Make Money Online: AI Visibility Market Discovery Index

Benchmark Summary

Questions This Section Answers

• Which brands lead the Make Money Online benchmark in October 2026, and how wide is the gap between them?
• Which brands posted the largest recommendation coverage gains since the July 2026 baseline?
• What changed in recommendation-shaped answer share and valid recommendation shortlist share this month?

Upwork leads the Make Money Online benchmark in October 2026 with valid recommendation coverage of 72.7%, followed by Fiverr at 68.2%. Upwork's lead over Fiverr is 4.5 points.

The largest coverage increase since baseline belongs to Upwork, which rose 21.5 points, from 51.2% in July 2026 to 72.7% in October 2026. Fiverr posted the next largest gain, moving to 68.2% in October 2026 from 51.7% in July 2026, an increase of 16.5 points. No brand recorded a significant decline from the July 2026 baseline in the current series.

Movement was widespread in October 2026: Amazon, Etsy, Fiverr, Rover, Survey Junkie (Acquiring Company: DISQO, Inc.), TaskRabbit, and Upwork all rose beyond normal month-to-month variation from July 2026 to October 2026. Against the prior month (September 2026), Fiverr, Rover, Survey Junkie (Acquiring Company: DISQO, Inc.), Upwork, and Swagbucks each moved beyond normal variation. Rover and TaskRabbit each rose in every tracked month since the July 2026 baseline, and Survey Junkie (Acquiring Company: DISQO, Inc.) rose in each of the two months since August 2026.

The benchmark began with 800 prompt-surface observations in each month and produced 352 qualified observations in October 2026 after qualification.

Current Benchmark at a Glance

Qualified surface breadth refers to the number of canonical AI/search surface families with at least one qualified observation: ChatGPT, Copilot, Gemini, Perplexity, AI Overviews, and AI Mode.

September 2026 posted a recommendation-shaped answer share of 67.5% and a valid recommendation shortlist share of 57.8%, between the July 2026 baseline and the October 2026 result.

For the strategic interpretation of this benchmark, read CiteWorks Studio's analysis of How AI Search Is Recommending Make Money Online

Research Scope and Qualification

Questions This Section Answers

• How many raw prompt-surface observations became qualified benchmark observations in October 2026?
• What is the difference between the raw collection and the public qualified denominator used for brand metrics?

The public benchmark is narrower than the raw collection universe by design. Brand-level percentages use the qualified observations as the public denominator, not the raw collection.

Brand-level percentages use the qualified observations as the public denominator, not the raw collection. Learn more in the AI Visibility Market Discovery Methodology.

Current Brand Standings

Questions This Section Answers

• Which brands lead on presence rate, valid recommendation coverage, top-three rate, and rank-one rate?
• How do Upwork and Fiverr differ across coverage, top-three, and rank-one metrics?
• Which tracked brands recorded low or no valid recommendation coverage in this benchmark?

How to Read the Standings

• Presence rate: share of qualified observations where the brand is mentioned at all.
• Valid recommendation coverage: share of qualified observations where the brand receives a valid recommendation.
• Top-three rate: share of observations where the brand appears among the top three recommended options.
• Rank-one rate: share of observations where the brand is the single top recommendation.
• Net sentiment: positive mentions minus negative mentions as a share of all sentiment-bearing mentions.

For formulas and denominator rules, see AI Visibility Market Discovery Metric Definitions.

Recommendation Coverage Movement

Questions This Section Answers

• Which brands moved beyond normal month-to-month variation from the July 2026 baseline to October 2026?
• How much did Upwork's coverage, top-three rate, and rank-one rate each climb over that period?
• Which tracked brand declined in valid recommendation coverage since the baseline?

Shopify was tracked through August 2026 but recorded no valid recommendation coverage in the current month.

Several brands moved beyond normal month-to-month variation from September 2026 to October 2026. Fiverr rose 13.6 points over that period, Upwork climbed 16.2 points, Rover added 7.2 points, and Survey Junkie (Acquiring Company: DISQO, Inc.) gained 8.7 points. Swagbucks rose 9.7 points from September 2026 to October 2026. Shopify POS declined 2.6 points over the same period.

Largest Increase: Upwork

Upwork's valid recommendation coverage rose to 72.7% in October 2026 from 51.2% in July 2026, an increase of 21.5 points. The climb from the prior month was also beyond normal variation, up 16.2 points from 56.5% in September 2026. Its top-three rate rose to 38.1% in October 2026 from 17.0% in July 2026, and its rank-one rate climbed to 25.6% from 8.1% over the same span, both beyond normal variation.

Category Leader: Upwork

Upwork holds the top position with valid recommendation coverage of 72.7% in October 2026, up from 51.2% in July 2026. Its rank-one rate of 25.6% in October 2026 is the highest in the current standings, ahead of Swagbucks at 12.2% and Fiverr at 11.1%.

Recommendation Placement Snapshot

Questions This Section Answers

• Why can two brands with similar coverage still have different recommendation strength?
• Which leading brand has the highest rank-one rate, and which has the highest top-three rate?

Coverage alone does not show how prominently a brand is recommended. Among the leading brands, placement profiles differ meaningfully.

Upwork holds the highest rank-one rate at 25.6% in October 2026, while Fiverr shows the highest top-three rate among the three at 35.2%. Close coverage can still hide different first-position rates.

Buyer-Intent Distribution

Questions This Section Answers

• Which buyer-intent classes contain qualified observations in this benchmark?
• Are pricing, value, or multi-brand comparison questions currently measured in the public series?

All qualified observations in both months fell into the Brand Recommendation class.

The current public series measures brand recommendation discovery and does not yet contain qualified observations in the pricing and value or multi-brand comparison classes.

Historical Measurement Record

Evidence and Source Layer

The benchmark is built from prompt-level observations that retain the query, AI/search surface, answer, brand outcome, recommendation placement, sentiment, and where exposed, citations or attributable evidence sources. Source presence is evidence about the information environment. It is not automatically proof that the source caused the recommendation.

Scope Boundaries

This public benchmark does not measure:

• Market share or sales attribution
• Every possible AI response to every possible query
• Organic-search ranking positions
• Social mention volume or sentiment outside AI surfaces
• Private, gated, or sponsored AI channels
• Causality from a metric movement alone

Top 10 Cited Domains

Questions This Section Answers

• Which domains are cited most across AI platform responses in October 2026?
• How concentrated are citations across the top 10 domains?
• Do any tracked Make Money Online brand domains appear among the most-cited sources?

Across all AI platform responses in October 2026, 6,972 citations were observed across 1,399 unique domains.

Citations are broadly distributed: the top 10 domains together account for roughly 29% of all citations, and no single domain exceeds 8%. None of the tracked brands' own domains appear in the top 10. The list is dominated by platform sites (YouTube, Google) and community or editorial sources (Reddit, Medium), alongside consumer-finance and gig-economy publishers such as NerdWallet, Finder, MoneyLion, and Side Hustle Nation.

About This Benchmark

The LLM Authority Index AI Visibility Market Discovery Index tracks how often brands appear and are recommended across six major AI and search surfaces, providing a neutral, repeatable measure of AI-era brand visibility and recommendation coverage.

• AI Visibility Market Discovery Index
• AI Visibility Market Discovery Methodology
• AI Visibility Market Discovery Metric Definitions
• AI Visibility Market Discovery Research Standards
• Modeled AI Visibility Authority Value

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