How AI Search Is Recommending Patent Attorneys: Monthly Trends

Executive Summary

Fish & Richardson holds first place in AI search recommendations for patent attorneys at 61.4% valid recommendation coverage in October 2026, up 2.9 points from 58.5% in July 2026, and the category leader is unchanged. The gap to the next brand, Wolf Greenfield at 35.7%, stands at a 25.7-point cushion. The coverage order has shifted meaningfully this month: Wolf Greenfield moved from fourth into second, displacing Finnegan, whose coverage fell from 39.0% in August to 22.4% in September before a partial recovery to 30.0% in October.

Wolf Greenfield is the category's strongest upward mover and the only brand flagged for significant movement. Its coverage rose 18.7 points from 17.0% in July to 35.7% in October, a significant gain, with a top-three rate of 32.9% (up 26.5 points) and a rank-one rate of 22.9% (up 21.8 points). Against September, coverage rose 18.6 points from 17.1%, also significant, placing it second in the category for the first time in the series.

Finnegan recorded the sharpest decline against baseline, falling 12.5 points from 42.5% in July to 30.0% in October, though this sits just inside the significance threshold. Against the immediately prior month the picture reverses: Finnegan rose 7.6 points from 22.4% in September to 30.0% in October, a recovery that falls within normal month-to-month variation, even as its four-month baseline level sits well below July.

The category was active rather than stable this month. One brand exceeded normal month-to-month variation, the leadership position held, and the second and third places changed hands. This is a repositioning month, not a quiet one.

Each monthly run begins with 570 prompt-surface observations (394 unique questions) in October 2026, against 427 prompt-surface observations (299 unique questions) in July 2026, across the benchmark's defined AI/search surface universe. In October, 569 mentioned a tracked brand or competitor, 89 were relevant and 480 were irrelevant. In July, all 427 mentioned a tracked brand or competitor, 101 were relevant and 326 were irrelevant. The public metrics use the qualified observation counts of 94 in July and 70 in October that survive both qualification stages.

Key Findings

AI Response Inconsistency Alerts

Three critical or high-severity factual inconsistencies were detected across three AI platforms, all concerning Kilpatrick Townsend.

Kilpatrick Townsend

AI platforms provided conflicting information about partner compensation ranges at Kilpatrick Townsend, a high-severity pricing conflict. When asked "How much do partners at Kilpatrick Townsend make?", Copilot stated that "partners typically earn between about $158,000 and $208,000 annually, with average around $176,000–$201,000," while ChatGPT stated that "Glassdoor shows $279K–$485K/year for partners; equity partners can be substantially higher." The two ranges are incompatible, with Copilot's ceiling of $208K falling well below ChatGPT's floor of $279K. Copilot cited CareerBliss and Salary.com, and ChatGPT referenced Glassdoor and the firm's Am Law 100 materials. A flagged source, the CareerBliss partner salary page, was cited on the Copilot side with an excerpt reading "Kilpatrick Townsend & Stockton Partners earn $201,000 annually."

Responses differed again on partner compensation when Gemini entered the picture. When asked the same question, Copilot gave the same $158,000 to $208,000 range, while Gemini stated that "non-equity partners typically earn $350,000 to $600,000+; equity partners earn well above $1.5M–$2M+." The gap here is larger still, with Copilot's maximum sitting more than $140K below Gemini's stated non-equity floor. Gemini referenced BigLaw Bear, and Copilot again drew on CareerBliss and Salary.com, with the same CareerBliss page flagged on the Copilot side.

The platforms also diverged on profits per partner, a separate high-severity pricing conflict. When asked how much partners at Kilpatrick Townsend make, Gemini stated that "profits per equity partner hover around $1.9 million," while ChatGPT stated that "Kilpatrick Townsend reported $1.357 million in profits per partner in 2020." These two figures cannot both describe the same metric for the same period. Gemini cited BigLaw Bear, ChatGPT cited Glassdoor, the firm's own Am Law materials, and Am Law 100 rankings, and the BigLaw Bear page was flagged on the Gemini side carrying a "Profits per partner $1.9M" excerpt.

Benchmark Context

Questions This Section Answers

• How many patent attorney prompts qualified for the October benchmark compared with July?
• Why are coverage percentages calculated within the qualified observation set rather than the raw collection universe?

The report separates the raw collection universe from the qualified analysis set. Brand-level recommendation percentages are calculated within the qualified benchmark set.

The October qualified base of 70 observations is the smallest in the four-month series, down from 94 in July, while the raw collection universe grew from 427 to 570 prompt-surface observations. August carried 77 qualified observations and September 76, both at a breadth of 6 surfaces, matching October.

Benchmark-Level Metrics

The share of qualified observations producing recommendation-shaped answers rose from 28.7% in July to 65.7% in October, and the valid recommendation shortlist share rose from 68.1% to 85.7%, the highest of the series. The intermediate months ran lower on the first measure (55.8% in August, 57.9% in September) before October's climb.

AI Recommendation Trend

Questions This Section Answers

• Which patent attorney firms lead AI search recommendations in October 2026?
• How did the upper tier of the patent attorney rankings change between July and October?

Fish & Richardson Holds First as Wolf Greenfield Overtakes Finnegan for Second

Fish & Richardson remains first with a 25.7-point cushion. The material change is beneath the leader: Wolf Greenfield moved into second place ahead of Finnegan, compressing the upper field into a new order. Finnegan and Knobbe Martens now form the next tier at 30.0% and 20.0% respectively, behind Wolf Greenfield's 35.7%, while Kilpatrick Townsend sits at 17.1% and Sterne Kessler at 10.0%.

Only Wolf Greenfield exceeded normal month-to-month variation in coverage between July and October. The category-level picture reflects that single large riser sitting alongside a combination of smaller movements, including Finnegan's and Knobbe Martens' baseline declines and the modest gains by Fish & Richardson, Kilpatrick Townsend, and Sterne Kessler.

What Changed This Month

Questions This Section Answers

• Which firm was the only significant riser in patent attorney AI recommendations this month?
• How did Wolf Greenfield's rank-one placements and recommendation conversion drive its move into second place?
• Why did Finnegan's October recovery still leave it behind Wolf Greenfield?

Wolf Greenfield: The Category's Only Significant Riser

Wolf Greenfield's valid recommendation coverage rose 18.7 points from 17.0% in July to 35.7% in October, a significant gain beyond normal variation. Against the prior month, coverage rose 18.6 points from 17.1% in September to 35.7% in October, also significant. The series ran 17.0% in July, 24.7% in August, 17.1% in September, then 35.7% in October, so the firm's climb is concentrated in the current month.

The gain shows up across every placement tier. Top-three rate rose 26.5 points to 32.9%, and rank-one rate rose 21.8 points to 22.9%. Raw mention presence rose 16.3 points to 38.6%. The firm appeared in 27 of 70 qualified observations in October with 25 valid recommendations, compared with 21 of 94 observations and 16 valid recommendations in July. Rank-one count rose from 1 to 16, and top-three count rose from 6 to 23. Net sentiment stood at 0.9 in October versus 1.0 in July.

The distinction worth noting is that Wolf Greenfield's October rank-one rate of 22.9% is now the highest in the category, marginally above Fish & Richardson's 21.4%, even though Fish & Richardson's overall coverage is nearly twice as high. At 27 appearances and 25 valid recommendations, the firm is converting almost every appearance into a recommendation.

Highest-priority diagnostic: Which prompts and AI surface families produced the rank-one gains, and which evidence sources AI systems cite when placing Wolf Greenfield first?

Finnegan: A Four-Month Baseline Decline That Hides a One-Month Rebound

Finnegan's valid recommendation coverage fell 12.5 points from 42.5% in July to 30.0% in October, a decline that sits inside the significance threshold. The prior-month movement runs the other way: coverage rose 7.6 points from 22.4% in September to 30.0% in October. The series ran 42.5% in July, 39.0% in August, 22.4% in September, then 30.0% in October, so the current reading is a partial recovery from September's trough rather than a continuation of decline.

Raw mention presence fell 21.4 points from 58.5% in July to 37.1% in October, and top-three rate fell 14.7 points to 25.7%. Rank-one rate fell 4.2 points to 4.3%. The firm appeared in 26 of 70 qualified observations in October with 21 valid recommendations, compared with 55 of 94 observations and 40 valid recommendations in July. Rank-one count fell from 8 to 3, and top-three count fell from 38 to 18. Net sentiment was 0.8 in October versus 0.9 in July.

The distinction is that Finnegan's recovery in coverage did not fully restore its presence. The firm is being mentioned less often than it was in July even as October's recommendation rate improved over September, which means its ranking against Wolf Greenfield turned on placement quality rather than visibility.

Highest-priority diagnostic: Which prompts account for the sustained presence decline, and where does Wolf Greenfield now appear in prompts Finnegan held in July?

Fish & Richardson: Leader Widens Its Coverage Lead

Fish & Richardson's valid recommendation coverage rose 2.9 points from 58.5% in July to 61.4% in October, within normal variation. The current leader sits above its baseline and well above its September reading of 43.4%, a swing of 18.0 points from September to October that was flagged as significant against the prior month. The series ran 58.5%, 58.4%, 43.4%, 61.4%.

Top-three rate rose 2.2 points to 54.3%, and rank-one rate fell 16.9 points to 21.4%. Raw mention presence fell 16.5 points to 68.6%. The firm appeared in 48 of 70 qualified observations in October with 43 valid recommendations, versus 80 of 94 observations and 55 valid recommendations in July. Rank-one count fell from 36 to 15, and top-three count fell from 49 to 38, even as the top-three rate rose because the qualified observation base also shrank. Net sentiment held at 0.9.

The distinction is that Fish & Richardson's overall coverage lead widened even as its rank-one strength eroded. The firm remains the most visible and most frequently recommended brand, but Wolf Greenfield now edges it for first-place placements. At 43 valid recommendations from 48 appearances, the firm keeps a strong conversion rate.

Highest-priority diagnostic: Which surfaces and prompt types carried the rank-one decline, and is the loss concentrated where Wolf Greenfield gained?

Knobbe Martens and the Lower Tier

Knobbe Martens fell 10.9 points from 30.9% in July to 20.0% in October, inside the significance threshold, having held third place through the summer before moving to fourth. Raw mention presence fell 20.4 points to 20.0%. The firm appeared in 14 of 70 qualified observations with 14 valid recommendations, compared with 38 of 94 observations and 29 valid recommendations in July.

Kilpatrick Townsend rose 3.3 points to 17.1% from 13.8% in July, with presence essentially flat at 22.9% and 16 appearances holding 12 valid recommendations. Sterne Kessler rose 3.6 points to 10.0% from 6.4% in July, its second consecutive month of improvement, converting all 7 of its appearances into recommendations. Harrity & Harrity fell 4.5 points to 2.9% on 2 valid recommendations. Cantor Colburn and Schwegman Lundberg each fell to 0.0% coverage in October after holding a small 1.1% share in July, while Banner Witcoff remained at 0.0% coverage across the full series.

The clearest lower-tier signal is Knobbe Martens' presence decline pairing with falling coverage, while Kilpatrick Townsend holds presence and Sterne Kessler converts all of it. For the three zero-coverage brands, the question is whether the absence reflects the current prompt set or a genuine gap in AI-cited source material.

Highest-priority diagnostic: Which prompt types account for Knobbe Martens' lost presence, and do the same prompts now surface Kilpatrick Townsend or Sterne Kessler?

Buyer-Intent Interpretation

Questions This Section Answers

• What buyer-intent clusters does the October patent attorney benchmark actually cover?
• What can the benchmark not yet show about how AI systems handle pricing or head-to-head comparisons between firms?

All 70 qualified observations in October fell into the Brand Recommendation cluster, as did all 94 in July, 77 in August, and 76 in September. The benchmark found no qualified observations in the Pricing & Value or Multi-Brand Comparison clusters, which means the current prompt set does not yet expose how AI systems handle cost questions or direct head-to-head comparisons between patent attorney firms.

This is a real limitation for anyone reading the coverage figures as a full commercial picture. The benchmark can show which firms AI recommends for general patent attorney queries, but it cannot yet answer whether AI positions certain firms as premium, value-oriented, or comparable to specific rivals. The inconsistency alerts above are the closest the current data comes to pricing, and they concern partner compensation figures rather than client-facing fees.

Brand Opportunity Summary

Questions This Section Answers

• Which brand signals and diagnostics should each patent attorney firm prioritize based on October coverage?
• What explains the rank-one rate decline for the category leader despite its widening coverage lead?
• What should the zero-coverage firms investigate about their absence from AI answers?

The benchmark identifies where attention is warranted; a company-level analysis is needed to explain why.

Evidence Behind the Benchmark

The aggregate metrics are built from prompt-level observations (query, surface, recommendation outcome, rank, sentiment, and citations where exposed). Company-level analysis can go deeper into prompt, competitor, surface, and evidence patterns. Source presence is not automatically treated as proof of causation.

About This Benchmark

This report is part of the CiteWorks Studio AI Visibility Market Discovery research program.

• AI Visibility Industry Market Methodology
• AI Visibility Industry Market Metrics
• AI Visibility Industry Market Standards

Report-Specific Interpretation Notes

• Qualified observation counts differ across months (94 in July, 77 in August, 76 in September, 70 in October); coverage percentages are calculated within each month's qualified set, so comparisons reflect rates, not raw counts.
• Movement between months identifies changes worth investigating, not established causes.
• Small-count brands (Cantor Colburn, Harrity & Harrity, Schwegman Lundberg, Banner Witcoff) carry limited weight, and their percentage moves should be read alongside the underlying counts.
• The qualified denominator is smaller than the raw collection universe because many collected prompts fall outside the patent attorney category scope.
• A brand appearing more often in AI answers does not necessarily mean it is being recommended more often, and vice versa.
• Significant movements flag changes beyond normal month-to-month variation; directional analysis identifies where to investigate, not what caused the movement.

Next Step

The Public Benchmark Shows Where a Brand Is Winning or Losing. A Company-Level Audit Shows Why.

The aggregate percentages in this report raise questions that only prompt-level inspection can answer. Which high-intent queries is a firm winning, and which is it losing? When AI recommends a competitor instead, which competitor takes the placement? What attributes do AI systems associate with each firm, and which external sources are shaping those answers?

A company-specific AI visibility audit maps those prompt, surface, competitor, ranking, sentiment, and evidence-source patterns into a prioritized visibility strategy. Where the benchmark shows a coverage shift, the audit identifies whether the change is concentrated in specific prompts, surfaces, or competitor substitutions.

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