AI SEO Statistics: Igaming (2026-07 edition)

Reading the 45 answers model by model shows how differently the three assistants treat the same igaming questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 46.7% (ChatGPT) down to 20% (Gemini), a 27-point gap on an identical question set.

Across the 15 igaming answers it produced, ChatGPT recommended hiring a professional in 46.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 26.7% of answers (about 0.9 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 33.3% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 33.3%, averaging 847 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 igaming answers it produced, Claude recommended hiring a professional in 40% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 53.3% of answers (about 2.6 distinct providers per answer) and included price or cost information 13.3% of the time. Claude asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 13.3%, averaging 334 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 igaming answers it produced, Gemini recommended hiring a professional in 20% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 33.3% of answers (about 1 distinct providers per answer) and included price or cost information 13.3% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 6.7%, averaging 203 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route an igaming buyer to a professional (46.7%) and Gemini the least (20%). ChatGPT produced the longest answers, at 847 words on average. Specific providers were named most often by Claude (53.3%) — even there, roughly one answer in 2 carried a name.

On other behaviors the three models move almost in lockstep — the points of near-consensus for igaming, where all three landed within a few points of each other:

• Gives price or cost information: 13.3% across all three models.
• Tells the buyer to check reviews: 0% across all three models.
• Mentions case studies or portfolio: 0% across all three models.
• Mentions local proximity: 0%–6.7% across all three (a 7-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "tells the buyer to check reviews" (identical coding in 100% of questions) and least consistently on "gives selection criteria" (33.3%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the igaming buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 0% of answers on average. Verifying credentials or certifications appeared in 17.8%. Warning about red flags or scams appeared in 11.1%.

On structuring the decision, a selection-criteria checklist showed up in 37.8% of answers on average and a recommendation to gather multiple quotes in 6.7%. The single least-reproduced protective signal for igaming is "tells the buyer to check reviews" at 0% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 15 questions behind every percentage on this page were drawn from real igaming (technology / SaaS; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact igaming question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.