How AI Search Is Recommending Sports Nutrition and Protein Supplements: Monthly Trends

Executive Summary

Optimum Nutrition remains the category leader in September 2026, being recommended by AI systems in 83.3% of qualified observations. The gap to the next brand is narrow, with Transparent Labs close behind at 81.4%. This two-brand dominance at the top continues a pattern seen in August 2026, when Optimum Nutrition led with 84.8% coverage against Transparent Labs' 78.1%.

Transparent Labs was the strongest upward mover this month, increasing its valid recommendation coverage by 3.3 points from 78.1% in August 2026 to 81.4% in September 2026. This movement narrows the gap to the leader considerably, though it was not classified as a significant riser. The brand's raw mention presence also rose from 81.7% to 84.9%, and its rank-one recommendation rate climbed from 32.6% to 34.1%.

The sharpest declines came from MuscleTech and Kaged, both registering significant drops. MuscleTech's valid recommendation coverage fell 3.7 points from 11.8% in August 2026 to 8.1% in September 2026, while Kaged declined 3.0 points from 8.3% to 5.3% over the same period. Both declines were classified as significant, marking the only material movements in an otherwise stable month.

The overall category structure held firm between August and September 2026, with most brands experiencing only minor fluctuations. Beyond the two significant decliners, the remaining eight tracked brands were classified as stable, suggesting that the competitive dynamics in this vertical are concentrated at the margins rather than undergoing wholesale repositioning.

Each monthly run begins with 800 prompt-surface observations (626 unique questions in August 2026 and 629 in September 2026) across the benchmark's defined AI/search surface universe. Of those, all 800 in each month mentioned a tracked brand or competitor; 794 were relevant and 6 were irrelevant in August 2026, while 796 were relevant and 4 were irrelevant in September 2026. The public metrics use the qualified observation counts of 684 for August 2026 and 695 for September 2026 that survive both qualification stages.

Key Findings

Benchmark Context

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

The benchmark-level metrics below summarize the qualified observation counts and recommendation shares that anchor every brand comparison in this report.

Benchmark-Level Metrics

AI Recommendation Trend

The Top Two Brands Tightened While Two Mid-Tier Brands Lost Ground

The category remains a two-brand story at the top, with Optimum Nutrition and Transparent Labs separated by just 1.9 points in September 2026. Below them, Dymatize holds a clear third position, while the remaining seven brands cluster at coverage levels below 24%. The significant declines for Kaged and MuscleTech are the only movements that exceed normal month-to-month variation, suggesting the category's overall structure is stable even as individual brands shift.

The month's movement was driven by losses rather than gains. No brand was classified as a significant riser this month, meaning the category-level change came exclusively from the significant declines for Kaged and MuscleTech, partially offset by Transparent Labs' rise.

What Changed This Month

MuscleTech

MuscleTech's valid recommendation coverage fell from 11.8% in August 2026 to 8.1% in September 2026, a drop of 3.7 points. This is the brand's largest single-month movement in the series and classes it as a significant decliner.

The decline appears rooted in reduced presence rather than lower recommendation quality. MuscleTech's raw mention presence rate dropped from 15.8% to 10.9% over the same period, a 4.9-point fall. The brand was present in 76 of 695 qualified observations in September 2026, down from 108 of 684 in August 2026.

The distinction between visibility and recommendation matters here. The primary driver was the brand disappearing from more conversations entirely, not being recommended less favorably when it did appear. MuscleTech's top-three rate declined from 6.9% to 5.2%, and its rank-one rate fell from 0.7% to 0.3%, but neither movement was significant on its own.

Highest-priority diagnostic: Which prompt categories drove the loss of presence, and which competitor is now being surfaced in the conversations where MuscleTech previously appeared?

Kaged

Kaged's valid recommendation coverage dropped from 8.3% in August 2026 to 5.3% in September 2026, a decline of 3.0 points. This is the brand's largest single-month movement and also classes it as a significant decliner.

Kaged's raw mention presence fell from 9.4% to 6.3%, a 3.1-point drop. The brand was present in 44 of 695 qualified observations in September 2026, down from 64 of 684 in August 2026. Its valid recommendation count fell from 57 to 37 over the same period.

Kaged lost both presence and recommendation prominence simultaneously, a more concerning pattern than a visibility-only decline. Its top-three rate fell from 4.2% to 1.9%, a 2.3-point decline that was itself significant.

Highest-priority diagnostic: Which specific product or use-case queries drove Kaged's loss of top-three placement, and which brands are capturing those recommendation slots?

Transparent Labs

Transparent Labs was the strongest upward mover, with valid recommendation coverage rising from 78.1% in August 2026 to 81.4% in September 2026, a gain of 3.3 points. While this gain was not classified as a significant movement, it materially narrowed the gap to the category leader.

The brand's raw mention presence rose from 81.7% to 84.9%, and its top-three recommendation rate climbed from 62.9% to 67.3%. Transparent Labs was present in 590 of 695 qualified observations in September 2026, up from 559 of 684 in August 2026. Its rank-one recommendation count rose from 223 to 237.

The movement for Transparent Labs is broad-based, spanning presence, top-three placement, and rank-one wins. The brand's average recommended rank improved from 2.06 to 2.00, placing it ahead of Dymatize and closing in on Optimum Nutrition's 1.80 average rank.

Highest-priority diagnostic: Which prompt categories drove Transparent Labs' expanded presence, and does this growth represent new query territory or deeper penetration of existing categories?

Category-Level Gap Widening

Three notable gaps widened between August 2026 and September 2026, each involving Transparent Labs pulling away from a mid-tier brand. The gap between MuscleTech and Transparent Labs widened from 66.3 points to 73.3 points; the gap between Kaged and Transparent Labs widened from 69.8 points to 76.1 points; and the gap between BSN and Transparent Labs widened from 69.9 points to 75.5 points.

These widening gaps reflect the combined effect of Transparent Labs' rise and the declines of MuscleTech, Kaged, and BSN. For brands in the mid-tier, the competitive distance to the top two is growing, which may have implications for how AI systems frame their recommendations.

Buyer-Intent Interpretation

All 695 qualified observations in September 2026 fell into the Brand Recommendation cluster, with zero observations in the Pricing & Value or Multi-Brand Comparison clusters. The same pattern held in August 2026, where all 684 qualified observations were classified as Brand Recommendation. While the response-type distribution shows some comparison analysis (41 prompts) and pricing analysis (12 prompts) in September 2026, these did not qualify for the cluster-level analysis in the public benchmark.

The implication is that the public benchmark can currently speak to which brands AI systems recommend, but it cannot yet answer commercial questions about how AI frames pricing or value, nor how brands fare in direct head-to-head comparison queries. These buyer-intent classes represent a gap in the current public evidence base.

Brand Opportunity Summary

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 LLM Authority Index AI Market Discovery research program.

• AI Industry Market Discovery Methodology
• AI Industry Market Discovery Metrics
• AI Industry Market Discovery Standards

Report-Specific Interpretation Notes

• Movement between months is directional analysis that identifies changes worth investigating; it does not by itself establish the cause of those changes.
• Small absolute counts for brands like GNC (16 valid recommendations in September 2026) and Muscle Milk (20) mean percentage movements can appear larger than their commercial weight.
• The qualified benchmark set of 695 observations in September 2026 is the public denominator for all brand-level metrics, not the raw collection of 800 prompts.
• A brand's raw mention presence and its valid recommendation coverage are distinct signals; a brand can be widely present but rarely recommended, as GNC demonstrates.

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 only reveal the surface of AI recommendation behavior. Beneath them sit questions with direct commercial consequences: which high-intent prompts is a brand winning or losing, which competitor takes the recommendation when a brand loses, what attributes AI systems associate with each option, and which external sources shape those answers in ways that differ across ChatGPT, Copilot, Gemini, Perplexity, AI Overviews, and AI Mode.

A company-specific AI visibility audit maps those prompt, surface, competitor, ranking, sentiment, and evidence-source patterns into a prioritized visibility strategy. Rather than reacting to monthly percentage movements, an audit identifies the specific queries and surfaces where intervention will have the greatest impact.

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