Public attention
30-day English Wikipedia pageviews, normalized across the measured set
AI company impact profile
Loading company impact history and evidence.

AI company impact profile
Maker of ChatGPT and the GPT model line, with a large consumer and developer surface.
Rank #1 · Dominant observable footprint
Rising attention
Data effective Sep 12, 2026, 12:00 AM UTC · recomputed Sep 13, 2026, 2:18 PM UTC
How to read 89
The score normalizes public attention, official developer activity, evidence-backed gains, evidence-backed problems, and source breadth across the measured company set.
It does not mean: that OpenAI is the “best,” safest, most valuable, or most responsible company. Documented problems contribute to visible footprint rather than being hidden as a negative subtraction.
Read the complete methodology →Normalized components
30-day English Wikipedia pageviews, normalized across the measured set
stars and public repositories in the company’s official GitHub organization
independent stored sources attached to company-linked gain readings
independent stored sources attached to company-linked problem readings
unique independent sources in the published Truvace record
Observed history
Fixed 0–100 scale
Lines connect observed snapshots only. Gaps do not mean a score stayed unchanged. Diamond markers indicate a methodology-version transition.
| Effective date | OpenAI |
|---|---|
| Jul 18, 2026 | 84 · visible-impact-v1 |
| Jul 19, 2026 | 86 · visible-impact-v2 · method changed |
| Jul 20, 2026 | 85 · visible-impact-v2 |
| Jul 21, 2026 | 86 · visible-impact-v2 |
| Jul 22, 2026 | 86 · visible-impact-v2 |
| Jul 23, 2026 | 87 · visible-impact-v2 |
| Jul 24, 2026 | 87 · visible-impact-v2 |
| Jul 25, 2026 | 87 · visible-impact-v2 |
| Jul 26, 2026 | 88 · visible-impact-v2 |
| Jul 27, 2026 | 89 · visible-impact-v2 |
| Jul 28, 2026 | 88 · visible-impact-v2 |
| Jul 29, 2026 | 89 · visible-impact-v2 |
| Jul 30, 2026 | 94 · visible-impact-v2 |
| Jul 31, 2026 | 89 · visible-impact-v2 |
| Aug 1, 2026 | 89 · visible-impact-v2 |
| Aug 2, 2026 | 89 · visible-impact-v2 |
| Aug 3, 2026 | 89 · visible-impact-v2 |
| Aug 4, 2026 | 89 · visible-impact-v2 |
| Aug 5, 2026 | 94 · visible-impact-v2 |
| Aug 6, 2026 | 94 · visible-impact-v2 |
| Aug 7, 2026 | 88 · visible-impact-v2 |
| Aug 8, 2026 | 89 · visible-impact-v2 |
| Aug 9, 2026 | 88 · visible-impact-v2 |
| Aug 10, 2026 | 89 · visible-impact-v2 |
| Aug 11, 2026 | 88 · visible-impact-v2 |
| Aug 12, 2026 | 88 · visible-impact-v2 |
| Aug 13, 2026 | 88 · visible-impact-v2 |
| Aug 14, 2026 | 88 · visible-impact-v2 |
| Aug 15, 2026 | 93 · visible-impact-v2 |
| Aug 16, 2026 | 96 · visible-impact-v2 |
| Aug 17, 2026 | 93 · visible-impact-v2 |
| Aug 18, 2026 | 88 · visible-impact-v2 |
| Aug 19, 2026 | 88 · visible-impact-v2 |
| Aug 20, 2026 | 88 · visible-impact-v2 |
| Aug 21, 2026 | 87 · visible-impact-v2 |
| Aug 22, 2026 | 87 · visible-impact-v2 |
| Aug 23, 2026 | 88 · visible-impact-v2 |
| Aug 24, 2026 | 61 · visible-impact-v2 |
| Aug 25, 2026 | 88 · visible-impact-v2 |
| Aug 26, 2026 | 88 · visible-impact-v2 |
| Aug 27, 2026 | 88 · visible-impact-v2 |
| Aug 28, 2026 | 88 · visible-impact-v2 |
| Aug 29, 2026 | 88 · visible-impact-v2 |
| Aug 30, 2026 | 88 · visible-impact-v2 |
| Aug 31, 2026 | 87 · visible-impact-v2 |
| Sep 1, 2026 | 88 · visible-impact-v2 |
| Sep 2, 2026 | 88 · visible-impact-v2 |
| Sep 3, 2026 | 88 · visible-impact-v2 |
| Sep 4, 2026 | 88 · visible-impact-v2 |
| Sep 5, 2026 | 88 · visible-impact-v2 |
| Sep 6, 2026 | 88 · visible-impact-v2 |
| Sep 7, 2026 | 88 · visible-impact-v2 |
| Sep 8, 2026 | 94 · visible-impact-v2 |
| Sep 9, 2026 | 89 · visible-impact-v2 |
| Sep 11, 2026 | 89 · visible-impact-v2 |
| Sep 12, 2026 | 89 · visible-impact-v2 |
No interpolation
| Effective | Score | Rank | Gain | Problem | Method |
|---|---|---|---|---|---|
| Sep 12, 2026, 12:00 AM UTC | 89 (0) | #1 (0) | 100 | 100 | visible-impact-v2 |
| Sep 11, 2026, 12:00 AM UTC | 89 (0) | #1 (0) | 100 | 100 | visible-impact-v2 |
| Sep 9, 2026, 12:00 AM UTC | 89 (-5) | #1 (0) | 100 | 100 | visible-impact-v2 |
| Sep 8, 2026, 12:00 AM UTC | 94 (+6) | #1 (0) | 100 | 100 | visible-impact-v2 |
| Sep 7, 2026, 12:00 AM UTC | 88 (0) | #1 (0) | 100 | 100 | visible-impact-v2 |
| Sep 6, 2026, 12:00 AM UTC | 88 (0) | #1 (0) | 100 | 100 | visible-impact-v2 |
| Sep 5, 2026, 12:00 AM UTC | 88 (0) | #1 (0) | 100 | 100 | visible-impact-v2 |
| Sep 4, 2026, 12:00 AM UTC | 88 (0) | #1 (0) | 100 | 100 | visible-impact-v2 |
| Sep 3, 2026, 12:00 AM UTC | 88 (0) | #1 (0) | 100 | 100 | visible-impact-v2 |
| Sep 2, 2026, 12:00 AM UTC | 88 (0) | #1 (0) | 100 | 100 | visible-impact-v2 |
| Sep 1, 2026, 12:00 AM UTC | 88 (+1) | #1 (0) | 100 | 100 | visible-impact-v2 |
| Aug 31, 2026, 12:00 AM UTC | 87 (-1) | #1 (0) | 100 | 100 | visible-impact-v2 |
Evidence profile
Private usage, enterprise revenue mix, and internal incident rates are not publicly observable.
Linked to the record
Claims are sorted by their existing Sourced Index impact score. Each card links to its Truvace record and original sources; company marketing is not treated as independent proof.
75 gain · 74 problem · 2 high-evidence
In a retrospective study published July 11 2026, investigators tested 500 chest radiographs from one tertiary center with two AI systems, M4CXR and ChatGPT-4o, having four radiologists score AI-generated reports for finding detection and RADPEER discrepancies. M4CXR reached 55.8% complete concordance versus 19.8% for GPT-4o and reduced mean reporting time to 16.3 seconds from 179.2 seconds unaided.
Jul 20, 2026
In a first large-scale empirical study published May 2026, researchers examined 1,899 open-source Model Context Protocol servers, the standard introduced by Anthropic in late 2024 to unify tool calling for Foundation Models. Using health metrics and a combined general and MCP-specific scanner, they measured adoption signals and code quality across the ecosystem.
Jul 13, 2026
By November 2023, researchers surveyed 503 Polish state university students to test an extended UTAUT2 model of ChatGPT acceptance. Using PLS-SEM, they found habit, performance expectancy, and hedonic motivation predicted behavioral intention, while behavioral intention, habit, and facilitating conditions predicted actual use behavior.
Sep 9, 2026
Published December 7, 2023, this exploratory synthesis examines ChatGPT after its November 30, 2022 public release and rapid adoption, reviewing recent literature on how the tool is being used in education. It identifies potential benefits for personalized and interactive learning and for formative assessment, while also noting drawbacks.
Aug 19, 2026
Published December 7, 2023, this exploratory synthesis examines ChatGPT after its November 30, 2022 public release and rapid adoption, reviewing recent literature on how the tool is being used in education. It identifies potential benefits for personalized and interactive learning and for formative assessment, while also noting drawbacks.
Aug 19, 2026
By February 2024, researchers analyzing over 4 million artworks from more than 50,000 users found that text-to-image generative AI adoption was linked to a 25% rise in creative productivity and a 50% rise in favorites per view, alongside shifts in novelty metrics.
Jul 31, 2026
In early 2024, researchers surveyed 23,218 higher education students in 109 countries and territories about ChatGPT. Students reported using it mainly for brainstorming, summarizing texts, and finding research articles, finding it helpful for simplifying complex information but less reliable for providing information and supporting classroom learning.
Jul 24, 2026
In a peer-reviewed study published October 27, 2024, researchers interviewed nineteen individuals about using generative AI chatbots like ChatGPT for mental health. Participants described high engagement and meaningful support, organized into themes of emotional sanctuary, insightful guidance about relationships, joy of connection, and comparisons to human therapy.
Jul 20, 2026
In a peer-reviewed study published October 27, 2024, researchers interviewed nineteen individuals about using generative AI chatbots like ChatGPT for mental health. Participants described high engagement and meaningful support, organized into themes of emotional sanctuary, insightful guidance about relationships, joy of connection, and comparisons to human therapy.
Jul 20, 2026
In a retrospective study published July 11 2026, investigators tested 500 chest radiographs from one tertiary center with two AI systems, M4CXR and ChatGPT-4o, having four radiologists score AI-generated reports for finding detection and RADPEER discrepancies. M4CXR reached 55.8% complete concordance versus 19.8% for GPT-4o and reduced mean reporting time to 16.3 seconds from 179.2 seconds unaided.
Jul 20, 2026
In a first large-scale empirical study published May 2026, researchers examined 1,899 open-source Model Context Protocol servers, the standard introduced by Anthropic in late 2024 to unify tool calling for Foundation Models. Using health metrics and a combined general and MCP-specific scanner, they measured adoption signals and code quality across the ecosystem.
Jul 13, 2026
On September 9, 2026, a peer-reviewed comparative study reported testing ChatGPT, Gemini, and Microsoft Copilot on 20 radiological cases split between congenital anomalies and tumors. Each system received the same questions and images and was scored for diagnostic accuracy and explanatory completeness, with ChatGPT scoring 19 correct, Gemini 17, and Copilot 16.
Sep 10, 2026
Recorded changes