TruaceTracing the truth around AIWednesday, August 26, 2026
The Index

What the evidence says.What the public feels.

Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.

1,155 results
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AI gains · 641

78
GainHealth· Stable· Evidence: Moderate (1 source)

AI with multi-omics and systems-level frameworks was used to support target identification, microbial network reconstruction, biomarker discovery, and therapeutic prioritization for anti-virulence strategies against Salmonella Typhi.

This critical review from July 30 2026 examines quorum-sensing, particularly LuxS-mediated AI-2 signaling, in Salmonella Typhi as a regulator of virulence, biofilm formation, and persistence amid rising multidrug-resistant and extensively drug-resistant typhoid. It surveys microbiome interactions and a range of anti-QS strategies and evaluates the role of AI, multi-omics integration, and systems-level frameworks in target identification and therapeutic prioritization.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%95

Updated Jul 31, 2026 · TRV-2026-0598

78
GainClimate· Stable· Evidence: Moderate (1 source)

Gradient-boosted machine learning predicted carbapenem MICs from resistance gene profiles with strong performance for imipenem and ertapenem, supporting AMR surveillance.

A One-Health study analyzed 30,554 E. coli whole-genome sequences from human, animal and environmental sources across 126 countries from 2000 to 2025, using AMRFinderPlus and MLST to map resistance genes and clones, and applied gradient-boosted machine learning to predict MICs from gene profiles and chromosomal features.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%94

Updated Jul 29, 2026 · TRV-2026-0584

78
GainEducation· Stable· Evidence: Moderate (1 source)

Universities are developing ethical-use guidelines, authentic assessments, and training programs that enhance teaching and learning and foster GAI literacy.

Published December 19, 2024, this peer-reviewed study analyzed generative AI adoption policies and guidelines from 40 universities across six global regions through the lens of Diffusion of Innovations Theory. It examined how institutions frame compatibility, trialability, observability, communication channels, and roles and responsibilities.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%93

Updated Jul 24, 2026 · TRV-2026-0549

78
GainClimate· Stable· Evidence: Moderate (1 source)

Aurora foundation model outperforms operational forecasts for air quality, ocean waves, tropical cyclone tracks and high-resolution weather while running at orders of magnitude lower computational cost.

On May 21, 2025, a Nature peer-reviewed paper introduced Aurora, a large-scale foundation model trained on more than one million hours of diverse geophysical data. The authors report it outperforms operational forecasts for air quality, ocean wave dynamics, tropical cyclone tracks and high-resolution weather at orders of magnitude lower computational cost.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%93

Updated Jul 24, 2026 · TRV-2026-0528

AI problems · 514

78
ProblemPolicy· Stable· Evidence: Moderate (1 source)

Organizations attempting to embed generative AI into strategic decision-making struggle to move beyond pilots due to hallucination risks, prompt-engineering deficiencies, data readiness gaps, and privacy or IP concerns.

Researchers conducted a qualitative single-case study of a global multi-brand group to examine how generative AI moves from pilots to routinised inputs in strategic decision-making. Based on 27 executive interviews and document triangulation, they identified enablers and barriers and proposed a four-stage pathway from Awareness and Exploration to Institutionalisation and Transformation with quality, provenance, explainability and accountability gates.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%93

Updated Jul 20, 2026 · TRV-2026-0400

78
ProblemBusiness· Stable· Evidence: Moderate (1 source)

Smart sensor deployment in precision farming is limited by calibration issues, data privacy concerns, interoperability gaps and adoption barriers.

A January 2026 review in Sensors examined smart sensor technologies in precision farming, describing how integration with IoT and AI has changed how agricultural data is collected, analyzed and utilized to optimize yield and conserve resources.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%93

Updated Jul 20, 2026 · TRV-2026-0399

78
ProblemPolicy· Stable· Evidence: Moderate (1 source)

AI-powered consumer contracts introduce risks of bias, opacity, privacy violations, and power asymmetries due to limited human intervention.

The source describes how artificial intelligence is used to automate the drafting, personalization, and enforcement of consumer contracts at scale with limited human intervention, and presents a comparative legal analysis of how jurisdictions including the EU, US, Canada, Brazil, and Asia-Pacific are responding.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%93

Updated Jul 20, 2026 · TRV-2026-0395

78
ProblemLabor· Stable· Evidence: Moderate (1 source)

Applying GnAI to literary fiction editing raises unresolved ethical and industrial concerns about intellectual property, trust, the author-editor relationship, and whether core professional editing principles translate to AI.

In a July 2024 peer-reviewed paper, researchers examined generative AI in book publishing by using a published story as a test case to compare edits made by GnAI with edits made by professional editors over multiple drafts and at different stages of editorial development. The work focuses on literary fiction editing within trade publishing.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%93

Updated Jul 20, 2026 · TRV-2026-0389

Recomputed live from the record · Aug 27, 2026, 12:42 AM