AI SEO Statistics: Technology (2026-07 edition)

Reading the 120 answers model by model shows how differently the three assistants treat the same technology questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 40% (ChatGPT) down to 20% (Gemini), a 20-point gap on an identical question set.

Across the 40 technology answers it produced, ChatGPT recommended hiring a professional in 40% of them and suggested a DIY approach first 30% of the time. It named a specific provider in 27.5% of answers (about 1.7 distinct providers per answer) and included price or cost information 12.5% of the time. ChatGPT asked a clarifying question before answering in 37.5% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 25%, averaging 745 words per answer. On the remaining cues it told the buyer to check reviews in 7.5%, pointed to case studies or a portfolio in 17.5%, and framed the choice around local proximity in 5%; a selection-criteria checklist appeared in 45% of its answers and a recommendation to gather multiple quotes in 7.5%.

Across the 40 technology answers it produced, Claude recommended hiring a professional in 25% of them and suggested a DIY approach first 25% of the time. It named a specific provider in 47.5% of answers (about 1.8 distinct providers per answer) and included price or cost information 17.5% of the time. Claude asked a clarifying question before answering in 50% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 12.5%, averaging 343 words per answer. On the remaining cues it told the buyer to check reviews in 12.5%, pointed to case studies or a portfolio in 10%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 55% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 technology answers it produced, Gemini recommended hiring a professional in 20% of them and suggested a DIY approach first 17.5% of the time. It named a specific provider in 40% of answers (about 1.6 distinct providers per answer) and included price or cost information 22.5% of the time. Gemini asked a clarifying question before answering in 2.5% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 2.5%, averaging 235 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 47.5% of its answers and a recommendation to gather multiple quotes in 2.5%.

Taken together, ChatGPT is the assistant most likely to route a technology buyer to a professional (40%) and Gemini the least (20%). ChatGPT produced the longest answers, at 745 words on average. Specific providers were named most often by Claude (47.5%) — 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 technology, where all three landed within a few points of each other:

• Mentions local proximity: 0%–5% across all three (a 5-point spread).
• Warns about red flags or scams: 12.5%–17.5% across all three (a 5-point spread).
• Recommends multiple quotes: 2.5%–7.5% across all three (a 5-point spread).
• Gives price or cost information: 12.5%–22.5% across all three (a 10-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions local proximity" (identical coding in 95% of questions) and least consistently on "asks a clarifying question" (37.5%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the technology buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 13.3%. Warning about red flags or scams appeared in 15%.

On structuring the decision, a selection-criteria checklist showed up in 49.2% of answers on average and a recommendation to gather multiple quotes in 5%. The single least-reproduced protective signal for technology is "recommends multiple quotes" at 5% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 40 questions behind every percentage on this page were drawn from real technology / SaaS (software, IT services, B2B startups) buyer journeys, expanded from 4 seed prompts. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact technology 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.