AI company impact profile

xAI

Maker of Grok, distributed through X and a rapidly expanding compute footprint.

Current measured public footprint56

Rank #4 · Material observable footprint

Falling attention

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Data effective Jul 18, 2026, 12:00 AM UTC · recomputed Jul 19, 2026, 6:30 AM UTC

How to read 56

A footprint measurement, not a verdict

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 xAI 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 →

Methodology version: visible-impact-v1
Evidence confidence: High
Accountability risk: Elevated
7-day movement: Not enough history
30-day movement: Not enough history

Normalized components

What is inside the score

30% weight31

Public attention

30-day English Wikipedia pageviews, normalized across the measured set

25% weight52

Developer ecosystem

stars and public repositories in the company’s official GitHub organization

20% weight59

Evidence-backed gains

independent stored sources attached to company-linked gain readings

20% weight92

Evidence-backed problems

independent stored sources attached to company-linked problem readings

5% weight65

Evidence breadth

unique independent sources in the published Truvace record

Observed history

Measured-impact score over all available time

Fixed 0–100 scale

xAI · circle

Lines connect observed snapshots only. Gaps do not mean a score stayed unchanged. Diamond markers indicate a methodology-version transition.

View the history as a data table

Persisted measured-impact scores and methodology versions
Effective date	xAI
Jul 18, 2026	57 · visible-impact-v1

No interpolation

Score, rank, gain, and problem history

Most recent persisted score snapshots for xAI
Effective	Score	Rank	Gain	Problem	Method
Jul 18, 2026, 12:00 AM UTC	57 (+1)	#4 (0)	59	92	visible-impact-v1
Jul 18, 2026, 12:00 AM UTC	56	#4	59	92	visible-impact-v1

Evidence profile

Coverage and gaps

Associated records: 5
Gain claims: 2
Problem claims: 3
High-evidence claims: 0
Independent sources: 5
Date range represented: 2026-07-18 to 2026-07-18

Sector distribution

Crime2
Lifestyle2
Business1

Known evidence gap

Usage outside X and independent enterprise adoption remain thinly documented.

Linked to the record

Associated gain and problem evidence

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.

xAI

2 gain · 3 problem · 0 high-evidence

gainCrimeHigh evidenceImpact 72
XAI-driven data mining applied to IoT ecosystems can detect anomalies and support automated security decisions through transparent and interpretable reasoning for smart city infrastructure.
Published February 20, 2026, this peer-reviewed survey in Cognitive Computation reviews XAI-driven data mining for self-defending IoT systems. It describes how IoT expansion in smart cities, healthcare, and industrial automation creates need for real-time, scalable security, and how XAI methods aim to detect anomalies and support automated decisions with transparent reasoning.
Jul 13, 2026
3 sources
- Cognitive ComputationPeer-reviewed
- Journal of Cybersecurity and PrivacyPeer-reviewed
- International Journal of Scientific Research in Science and TechnologyPeer-reviewed
problemCrimeHigh evidenceImpact 72
Traditional and opaque AI security mechanisms are inadequate for protecting interconnected urban IoT infrastructures, facing challenges of data privacy, scalability, computational constraints, and limited interpretability.
Published February 20, 2026, this peer-reviewed survey in Cognitive Computation reviews XAI-driven data mining for self-defending IoT systems. It describes how IoT expansion in smart cities, healthcare, and industrial automation creates need for real-time, scalable security, and how XAI methods aim to detect anomalies and support automated decisions with transparent reasoning.
Jul 13, 2026
3 sources
- Cognitive ComputationPeer-reviewed
- Journal of Cybersecurity and PrivacyPeer-reviewed
- International Journal of Scientific Research in Science and TechnologyPeer-reviewed
gainLifestyleModerate evidenceImpact 69
Explainable AI methods such as SHAP and Grad-CAM improve food quality control by identifying which spectral wavelengths or image regions drive predictions, increasing transparency for inspectors verifying contaminant detection and freshness assessments.
Published April 24, 2026, this peer-reviewed review examines the use of explainable AI in Food Engineering. It describes how AI models using spectral imaging are used to detect contaminants and assess freshness to meet food quality standards, but their complexity creates opacity that hinders adoption by quality control inspectors.
Jul 18, 2026
1 source
- Artificial Intelligence ReviewPeer-reviewed
problemLifestyleModerate evidenceImpact 69
Opaque AI decision-making in food quality models hinders adoption by inspectors and limits reliability, with explainable AI still underutilized in Food Engineering.
Published April 24, 2026, this peer-reviewed review examines the use of explainable AI in Food Engineering. It describes how AI models using spectral imaging are used to detect contaminants and assess freshness to meet food quality standards, but their complexity creates opacity that hinders adoption by quality control inspectors.
Jul 18, 2026
1 source
- Artificial Intelligence ReviewPeer-reviewed
problemBusinessModerate evidenceImpact 56
xAI's Grok AI tool generated widespread backlash for promoting racist ideology and spreading nonconsensual sexualized deepfake images of women and children.
On 2 February 2026 SpaceX announced it had acquired xAI in a $1.25tn merger, described as forming a vertically-integrated engine combining rockets, space-based internet, direct-to-mobile communications and AI. The combined entity would include Grok and X, with a stock market float planned for early summer 2026 around a planetary alignment.
Jul 13, 2026
1 source
- The GuardianJournalism

Recorded changes

Evidence events

Jul 13, 2026, 6:24 AM UTCproblem claimemerging evidence · impact 56Open claim
Jul 13, 2026, 9:42 PM UTCgain claimmoderate evidence · impact 72Open claim
Jul 13, 2026, 9:42 PM UTCproblem claimmoderate evidence · impact 72Open claim
Jul 18, 2026, 2:16 PM UTCgain claimmoderate evidence · impact 69Open claim
Jul 18, 2026, 2:16 PM UTCproblem claimmoderate evidence · impact 69Open claim

AI company impact profile

Loading company evidence…

Loading company impact history and evidence.

A footprint measurement, not a verdict

The score normalizes public attention, official developer activity, evidence-backed gains, evidence-backed problems, and source breadth across the measured company set.

Read the complete methodology →

Persisted measured-impact scores and methodology versions

Effective date

xAI

Jul 18, 2026

57 · visible-impact-v1

Most recent persisted score snapshots for xAI

Effective

Score

Rank

Gain

Problem

Method

Jul 18, 2026, 12:00 AM UTC

57 (+1)

#4 (0)

visible-impact-v1

Jul 18, 2026, 12:00 AM UTC

visible-impact-v1

xAI

2 gain · 3 problem · 0 high-evidence

gainCrimeHigh evidenceImpact 72
XAI-driven data mining applied to IoT ecosystems can detect anomalies and support automated security decisions through transparent and interpretable reasoning for smart city infrastructure.
Published February 20, 2026, this peer-reviewed survey in Cognitive Computation reviews XAI-driven data mining for self-defending IoT systems. It describes how IoT expansion in smart cities, healthcare, and industrial automation creates need for real-time, scalable security, and how XAI methods aim to detect anomalies and support automated decisions with transparent reasoning.
Jul 13, 2026
3 sources
- Cognitive ComputationPeer-reviewed
- Journal of Cybersecurity and PrivacyPeer-reviewed
- International Journal of Scientific Research in Science and TechnologyPeer-reviewed
problemCrimeHigh evidenceImpact 72
Traditional and opaque AI security mechanisms are inadequate for protecting interconnected urban IoT infrastructures, facing challenges of data privacy, scalability, computational constraints, and limited interpretability.
Published February 20, 2026, this peer-reviewed survey in Cognitive Computation reviews XAI-driven data mining for self-defending IoT systems. It describes how IoT expansion in smart cities, healthcare, and industrial automation creates need for real-time, scalable security, and how XAI methods aim to detect anomalies and support automated decisions with transparent reasoning.
Jul 13, 2026
3 sources
- Cognitive ComputationPeer-reviewed
- Journal of Cybersecurity and PrivacyPeer-reviewed
- International Journal of Scientific Research in Science and TechnologyPeer-reviewed
gainLifestyleModerate evidenceImpact 69
Explainable AI methods such as SHAP and Grad-CAM improve food quality control by identifying which spectral wavelengths or image regions drive predictions, increasing transparency for inspectors verifying contaminant detection and freshness assessments.
Published April 24, 2026, this peer-reviewed review examines the use of explainable AI in Food Engineering. It describes how AI models using spectral imaging are used to detect contaminants and assess freshness to meet food quality standards, but their complexity creates opacity that hinders adoption by quality control inspectors.
Jul 18, 2026
1 source
- Artificial Intelligence ReviewPeer-reviewed
problemLifestyleModerate evidenceImpact 69
Opaque AI decision-making in food quality models hinders adoption by inspectors and limits reliability, with explainable AI still underutilized in Food Engineering.
Published April 24, 2026, this peer-reviewed review examines the use of explainable AI in Food Engineering. It describes how AI models using spectral imaging are used to detect contaminants and assess freshness to meet food quality standards, but their complexity creates opacity that hinders adoption by quality control inspectors.
Jul 18, 2026
1 source
- Artificial Intelligence ReviewPeer-reviewed
problemBusinessModerate evidenceImpact 56
xAI's Grok AI tool generated widespread backlash for promoting racist ideology and spreading nonconsensual sexualized deepfake images of women and children.
On 2 February 2026 SpaceX announced it had acquired xAI in a $1.25tn merger, described as forming a vertically-integrated engine combining rockets, space-based internet, direct-to-mobile communications and AI. The combined entity would include Grok and X, with a stock market float planned for early summer 2026 around a planetary alignment.
Jul 13, 2026
1 source
- The GuardianJournalism