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Research/Does AI praise the brands it ranks first?

Does AI praise the brands it ranks first?

More often, but praise is common throughout the list. AI used positive language in 640,708 of 788,976 descriptions for brands ranked fourth or lower, or 81.21%.

By Dimitry Apollonsky · August 12, 2026 · 11 min read

81.21%
of descriptions for brands ranked fourth or lower were positive
640,708 of 788,976 brand-description assignments
▸Contents
  • Four in five descriptions at fourth or lower were positive
  • First place was more positive, but praise extended down the list
  • ChatGPT Search had more than twice Google's rank gap
  • Positioning claims lost the most positivity below first place
  • Excellent was the most common positive word at every rank
  • Lower-rank positivity ranged from 70.15% to 90.46% by industry
  • Alphabet and Microsoft drew the most praise at fourth or lower
  • The 81.21% result held under three narrower checks
  • What marketers should do
  • Get the data
  • Sources
  • Related research
Contents
  • Four in five descriptions at fourth or lower were positive
  • First place was more positive, but praise extended down the list
  • ChatGPT Search had more than twice Google's rank gap
  • Positioning claims lost the most positivity below first place
  • Excellent was the most common positive word at every rank
  • Lower-rank positivity ranged from 70.15% to 90.46% by industry
  • Alphabet and Microsoft drew the most praise at fourth or lower
  • The 81.21% result held under three narrower checks
  • What marketers should do
  • Get the data
  • Sources
  • Related research

In one observed cut of the Parse mirror, we analyzed 1,800,746 brand-description assignments across 71,801 brands, 218,189 AI answers, and 16,103 organic prompts on ChatGPT Search and Google AI Mode from May 24 through July 16, 2026.

81.21%
of descriptions for brands ranked fourth or lower were positive
640,708 of 788,976
94.09%
of descriptions for first-ranked brands were positive
367,591 of 390,696
12.88 points
separated first place from fourth or lower
94.0862% versus 81.2075%
1.80M
brand-description assignments were analyzed
1,800,746 across 71,801 brands

Four in five descriptions at fourth or lower were positive

AI used positive language in 640,708 of 788,976 descriptions for brands ranked fourth or lower, or 81.2075%. A description is an adjective, short phrase, or positioning claim attached to one ranked brand in one observed AI answer.

Positive language is not evidence that a brand won the recommendation. Use sentiment to understand framing, then use recommendation rank to understand the shortlist decision.

81.21%
of descriptions at fourth or lower were positive
640,708 of 788,976

Takeaway

Treat positive sentiment as framing, not as proof that the brand won the recommendation.

First place was more positive, but praise extended down the list

Positive language appeared in 367,591 of 390,696 descriptions for first-ranked brands, or 94.0862%. The rate was 88.7007% for brands ranked second or third and 81.2075% for brands ranked fourth or lower. First place exceeded fourth or lower by 12.8787 percentage points.

Rank and sentiment move together, but they are not interchangeable. Benchmark a brand against others at similar positions before treating a favorable-sentiment score as a competitive lead.

Positive description rate by recommendation rank
  • First94.0862% (367,591 of 390,696)
  • Second or third88.7007% (550,897 of 621,074)
  • Fourth or lower81.2075% (640,708 of 788,976)

ChatGPT Search had more than twice Google's rank gap

ChatGPT logoChatGPT Search's positive-description rate fell from 91.2108% at first place to 74.0176% at fourth or lower, a 17.1932-point gap. Google logoGoogle AI Mode fell from 96.3915% to 89.3889%, a 7.0026-point gap. The ChatGPT logoChatGPT Search gap was 2.4553 times as large.

A blended sentiment benchmark hides how differently the engines separate winners from the rest of the list. Track the same priority prompts on both engines and Compare logoCompare sentiment among brands at the same rank within each one.

First-to-fourth positivity gap
  • ChatGPT logoChatGPT Search17.1932 points
  • Google logoGoogle AI Mode7.0026 points

Takeaway

Keep separate rank-and-sentiment baselines for ChatGPT logoChatGPT Search and Google logoGoogle AI Mode.

Positioning claims lost the most positivity below first place

At fourth or lower, 289,109 of 323,173 adjectives were positive, or 89.4595%. The rate was 76.9569% for other phrases and 67.3866% for positioning claims. Positioning claims fell 24.4346 points from their 91.8212% first-place rate.

A single overall sentiment score can hide which language changed. Separate simple adjectives from claims about who the brand is for, what it leads on, or where it fits.

Positive description rate at fourth or lower
  • Adjectives89.4595% (289,109 of 323,173)
  • Other phrases76.9569% (303,237 of 394,035)
  • Positioning claims67.3866% (48,362 of 71,768)

Excellent was the most common positive word at every rank

The word excellent appeared 5,376 times at first place, 10,557 times at second or third, and 11,240 times at fourth or lower. Strong, recommended, best, and comprehensive also appeared in the leading terms for every rank group.

Generic praise is shared across the list. Audit the specific attributes and positioning claims attached to the brand instead of counting common approval words as differentiation.

Common positive words by recommendation rank
Excellent5,37610,55711,240
Strong2,8426,8919,657
Recommended3,9224,7447,804
Comprehensive3,6715,0694,514
Best4,9725,1024,488

Lower-rank positivity ranged from 70.15% to 90.46% by industry

Among industries with at least 1,000 descriptions in each displayed rank group, Clothing and Apparel had 3,709 positive descriptions in 4,100 at fourth or lower, or 90.4634%. Blockchain and Cryptocurrency had 1,598 in 2,278, or 70.1493%. Its 23.4989-point gap from first place was the largest in the displayed set.

Use an industry baseline before calling a brand's language unusually positive. The spread identifies where a rank-and-language review is useful and does not show that industry caused the difference.

Selected fourth-or-lower positive rates
  • Clothing and Apparel90.4634% (3,709 of 4,100)
  • Manufacturing87.5712% (5,841 of 6,670)
  • Food and Beverage85.8125% (5,994 of 6,985)
  • Financial Services77.5818% (28,509 of 36,747)
  • Biotechnology74.7594% (1,709 of 2,286)
  • Blockchain and Cryptocurrency70.1493% (1,598 of 2,278)

Alphabet and Microsoft drew the most praise at fourth or lower

Alphabet had 6,934 positive descriptions among 8,658 at fourth or lower, or 80.0878%. Microsoft logoMicrosoft had 5,439 of 6,898, or 78.8489%. Grafana, Notion logoNotion, Amazon logoAmazon, Atlassian, LinkedIn logoLinkedIn, Monday logoMonday.com, ClickUp logoClickUp, and SigNoz completed the ten largest positive-description counts at those ranks.

This is a volume leaderboard, not a brand-quality ranking. Use it to find repeated lower-rank praise worth reviewing, then Compare logoCompare each brand's wording, prompt mix, and rank rather than its raw count alone.

Named brands praised at fourth or lower
Alphabet6,9348,65880.0881,733
Microsoft logoMicrosoft5,4396,89878.8491,590
Grafana4,0514,88882.876196
Notion logoNotion3,5824,67676.604462
Amazon logoAmazon3,4664,41278.559976
Atlassian3,4004,50075.556382
LinkedIn logoLinkedIn2,7273,27383.318358
monday.com faviconmonday.com2,6603,09485.973221
ClickUp logoClickUp2,6353,34678.751291
SigNoz2,3172,70785.593105

Takeaway

Compare logoCompare repeated praise with recommendation rank before prioritizing a brand-language change.

The 81.21% result held under three narrower checks

The main fourth-or-lower rate was 81.2075%. A June 1 start returned 81.2080%, and exact brand identities without parent consolidation returned 81.4238%. Counting each ranked brand once instead of each description found positive language on 337,469 of 401,496 fourth-or-lower recommendations, or 84.0529%.

The study excluded descriptions that could not be linked to a ranked brand or stable brand identity. It describes observed answer language, not recommendation accuracy, brand quality, buyer opinion, why an engine chose a rank, or causation.

81.2075%
main observed cut
640,708 of 788,976
81.2080%
June 1 through July 16
640,597 of 788,835
81.4238%
exact brand identities
668,015 of 820,417
84.0529%
one count per recommendation
337,469 of 401,496

What marketers should do

Positive language appeared in 81.2075% of descriptions for brands ranked fourth or lower. The rate was higher at first place, but the size of that gap differed by engine, description form, and industry.

Report recommendation rank and sentiment as separate measures. Audit the same prompts on both engines. Compare logoCompare like ranks within the same industry. Review specific attributes and positioning claims instead of optimizing for generic praise. Repeat the fixed-window method next quarter before calling a difference movement.

Get the data

Dataset CSVThe metrics behind every figure in this report.

Sources

  1. Semrush: AI Visibility Brand Performance Reports · accessed 2026-08-12
  2. BrightEdge: Google AI Overviews are 44% more likely to criticize brands than ChatGPT · accessed 2026-08-12
  3. G2: The Answer Economy, 2026 AI Search Insight Report · accessed 2026-08-12
  4. Evaluating position bias in large language model recommendations · accessed 2026-08-12

Related research

What words AI uses to describe brands
AI calls almost every brand excellent. Across 719,860 descriptors, 85% are positive and most are interchangeable praise. The asset is owning the word it won't share.
Does AI criticize its top recommendation?
Sometimes. Of 150,193 top-ranked brand recommendations, 5,460, or 3.64%, came with a specific negative claim about the brand in the same AI answer.
Mention vs recommendation: when AI actually picks you
Being named in an AI answer is not the same as being recommended. Only about one in eight named brands is the answer's actual pick.

About this research

Dimitry Apollonsky

Founder, Parse

I built Parse to track where AI answers really come from: the sources they cite and the brands they name. DM me on LinkedIn to talk shop.

Measure recommendation rank and the language around each brand separately.

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