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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 932 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.

932 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.

1,702 results
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AI gains · 932

769
HealthStableModerate evidence · 1 source

Agentic AI systems demonstrated autonomous, goal-directed behavior with high accuracy in cancer diagnosis, treatment planning, alert generation, coaching, and workflow optimization across emergency medicine, oncology, radiology, and rehabilitation pilots.

A March 2026 scoping review in npj Digital Medicine examined agentic AI in healthcare, defined as systems capable of operating autonomously to achieve defined clinical goals. Across five databases, seven studies met criteria, spanning emergency medicine, oncology, radiology, and rehabilitation, with features including autonomous operation, goal-directed behavior, action initiation, and multi-agent collaboration.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0166

67Index score
770
BusinessStableModerate evidence · 1 source

Provincial panel analysis for 2020-2023 found digitalization significantly increased labor productivity for micro, small and medium enterprises in Indonesia.

A peer-reviewed study of Indonesian provinces from 2020 to 2023 examined how digitalization and AI adoption relate to MSME labor productivity, using fixed-effects panel estimation and machine learning methods and framing results with Ibn Khaldun's institutional ideas.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0165

67Index score
771
ScienceStableModerate evidence · 1 source

The AI Scientist pipeline autonomously executed the entire research lifecycle and produced a manuscript that cleared initial peer review at a selective machine learning workshop.

Researchers built The AI Scientist, an agentic system using foundation models to automate conception, coding, experimentation, data analysis, manuscript writing, and peer review. By March 2026 they reported that a manuscript fully generated by the system passed first-round review for a workshop at a top-tier machine learning conference.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0163

67Index score
772
EducationStableModerate evidence · 1 source

Students reported significantly lower anxiety during AIvaluate-mediated performance-based assessments compared to traditional face-to-face assessments, with usability rated in the good range.

Researchers compared traditional face-to-face performance-based assessments with sessions mediated by AIvaluate, an LLM-augmented emotionally intelligent conversational agent, using 35 pre-university students in a counterbalanced within-subjects design measuring emotional state, usability and qualitative perceptions.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0162

67Index score
773
EducationStableModerate evidence · 1 source

AI use was associated with strong perceived gains in educational support through enhanced and personalized learning environments, and in emotional and mental well-being.

On 2026-06-04 a peer-reviewed study reported results from 150 participants surveyed about AI's influence on behavior and well-being. Using a Likert-scale questionnaire analyzed in SPSS version 25, the authors found educational support had the highest mean at 4.89 and emotional and mental well-being had the highest mean difference at 4.9, with all T-tests significant at p < 0.000.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0161

67Index score
774
LaborStableModerate evidence · 1 source

Passive AI use initially boosted enjoyment and satisfaction, while active collaboration where workers drafted first then used AI to refine preserved psychological connection comparable to independent work.

Researchers ran a pre-registered lab experiment with 269 participants doing occupation-specific writing under no AI, passive AI copying, or active drafting-then-refining, plus a 270-person real-world survey. Passive copying reduced self-efficacy, ownership, and meaningfulness, with efficacy and meaningfulness losses persisting after returning to manual work, while active collaboration preserved connection similar to working alone.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0160

67Index score

AI problems · 770

Recomputed live from the record · Oct 11, 2026, 9:37 AM