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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 933 sourced gains and 770 sourced problems, averaging 68 and 66 on the index score. Readers have logged 22 public signals on the Pulse, which is kept apart and never counted as evidence.

933 gains
770 problems
Every sourced claim in the Index, one square each, shaded by the strength of its evidence. High Moderate Emerging

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

1,703 results
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AI gains · 933

185
LifestyleStableModerate evidence · 1 source

Machine learning, led by neural networks, provides advanced quality control, safety monitoring, and process optimization across food industry domains including defect detection and predictive QC.

On 2025-10-04, a peer-reviewed review in Foods synthesized 25 studies selected from 124 Scopus records from 2005-2025 to map machine learning use for quality control in food production. It organized findings into six domains covering quality applications, defect detection and visual inspection, ingredient optimization, packaging sensors and predictive QC, supply chain traceability, and Industry 4.0 models.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
84

Updated Jul 22, 2026 · TRV-2026-0481

72Index score
186
LaborStableModerate evidence · 1 source

Frontier models are approaching industry experts in deliverable quality on real-world economically valuable tasks and can perform them cheaper and faster than unaided experts when paired with human oversight.

Researchers introduced GDPval, a benchmark of real-world economically valuable tasks spanning 44 occupations and the top 9 U.S. GDP sectors, built from work of experienced industry professionals. As of January 2026, they reported frontier models improving linearly and approaching expert deliverable quality, with potential to complete tasks cheaper and faster than unaided experts when paired with human oversight.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
84

Updated Jul 22, 2026 · TRV-2026-0471

72Index score
187
ScienceStableModerate evidence · 1 source

LLM-based autonomous agents leveraging vast Web knowledge show potential for human-level intelligence and enable applications across social science, natural science, and engineering.

Published March 22, 2024, this peer-reviewed survey examines the shift from agents trained with limited knowledge in isolated environments to agents built on large language models trained on vast Web knowledge. The authors propose a unified construction framework and systematically review applications and evaluation methods.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0461

72Index score
188
HealthStableModerate evidence · 1 source

AI systems deployed in hospitals and clinics have improved clinical decision-making, hospital operations, medical image analysis, and patient monitoring via wearables.

This peer-reviewed review from March 2024 surveys how artificial intelligence is being integrated across hospitals and clinics, covering clinical decision support, operational management, medical image analysis, and patient monitoring with AI-powered wearables, drawing on case studies of domain-specific transformation.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0454

72Index score

AI problems · 770

185
HealthStableModerate evidence · 1 source

Adolescents with higher parental attachment anxiety experienced greater rumination and depression, which were associated with increased authentic self-disclosure to generative AI, with stronger rumination-to-depression links among girls.

A peer-reviewed study published September 23, 2026 surveyed 685 Chinese adolescents averaging 13.17 years old to examine how parental attachment anxiety relates to authentic self-disclosure to generative AI. It found father and mother attachment anxiety were positively associated with rumination, which was linked to depression, and both rumination and depression were positively associated with disclosure to GenAI through sequential mediation.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
97

Updated Sep 27, 2026 · TRV-2026-1199

68Index score
186
EducationStableModerate evidence · 1 source

AI-generated study guide drafts require careful instructor review and revision for scientific accuracy, course alignment, clarity, cognitive level, and usability before student use.

Published September 23, 2026 in Journal of Microbiology & Biology Education, this Tips and Tools article outlines how an instructor can use generative AI to draft fillable study guides for introductory biology. The author provides lecture content, assignments, and example worksheets as context for an initial prompt, then iteratively refines the AI draft for accuracy and alignment.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
97

Updated Sep 27, 2026 · TRV-2026-1198

68Index score
187
ScienceStableModerate evidence · 1 source

Learning the output distributions of brickwork random quantum circuits is average-case hard in the statistical query model, requiring super polynomially many queries at super logarithmic depth to achieve constant success probability.

By the publication date of 2025-10-13, the authors had proven that learning the output distributions of brickwork random quantum circuits on n qubits is average-case hard in the statistical query model, establishing super polynomial and exponential query lower bounds that grow with circuit depth d.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
97

Updated Sep 26, 2026 · TRV-2026-1193

68Index score
188
EducationStableModerate evidence · 1 source

Widespread adoption and legitimization of LLMs in higher education research and education rewrites epistemic governance to favor Big EdTech, turning epistemic agents into epistemic consumers and undermining academic freedom and holistic knowledge needed for democratic deliberation.

This peer-reviewed theory paper from October 2025 asks why and how large language models transform epistemic agency and epistemic governance in higher education. It argues LLMs function as an epistemic technology that changes who produces knowledge and how research and education operate, driven by Big EdTech and subsequent institutional adoption that rewrites governance rules in favor of industry actors.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
97

Updated Sep 26, 2026 · TRV-2026-1191

68Index score

Recomputed live from the record · Oct 11, 2026, 11:40 PM