AI Revolution Tracker

A structured knowledge base tracking AI expert predictions, deployment evidence, and industry impacts with outcome validation.

190 entries | 1,287 tracked claims | 1,062 key facts | 106 named experts | 15 claim categories

---

Why This Exists

The AI revolution is generating thousands of predictions, claims, and projections daily. Most are:

• Scattered across interviews, podcasts, keynotes, and papers with no central tracking
• Unverified — bold predictions made in 2023 are forgotten by 2025
• Context-free — "AI will transform healthcare" means nothing without specifics
• Hype-contaminated — no distinction between evidence-backed claims and marketing

This database tracks specific, attributable claims from named experts with:

• What exactly was predicted
• Who said it and when
• What category it falls into
• What timeframe was given
• What actually happened (outcome tracking)

What's Inside

1,287 Individual Claims Tracked

Each claim is extracted, categorized, and tracked:

{
  "claim": "AI will automate 30% of current work hours by 2030",
  "category": "labor",
  "industries": ["all"],
  "timeframe": "2025-2030",
  "outcome": "pending",
  "outcomeDate": null,
  "outcomeNote": "McKinsey estimate; early indicators suggest pace is accelerating"
}

Claim Categories

Category	Claims	Description
Capability	326	What AI can or will be able to do
Economics	186	Market size, revenue, cost impacts
Adoption	141	Deployment rates, enterprise uptake
Labor	117	Job displacement, creation, transformation
Timeline	82	When milestones will be reached
Risk	79	Safety, alignment, existential concerns
Competition	73	Company/country positioning, market dynamics
Deployment	70	Real-world implementation evidence
Infrastructure	57	Data centers, compute, energy requirements
Investment	45	Funding flows, valuations, VC activity
Regulation	43	Policy, governance, legal frameworks
Scientific	27	Research breakthroughs, benchmarks
Energy	20	Power consumption, sustainability
Benchmark	12	Performance metrics, test results
Geopolitical	9	US-China dynamics, national strategy

106 Named Experts Tracked

Including: Eric Schmidt, Dario Amodei, Demis Hassabis, Sam Altman, Jensen Huang, Geoffrey Hinton, Ray Kurzweil, Nick Bostrom, Francois Chollet, Yoshua Bengio, and 96 others.

Each expert's claims can be filtered and their prediction accuracy assessed over time.

Data Sources

190 entries curated from 695 video summaries covering:

• Expert interviews and keynotes
• Industry analysis and deployment reports
• Economic impact assessments
• AI safety and alignment discussions
• Technology capability demonstrations
• Investment and market analysis

Sources span 2023-2026, with claims dated and attributed to specific experts.

Schema

Each entry contains:

Field	Type	Description
source	string	Source title
channel	string	YouTube channel or publication
expert	string	Primary expert featured
expertAngle	string	Expert's known perspective/bias
publishDate	string	When the source was published
url	string	Source URL
summary	string	Overview of the source content
claims	object[]	Individual claims with category, timeframe, outcome
keyFacts	string[]	Verified facts and data points
tags	string[]	Searchable topic tags

Claim Object

Field	Type	Description
claim	string	The specific prediction or assertion
category	string	One of 15 categories (see above)
industries	string[]	Which industries are affected
timeframe	string	When the claim applies
outcome	string	pending, confirmed, partially-confirmed, wrong
outcomeDate	string	When outcome was determined
outcomeNote	string	Context on the outcome

Quick Start

The entire database is a single JSON file: ai-revolution-tracker.json

Load in Python

import json

with open('ai-revolution-tracker.json') as f:
    db = json.load(f)

entries = db['entries']

# Get all claims by category
from collections import Counter
claims = [c for e in entries for c in e.get('claims', [])]
cats = Counter(c['category'] for c in claims)
for cat, count in cats.most_common():
    print(f"{cat}: {count} claims")

Filter by Expert

# What has Jensen Huang predicted?
huang_claims = []
for entry in entries:
    if 'Huang' in entry.get('expert', ''):
        huang_claims.extend(entry.get('claims', []))

for c in huang_claims:
    print(f"[{c['category']}] {c['claim']}")
    print(f"  Timeframe: {c.get('timeframe', 'unspecified')}")
    print(f"  Outcome: {c.get('outcome', 'pending')}")

Track Prediction Accuracy

# Which predictions have been confirmed vs wrong?
outcomes = Counter(c.get('outcome', 'pending') for c in claims)
for outcome, count in outcomes.most_common():
    print(f"{outcome}: {count}")

Filter by Industry

# What do experts predict for healthcare?
healthcare = [c for c in claims
              if any('health' in i.lower() for i in c.get('industries', []))]
print(f"{len(healthcare)} claims about healthcare AI")

Use Cases

• Investors: Track which AI predictions are materializing and which are hype
• Journalists: Find specific, attributed expert claims with dates
• Researchers: Analyze prediction accuracy patterns across experts and categories
• Developers: Build prediction tracking dashboards or AI trend analysis tools
• Educators: Teach about technology forecasting with real data

Stats at a Glance

• 190 source entries
• 1,287 individual tracked claims
• 1,062 verified key facts
• 106 named experts
• 15 claim categories
• Sources span 2023-2026

Contributing

Found a claim that's been confirmed or proven wrong? Have an expert prediction to add? Open an issue or PR.

Guidelines:

• Claims must be specific and attributable (who said what, when)
• Include the source URL
• Use existing category taxonomy
• Track outcomes when verifiable

License

This data is released under CC BY 4.0. You are free to use, share, and adapt it for any purpose with attribution.

---

Built with VidBrainz research intelligence. Data extracted and structured using AI from curated video sources.