Life Insurance Consensus Index studies how major AI platforms recommend life insurance companies when given the same consumer scenario. Comparing their responses helps us identify agreement, disagreement, and recurring carrier visibility across distinct recommendation environments.
Our standard platform set
Our standard research framework includes seven platforms. Each receives the same core scenario and contributes equally to basic recommendation coverage:
- ChatGPT — OpenAI
- Gemini — Google
- Claude — Anthropic
- Perplexity
- Grok — xAI
- DeepSeek
- Kimi — Moonshot AI
These systems can differ in how they interpret a question, retrieve information, cite sources, and order recommendations. We study those outputs as research data. Inclusion does not mean we regard a platform as a licensed insurance professional or consider every supporting claim reliable.
One platform receives one vote
Each included platform has equal voting weight regardless of user base, market share, answer length, confidence, or citation count. If six of seven usable responses recommend a carrier, its recommendation coverage is 85.7%.
Running several models from the same provider does not normally create additional votes in a standard study. Model-level comparisons, including comparisons of reasoning modes or web access, may be conducted as separately identified research. They should not silently change the standard platform-level calculation.
Platforms and research metadata
The platform is our primary public research unit because its underlying models, modes, search features, and interface can change. Where practical, the research record retains the model, mode, interface, web or search status, timestamp, raw response, and citations associated with each run.
These details help explain the environment in which a result was produced. A study remains a dated snapshot of that environment rather than a permanent claim about what a platform will always recommend. Research, publication, verification, editorial review, and page-update dates describe different events.
Consistent questions and controlled sessions
Each platform should receive substantially the same applicant profile, coverage need, and constraints. A scenario might specify a healthy nonsmoking 40-year-old seeking $1 million of 20-year term life insurance who prefers to avoid a traditional medical exam.
For standard open recommendation research, we generally avoid supplying a preferred carrier list. We want to observe which companies the platform identifies for the scenario. Where practical, fresh or controlled sessions reduce the influence of earlier carrier discussions, unrelated prompts, personalization, and stated brand preferences.
Usable responses determine coverage
A scheduled platform may fail technically, become inaccessible, refuse a request, or produce incomplete or irrelevant output. We distinguish those cases from a usable answer that simply does not recommend a particular carrier.
If seven platforms are scheduled, six produce usable responses, and five recommend a carrier, its coverage is 5 of 6, or 83.3%. The actual usable response count should be disclosed. A technical failure is not automatically treated as a negative vote for every insurer.
Recommendations, qualifications, and citations
A company name appearing in a response does not necessarily earn recommendation credit. We distinguish meaningful recommendations from examples, historical references, comparisons, and criticism. Conditional recommendations can count, with the applicant or product qualification preserved.
Where a platform supplies an ordered list, we record placement where practical. Recommendation coverage remains the primary signal, while position adds context. We also preserve citations when available because they can show which sources influenced the answer and help investigate recurring claims.
Citations require their own scrutiny. A linked page does not establish that the platform interpreted it correctly or that its information is current. Material claims may be separately checked against carrier documentation, policy information, regulatory sources, rating organizations, and other appropriate evidence.
Shared sources and visibility limits
Separate platforms can rely on overlapping websites, publishers, regulatory information, and training material. Seven matching recommendations therefore do not establish seven independent investigations of an insurer. They show agreement among the platform responses we collected.
Large or familiar insurers may appear more often because more information about them is available online. Newer products, specialized carriers, and less visible insurers may be underrepresented. Model updates, retrieval changes, randomness, location, and platform configuration can also affect results even when the prompt stays the same.
Verification and editorial review
Life insurance recommendations depend heavily on applicant details, and even unanimous platform agreement cannot determine a person's approval, premium, underwriting class, or exam requirements. We supplement the recommendation record with factual verification and experienced editorial review where appropriate.
Chris Huntley's editorial role is to help explain product and underwriting context. He does not act as an eighth platform or change the recorded votes. A finding can retain its original recommendation count while the published analysis explains a factual error or a practical limitation in the supporting reasoning.
Changes to the platform set
The platform set may evolve with consumer relevance, availability, accessibility, technical capability, and the wider AI market. Significant changes should be disclosed and versioned, and material deviations within a research cycle should be recorded rather than hidden by a silent substitution.
Older studies retain the platforms and usable-response denominator from their own research cycle. Preserving platform-level responses allows us to examine whether carriers gain or lose visibility, whether consensus changes, and whether particular systems consistently differ from the others.
Life Insurance Consensus Index is owned and operated by 3D Chess Media, LLC, a Nevada limited liability company. Its platform analysis is provided for research and educational purposes.