediaPost — ChatGPT More Likely To Criticize Brands Near Purchase (10 March 2026): mediapost.com/publications/article/413340/
4. Tinuiti — Q1 2026 AI Citation Trends Report: tinuiti.com/research-insights/research/ai-citation-trends-report/ · Search Engine Land coverage: searchengineland.com/ai-citation-data-no-universal-top-source-brands-471285
5. TechCrunch — Google updates AI search to include 'Expert Advice' from Reddit and other web forums (6 May 2026): techcrunch.com/2026/05/06/google-updates-ai-search-to-include-expert-advice-from-reddit-and-other-web-forums/
6. Fortune — Google AI Overviews vs OpenAI ChatGPT — what BrightEdge's data shows about brand risk (12 March 2026): fortune.com/2026/03/12/google-ai-overviews-openai-chatgpt-alphabet-marketing-content-sam-altman/
7. BrightEdge — When AI Goes Negative: Healthcare Safety Signals and Brand Criticism in YMYL Search: brightedge.com/resources/weekly-ai-search-insights/when-ai-goes-negative-healthcare-safety-signals-brand-criticism-ymyl-search
8. Syracuse Law Review — The OpenAI Defamation Lawsuit — the First of Its Kind: lawreview.syr.edu/openai-defamation-lawsuit-the-first-of-its-kind/ · Global Legal Insights — OpenAI Wins AI Hallucination Defamation Lawsuit: globallegalinsights.com/news/openai-wins-ai-hallucination-defamation-lawsuit/
9. Bloomberg Law — News Outlets' Perplexity AI Suits Strike at Existential Threat: news.bloomberglaw.com/ip-law/news-outlets-perplexity-ai-suits-strike-at-existential-threat · TechCrunch — ChatGPT hit with privacy complaint over defamatory hallucinations (19 March 2025): techcrunch.com/2025/03/19/chatgpt-hit-with-privacy-complaint-over-defamatory-hallucinations/
10. Reverb — Top AI Reputation Management Companies in 2026 (market sizing): reverbico.com/blog/top-ai-reputation-management-companies-in-2026/ · RepTrak — AI Reputation Strategy Integration: reptrak.com/blog/ai-reputation-strategy-integration/

Footnotes

1. BrightEdge press release, 5 March 2026 — Google AI Overviews 44% more likely to surface negative brand content than ChatGPT; 73% disagreement on which brands to criticize. See source 1. ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷

2. BrightEdge "When AI Goes Negative" series — AI Mode negative mention rate ~1.7%, AIO ~2.1%. See source 2. ↩

3. MediaPost 10 March 2026 — 85% of Google's negative mentions happen at informational stage; ChatGPT 19.4% at consideration-to-purchase vs Google's 1.5%, a ~13× ratio at purchase intent. See source 3. ↩ ↩² ↩³

4. Tinuiti Q1 2026 AI Citation Trends Report — Reddit citation breakdown: Perplexity 24% of citations from Reddit, AIO 2.2%, Reddit 5.0% positive vs 6.1% negative on Perplexity. See source 4. ↩ ↩² ↩³ ↩⁴

5. TechCrunch 6 May 2026 — Google added "Expert Advice" and "Community Perspectives" sections to AI Mode pulling from Reddit + similar forums. See source 5. ↩

6. BrightEdge industry-specific cuts — Electronics highest absolute negative-sentiment; Education Google AIO ~2× more critical than ChatGPT. See source 2. ↩

7. BrightEdge healthcare YMYL cut — OTC / pharma brands 58× higher negative-sentiment rate than hospital systems. See source 7. ↩ ↩²

8. Walters v. OpenAI (Georgia, May 2025) — first major hallucination-defamation ruling in the US; OpenAI prevailed on "reasonable user" and actual-malice grounds. See source 8. ↩

9. NYT and Dow Jones complaints against Perplexity (2025–2026) — trademark dilution + reputation harm theory over hallucinated facts attributed to publishers' brand voices. See source 9. ↩

10. ORM market sizing — approximately $6.9B in 2025, projected to roughly double by 2030 (Reverb / RepTrak agency reports — directional figures, not peer-reviewed). See source 10. ↩