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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

745
ScienceStableModerate evidence · 1 source

Data-driven machine learning models can rapidly generate biomolecular structures and propose conformational ensembles for recognition events with high predictive performance.

Published July 17, 2026, this perspective argues that quantitative prediction of biomolecular recognition requires moving beyond static structures to ensemble-based thermodynamic and kinetic observables. It reviews physics-based sampling under approximate Hamiltonians and modern machine learning models that learn from structural and bioactivity data.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 17, 2026 · TRV-2026-0244

67Index score
746
ScienceStableModerate evidence · 1 source

MLFFs provide high accuracy with high efficiency for atomic-level studies of inorganic crystalline materials, overcoming traditional limits in structure prediction, properties, defects, and phase transitions.

On 2026-07-17, a review in Physical Chemistry Chemical Physics summarized machine learning force fields for inorganic crystalline materials, describing how they combine first-principles accuracy with classical force-field efficiency to enable atomic-level studies across structural prediction, physical properties, defects and interfaces, and phase transitions.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 17, 2026 · TRV-2026-0243

67Index score
747
HealthStableModerate evidence · 1 source

Deep learning CT segmentation using Comp2Comp enabled compartment-specific measurement of visceral adipose tissue, subcutaneous fat, and muscle, revealing sustained visceral fat reduction after metabolic and bariatric surgery that BMI alone does not capture.

Researchers retrospectively analyzed prospectively collected abdominal CTs using Comp2Comp, a validated deep learning pipeline that automatically segments visceral adipose tissue, subcutaneous adipose tissue, and skeletal muscle. They studied 435 adults with BMI >=25 for baseline BMI-VAT relationships and 39 metabolic and bariatric surgery patients with 151 scans followed up to 89 months to track compartment changes.

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

Updated Jul 17, 2026 · TRV-2026-0236

67Index score
748
HealthStableModerate evidence · 1 source

First-year medical students showed a modest net improvement in accuracy after reviewing ChatGPT-generated answers, because incorrect-to-correct changes exceeded correct-to-incorrect changes.

In a July 2026 peer-reviewed study, 57 first-year medical students completed 24 paired clinical and foundational questions during a pediatric nephrology and urology case-based session, answering individually, then viewing a ChatGPT-generated answer that was deliberately correct or incorrect, and re-answering.

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

Updated Jul 17, 2026 · TRV-2026-0235

67Index score

AI problems · 770

745
EducationStableModerate evidence · 1 source

Lecturers using artificial intelligence to create teaching materials risks degrading teaching quality and prompting student backlash.

A Guardian letters page publishes a response to a 20 November story about students fighting back over a course taught by AI. The writer, a recent doctoral graduate on fixed or zero-hours contracts, says each taught hour may take days of unpaid preparation and describes creating work plans, assessments, reading lists and tutorial tasks for three modules, while disagreeing with lecturers' decision to use artificial intelligence to create teaching materials but understanding the incentives.

Impact 30%
49
Evidence 25%
62
Scale 20%
35
Confidence 15%
62
Recency 10%
83

Updated Jul 12, 2026 · TRV-2026-0097

55Index score
746
BusinessStableModerate evidence · 1 source

Americans risk having pension plans and investment portfolios tied to a frenzied, risky multibillion-dollar race to build AI that automates cognitive tasks

The article reports that despite broad public worry about AI, upcoming large IPOs including SpaceX will push AI exposure into ordinary Americans' pension plans and investment portfolios, linking household financial futures to tech firms whose current revenue comes from internet access but whose capital needs are driven by AI ambitions like orbital datacenters.

Impact 30%
49
Evidence 25%
62
Scale 20%
35
Confidence 15%
62
Recency 10%
83

Updated Jul 12, 2026 · TRV-2026-0094

55Index score
747
BusinessStableModerate evidence · 1 source

Near 800,000 Accenture employees face being exited if they do not adopt AI tools at work amid the shift to AI-led services.

Accenture has begun calling its near 800,000 employees reinventors as part of a push to be seen as a leader in artificial intelligence. The term came from a June reorganisation that combined strategy, consulting, creative, technology and operations into Reinvention Services, and chief executive Julie Sweet has started using the label internally while the firm pushes broader adoption.

Impact 30%
49
Evidence 25%
62
Scale 20%
35
Confidence 15%
62
Recency 10%
83

Updated Jul 12, 2026 · TRV-2026-0093

55Index score
748
EducationStableModerate evidence · 1 source

A trainee English teacher experienced increased uncertainty and anxiety about how to respond to artificial intelligence in teaching

Two years ago at age 39, after 15 years as a freelance writer and novelist, the author began training to teach English with the aim of helping young people become stronger readers, writers and thinkers. As training progressed, a persistent question about what to do about artificial intelligence created growing uncertainty.

Impact 30%
49
Evidence 25%
62
Scale 20%
35
Confidence 15%
62
Recency 10%
83

Updated Jul 12, 2026 · TRV-2026-0088

55Index score

Recomputed live from the record · Oct 11, 2026, 12:26 PM