3.9 KiB
3.9 KiB
AI Bubble Case Study — Summary Tables
Generated from
src.data.*modules. Data retrieved June 2026.
1. Bubble Indicators Comparison
| Indicator | Current Value | Historical Mean | Zone | Source |
|---|---|---|---|---|
| Shiller CAPE | 40.03 | 17.39 | Bubble (>30) | Yale/Shiller |
| Buffett Indicator | 219% | ~105% | Bubble (>200%) | Composite |
| S&P 500 P/E | 29.6 | ~17.9 | Warning | multpl.com |
| Dividend Yield | 1.04% | ~3.15% | Near historic low | multpl.com |
2. Hyperscaler Capex by Year/Company
| Year | Microsoft | Alphabet | Meta | Amazon | Combined |
|---|---|---|---|---|---|
| 2020 | $8B | $16B | $14B | $17B | $55.3B |
| 2021 | $21B | $22B | $16B | $52B | $110.5B |
| 2022 | $28B | $25B | $19B | $61B | $132.7B |
| 2023 | $30B | $32B | $28B | $71B | $160.8B |
| 2024 | $53B | $52B | $38B | $83B | $226.0B |
| 2025 | $80B | $75B | $60-$72B | $80-$131B | ~$326B |
| 2026 | $100B+ | $175-$185B | $115-$135B | $200B | ~$605B |
3. AI Startup Valuations
| Company | Valuation | Revenue Multiple | Date | Source |
|---|---|---|---|---|
| OpenAI | $840B | 31x revenue | Q1 2026 | CB Insights |
| Anthropic | $380B | 40x revenue | Q1 2026 | CB Insights |
| Perplexity AI | $5.3B | 27x revenue | Q1 2025 | Crunchbase |
| Scale AI | $14B | 7x revenue | 2024 | Crunchbase |
| Mistral AI | $8B | 40x revenue | 2024 | Company filings |
| Cohere | $3.7B | N/A (pre-profit) | 2024 | Crunchbase |
| Hugging Face | $4.5B | N/A (pre-profit) | 2024 | Crunchbase |
4. Agent Adoption Survey Data
| Survey | Production % | Scaling % | Sample Size | Date |
|---|---|---|---|---|
| LangChain 2025 | 57.3% | — | 1,340 | 2025-11 to 2025-12 |
| McKinsey 2025 | — | 23% | 1,993 | 2025-11 |
| PwC 2025 | 79% | — | 308 | 2025-04 |
5. Productivity Case Study Metrics
| Company | System | Key Metric | Value | Confidence |
|---|---|---|---|---|
| Klarna | AI Assistant (LangGraph + LangSmith) | FTE equivalent | 700 | HIGH |
| Klarna | AI Assistant (LangGraph + LangSmith) | Resolution time reduction | 80% | HIGH |
| Klarna | AI Assistant (LangGraph + LangSmith) | Task automation | 70% | HIGH |
| JPMorgan Chase | COiN (Contract Intelligence) | Hours saved/year | 360,000 | HIGH |
| JPMorgan Chase | COiN (Contract Intelligence) | Contracts processed/year | 12,000 | HIGH |
| JPMorgan Chase | COiN (Contract Intelligence) | Annual value | $150,000,000 | HIGH |
| ServiceNow (SnowGeek) | Now Assist + Agentic AI for IT Operations | Midnight escalation reduction | 73% | MEDIUM |
| ServiceNow (SnowGeek) | Now Assist + Agentic AI for IT Operations | MTTR improvement | 65% | MEDIUM |
| ServiceNow (SnowGeek) | Now Assist + Agentic AI for IT Operations | Annual downtime savings | $2,300,000 | MEDIUM |
| Morgan Stanley | DevGen.AI Developer Assistant | Developer hours saved | 280,000 | LOW |
6. Failure Modes
| Finding | Rate | Source | Confidence |
|---|---|---|---|
| 95% of corporate AI pilots deliver zero measurable return; only 5% reach production with impact | 95% | MIT Media Lab 2025 | HIGH |
| 42% of companies abandoned most AI initiatives in 2025 (up from 17% in 2024); 46% of PoCs scrapped before production | 42% | S&P Global 2025 | HIGH |
| Over 80% of AI projects fail — twice the failure rate of non-AI technology projects | 80% | RAND Corporation 2025 | MEDIUM |
| ~80% of autonomous-AI deployers cut headcount; ZERO correlation between layoffs and ROI | — | Gartner May 2026 | MEDIUM |
| Over 40% of agentic AI projects will be canceled by end of 2027 due to escalating costs, unclear value, or inadequate risk controls | 40% | Gartner prediction | MEDIUM |
| 88% AI adoption but only 31% scaling — vast majority stuck in pilots | — | McKinsey State of AI 2025 | HIGH |
| External partnership deployments succeed at ~67% vs ~33% for internal builds | — | MIT Media Lab 2025 | MEDIUM |
| 90%+ of companies have employees using personal AI tools; only 40% have official licensing | — | Multiple sources | MEDIUM |
Tables generated programmatically from research data modules.