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Economic & Labor

economic-labor (E111)
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title: "Economic & Labor Metrics"
description: "Investment flows, labor market impacts, and economic indicators for AI development and deployment"
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import {R, EntityLink, DataExternalLinks} from '@components/wiki';



## Key Links

| Source | Link |
|--------|------|
| Official Website | [bls.gov](http://www.bls.gov/ect/) |
| Wikipedia | [en.wikipedia.org](https://en.wikipedia.org/wiki/Economic_indicator) |

<DataExternalLinks pageId="economic-labor" />

## Overview

This page tracks the economic and labor market dimensions of AI development, including investment flows, market valuations, employment impacts, and productivity gains. These metrics help assess AI's integration into the economy and its effects on work and value creation.

---

## Investment Metrics

### Total AI Investment (2024-2025)

**Global Venture Capital Investment**
- **2024**: \$114 billion in AI-related VC funding
- **2025**: \$202.3 billion (75% year-over-year increase)
- **Q1 2025**: \$80 billion raised by VC-backed companies (30% increase over Q4 2024)
- AI funding as share of total VC: 33% (2024) → 50% (2025)

*Sources: <R id="7896f83275efecdd">Crunchbase 2025 Analysis</R>, <R id="1af1400b24bbd0f3">KPMG Venture Pulse</R>*

**Corporate AI Investment**
- **2024**: \$252.3 billion in corporate AI investment (44.5% increase in private investment)
- **Big Tech Infrastructure Spending** (Microsoft, Alphabet, Amazon, Meta):
  - 2024: \$230 billion combined capex
  - 2025 planned: \$320 billion (39% increase)
  - Amazon alone: \$100+ billion planned for 2025

*Sources: <R id="1db7de7741f907e5">Stanford AI Index 2025</R>, <R id="c1e31a3255ae290d">McKinsey State of AI 2025</R>*

**US vs. Global Distribution**
- **US private AI investment (2024)**: \$109.1 billion (12x China's \$9.3 billion, 24x UK's \$4.5 billion)
- **2025 US share**: 79% of global AI funding (\$159 billion)
- **San Francisco Bay Area**: \$122 billion (77% of US AI funding in 2025)

*Source: <R id="1db7de7741f907e5">Stanford AI Index 2025</R>*

**Generative AI Specific**
- **2023**: \$24 billion in global GenAI VC funding
- **2024**: \$45 billion (nearly double)
- Major rounds: <EntityLink id="E218">OpenAI</EntityLink> (\$40 billion at \$300B valuation), <EntityLink id="E22">Anthropic</EntityLink> (\$13 billion)

*Sources: <R id="354489ac28697c93">Stanford AI Index</R>, <R id="3c6cac635dba0c16">CNBC</R>*

**Data Quality**: High reliability for reported VC deals and public company capex. Private corporate R&D may be underestimated.

---

## Market Capitalization & Valuations

### AI Company Valuations (Private Market)

**Top Valuations (2025)**
- **OpenAI**: \$100 billion (most valuable private company ever)
  - Revenue: \$12 billion (2025), up from \$3.7 billion (2024)
  - Annualized revenue: \$13 billion (July 2025)
- **Anthropic**: \$183 billion → \$350 billion range (2025)
  - Revenue growth: \$87 million (early 2024) → \$7 billion (late 2025) - 80x increase
- **xAI**: \$90 billion
  - Revenue: \$100 million (late 2024) → \$500 million annualized (mid-2025)
- **Databricks**: \$100 billion

*Sources: <R id="3c6cac635dba0c16">CNBC OpenAI</R>, <R id="787a2639f9e64ca5">CNBC Anthropic</R>*

**Aggregate Private AI Market**
- Top 7 private AI companies: \$1.3 trillion combined valuation (nearly doubled in past year)
- 4x increase since late 2022 (ChatGPT launch)

*Source: <R id="3c6cac635dba0c16">Forge Global Analysis via CNBC</R>*

**Total AI Market Size**
- **2024**: \$638.23 billion global AI market
- **2025**: \$757.58 billion projected
- **2034 projection**: \$3.68 trillion (19.20% CAGR)

*Sources: <R id="f166562e5c51daa8">Precedence Research</R>, <R id="f48b4210ef95dbd6">DemandSage</R>*

**Data Quality**: Private valuations based on funding rounds; actual worth may vary significantly. Public market data more reliable.

---

## Labor Market Impact

### Jobs Displaced by AI

**Current Displacement (2024)**
- **14% of all workers** have already been displaced by AI (higher among younger/mid-career workers in tech/creative fields)
- **12,700 jobs lost** directly to AI in 2024 (0.1% of all layoffs)
- **Goldman Sachs estimate**: 2.5% of US employment at risk if AI use cases expanded; 6-7% if widely adopted

*Sources: <R id="9f9735edfba1b066">National University AI Job Statistics</R>, <R id="0c182d0511d4ee57">ITIF Analysis</R>*

**Projected Displacement (2030)**
- **30% of current US jobs** could be automated by 2030 (McKinsey)
- **McKinsey**: Activities accounting for 30% of hours worked could be automated
- **McKinsey**: 40% of jobs in highly automatable roles; 57% of work hours technically automatable

*Sources: <R id="42c37f8b5b402f95">McKinsey Future of Work</R>, <R id="20938c000c581ae4">Nexford University</R>*

**Bureau of Labor Statistics Projections (2023-2033)**
- **Bank tellers**: -15% (51,400 jobs eliminated)
- **Cashiers**: -11% (353,100 jobs eliminated)
- **Computer and mathematical occupations**: Unemployment increases correlated with 80% AI exposure score

*Sources: <R id="e331256e28403b8d">BLS Employment Projections</R>, <R id="075aac90b6b8460f">St. Louis Fed Analysis</R>*

**High-Risk Sectors**
- Office support, customer service, food service
- Manufacturing (30% of jobs automatable by mid-2030s)
- Financial services (shorter-term vulnerability)

**Low-Risk Sectors**
- Construction and skilled trades
- Personal services (food service, medical assistants, cleaners)
- Healthcare professionals and STEM roles (17-30% growth projected)

*Source: <R id="d709902c9ca11c41">McKinsey Reports</R>*

**Gender Disparities**
- **79% of employed women** in US in high-risk automation jobs vs. 58% of men
- **High-income nations**: 9.6% of women's jobs at highest risk vs. 3.2% for men

*Source: <R id="9f9735edfba1b066">AI Job Displacement Analysis</R>*

**Data Quality**: Moderate. Displacement projections vary widely between studies (9-47% of jobs). Task-based approaches (OECD) show lower risk than occupation-based (Frey & Osborne).

### Jobs Created by AI

**Current Job Creation (2024)**
- **119,900 direct jobs created** by AI in 2024:
  - 8,900 AI model development/operations jobs (ML engineers, data scientists)
  - 110,000+ construction jobs from AI-driven data center construction
- **Net effect**: +107,200 jobs (119,900 created - 12,700 lost)

*Source: <R id="0c182d0511d4ee57">ITIF Analysis</R>*

**Job Market Growth (Q1 2025)**
- **35,445 AI-related positions** in US (25.2% increase from Q1 2024, 8.8% from Q4 2024)
- AI job postings **more than doubled** from 2023 to 2024
- **2025**: 56% increase in AI job share compared to 2024

*Source: <R id="97d57edf34dc02e8">Veritone Q1 2025 Analysis</R>*

**US AI Job Postings**
- **H1 2025**: 1.2 million AI-related job postings (vs. 980,000 in H1 2024)
- **Global AI employment growth**: 26% year-over-year (2024-2025)

*Sources: <R id="baf18e80d7d5e43e">SQ Magazine</R>, <R id="baf18e80d7d5e43e">AI Job Creation Statistics</R>*

**Long-term Projections**
- **97 million new jobs** projected by 2025 (World Economic Forum)
- **McKinsey**: 20-50 million new AI jobs globally by 2030
- **BLS**: Computer and information research scientists +23% (2022-2032)

*Sources: <R id="97526424d207d64e">Edison and Black</R>, <R id="1cadef354ccfc708">McKinsey Estimates</R>*

**Data Science & ML Engineer Outlook**
- **500,000+ ML engineering positions** available worldwide (2025)
- **US BLS**: Data science jobs +36% (2023-2033)
- **Operations research analyst**: +23% growth

*Sources: <R id="f117cde8e2ea2a1d">World Economic Forum</R>, <R id="641872cbfea515f5">BLS Projections</R>*

**Data Quality**: Good for job postings and BLS projections. Long-term forecasts (97M jobs) highly uncertain.

---

## Productivity & Economic Impact

### AI Productivity Gains

**McKinsey Global Institute Estimates**
- **Annual economic value**: \$1.6-4.4 trillion from generative AI alone
  - Equivalent to ~4% of global GDP
  - For context: UK's entire 2021 GDP was \$3.1 trillion
- **Labor productivity growth**: 0.1-0.6% annually through 2040
- **Combined with all automation**: 0.2-3.3 percentage points annual productivity increase
- **GDP growth impact**: 1.5-3.4 percentage point increase in average annual GDP growth (developed world, next decade)

*Sources: <R id="5d69a0f184882dc6">McKinsey Economic Potential of GenAI</R>, <R id="5a4132229d8d8b50">World Economic Forum</R>*

**Value Concentration**
- 75% of GenAI value in 4 areas: Customer operations, marketing/sales, software engineering, R&D

*Source: <R id="5d69a0f184882dc6">McKinsey Report</R>*

**Alternative Estimates**
- **Daron Acemoglu**: More conservative - 0.07% annual productivity increase, 0.9-1.8% GDP increase over 10 years
- **Penn Wharton Budget Model**: 1.5% productivity/GDP increase by 2035, 3% by 2055, 3.7% by 2075

*Sources: <R id="0e088b8a65ae5079">Penn Wharton Budget Model</R>, <R id="ebbc0b066e5ccaf8">Marketing AI Institute</R>*

**Current Adoption Gap**
- **99% of executives** aware of AI; **92% planning** to increase investment
- **Only 1%** of organizations have achieved mature AI deployment

*Source: <R id="5d69a0f184882dc6">McKinsey</R>*

**Data Quality**: Moderate. Wide range in estimates reflects genuine uncertainty about adoption speed and productivity translation.

---

## Enterprise Adoption & Revenue

### Fortune 500 & Enterprise Adoption

**Adoption Rates (2024-2025)**
- **99%+ of Fortune 500** companies use AI
- **92% of Fortune 500** use ChatGPT
- **70% of Fortune 500** use both ChatGPT and Microsoft Copilot
- **78% of all organizations** use AI in at least one business function (up from 55% in 2023)
- **71% of organizations** use generative AI regularly (up from 33% in 2023)

*Sources: <R id="117d6da50b968b24">DemandSage</R>, <R id="9593a5e63fb2e295">McKinsey State of AI</R>*

**Scaling Challenges**
- **31% of use cases** reached full production (2025) - double from 2024
- **42% of companies** abandoned most AI initiatives in 2025 (up from 17% in 2024)
- **Only 26%** have capabilities to move beyond proof-of-concept to production
- **74% still struggle to scale** despite regular use

*Sources: <R id="fb7dd896db51b368">ISG Enterprise AI Report</R>, <R id="c1e31a3255ae290d">McKinsey</R>*

**By Company Revenue Size**
- **\$1B+ revenue companies**: 58% fully scaling AI to automate operations
- **High performers**: 75% scaling or scaled AI vs. 33% of other organizations
- **Under \$100M revenue**: Only 29% at scaling phase

*Source: <R id="c1e31a3255ae290d">McKinsey State of AI</R>*

**Regional Adoption**
- **North America**: 82% adoption
- **Europe**: 80% adoption (23 percentage point increase since 2024)
- **India**: 59% integration (global leader)
- **UAE**: 58%
- **United States**: 33% (surprisingly low)

*Source: <R id="b342edfd8dcd3796">Netguru AI Adoption Statistics</R>*

**Data Quality**: High for survey data on large companies. Self-reported "adoption" definitions may vary.

### AI Products & Services Revenue

**AI Software Market**
- **2024**: \$98 billion
- **2030 projection**: \$391.43 billion (30% CAGR)
- **McKinsey long-term**: \$1.5-4.6 trillion by 2040

*Sources: <R id="d3dba3ecd4766199">Aristek Systems</R>, <R id="ebbc0b066e5ccaf8">McKinsey</R>*

**Generative AI Market**
- **2025**: \$59.01 billion
- **2031 projection**: \$400 billion (37.57% annual growth)

*Source: <R id="f48b4210ef95dbd6">DemandSage</R>*

**Enterprise AI Spending**
- **Global enterprise AI application spending**: ≈\$5 billion (8x increase year-over-year)
- Still less than 1% of total software application spending

*Source: <R id="de5b54261b7a8e9c">McKinsey SaaS AI Era</R>*

**Data Quality**: Good for reported market sizes. Revenue attribution to "AI" components can be ambiguous.

---

## Cost Savings & ROI

### Enterprise Cost Savings

**Aggregate Impact**
- **McKinsey 2024**: Leading companies attribute >10% of EBIT to generative AI
- **Average enterprise**: \$1.4 million annual savings (mid-market companies)
- **Typical ROI**: 3.2x within 18 months
- **Operational cost reduction**: 35% within 18 months

*Sources: <R id="c1e31a3255ae290d">McKinsey State of AI</R>, <R id="0def949f17cba497">Axis Intelligence</R>*

**McKinsey Survey Results**
- **42% of organizations** report cost reductions from AI (including GenAI)
- **59% report revenue increases**
- 10 percentage point increase in cost reduction reports vs. previous year

*Source: <R id="67d5fc8183ab61e3">McKinsey State of AI 2024</R>*

**By Industry (ROI Multiples)**
- **Financial services**: 4-6x ROI (highest, due to data-rich environments)
- **Manufacturing**: 3-5x ROI (predictive maintenance, quality control)
- **Healthcare/Professional services**: 2.5-4x ROI

*Source: <R id="3fd24c6c4e5d1484">IntegraNXT ROI Analysis</R>*

**Manufacturing-Specific**
- Early adopters: **15-25% operational cost recovery**
- **30-50% defect reduction**
- **73% hit ROI within 18 months** (Gartner)

*Source: <R id="cfae5ea22644a458">Sightsource Manufacturing ROI</R>*

**Time Savings**
- **Average**: 1 hour saved per worker per day
- **5-year projection**: 12 hours/week savings
- **Energy/utilities**: 75 minutes daily
- **Manufacturing**: 62 minutes daily
- **Sales (Lumen Technologies)**: 4 hours weekly per seller

*Source: <R id="ffdf885bc42c0a8a">Hypersense AI Adoption Trends</R>*

**Implementation Costs vs. Returns**
- **Off-the-shelf AI model deployment**: ≈\$2 million
- **Customizing existing model**: ≈\$10 million
- **Building from scratch**: ≈\$200 million
- **Accenture**: Strategic scaling yields 3x returns vs. siloed POCs

*Sources: <R id="5d69a0f184882dc6">McKinsey</R>, <R id="ac1d6fbfdc373395">Virtasant</R>*

**Reality Check**
- **BCG late 2024**: Only 4% of companies have "cutting-edge" AI capabilities enterprise-wide
- **22% starting** to realize substantial gains
- **74% have yet** to show tangible value despite investment

*Source: <R id="1f42de66a839e1b5">Agility at Scale</R>*

**Data Quality**: Moderate. Self-reported ROI data subject to selection bias. Early adopters likely overrepresented.

---

## Compensation & Salaries

### AI Engineer Salaries (2024-2025)

**AI Engineers**
- **Median (Glassdoor)**: \$138,986/year
- **Average (2025)**: \$153,000 (9% increase from 2024)
- **High-level positions**: \$206,000 average (Glassdoor early 2025)
- **Salary range**:
  - 25th percentile: \$110,824
  - 75th percentile: \$176,648
  - 90th percentile (top decile): \$217,654

*Sources: <R id="457d9d9bf018a9d7">Glassdoor</R>, <R id="8d2fb27e31cd4c07">Qubit Labs</R>*

**By Industry (Top 5)**
1. Media & Communication: \$187,908
2. Information Technology: \$165,652
3. Manufacturing: \$139,738
4. Management & Consulting: \$139,340
5. Aerospace & Defense: \$131,685

*Source: <R id="8d2fb27e31cd4c07">Qubit Labs Salary Guide</R>*

**Growth Trajectory**
- August 2022: \$231,000
- March 2023: \$268,000
- March 2024: \$300,600 (senior positions)

*Source: <R id="d7ef3b86cab3e17a">NetCom Learning</R>*

### Machine Learning Engineer Salaries

**Current Compensation (2024-2025)**
- **Median (Glassdoor)**: \$158,804/year
- **Median with total comp (Levels.fyi)**: \$260,000 (top tech companies)
- **Average (Indeed)**: \$182,904/year (based on 3,800 salaries)
- **Salary range**:
  - 25th percentile: \$127,573
  - 75th percentile: \$200,269
  - 90th percentile (top decile): \$245,185

*Sources: <R id="de489373a70b1101">Glassdoor ML Engineer</R>, <R id="6a63d67067867cc8">Levels.fyi</R>, <R id="ef1242b1c59227c1">Indeed</R>*

**By Experience**
- **Entry-level**: \$53,578 - \$184,575/year
- **5+ years experience**: \$102,282 - \$232,816/year

*Source: <R id="d2821ce4ebf02d55">DataCamp ML Salaries</R>*

**Growth**
- 2023 average: \$131,000
- 2024 average: \$166,000 (\$35,000 increase)

*Source: <R id="d2821ce4ebf02d55">Glassdoor via DataCamp</R>*

### Data Scientist Salaries

**Current Compensation (2024-2025)**
- **Median (BLS, May 2024)**: \$112,590/year
  - Lowest 10%: under \$63,650
  - Highest 10% (top decile): over \$194,410
- **Average (Glassdoor)**: \$153,361/year
  - 25th percentile: \$121,243
  - 75th percentile: \$196,583
  - 90th percentile: \$243,959
- **Median with total comp (Levels.fyi)**: \$171,000

*Sources: <R id="641872cbfea515f5">BLS Data Scientists</R>, <R id="47c628a15ef621ea">Glassdoor Data Scientist</R>, <R id="0ca8b8d0e4d99748">Levels.fyi</R>*

**Senior Data Scientist**
- **Average**: \$230,901/year
  - 25th percentile: \$190,211
  - 75th percentile: \$286,138
  - 90th percentile: \$344,615

*Source: <R id="31b1478781cad608">Glassdoor Senior Data Scientist</R>*

**Job Growth**
- **BLS projection**: +34% (2024-2034) - much faster than average
- **~23,400 openings** projected annually

*Source: <R id="641872cbfea515f5">BLS Data Scientists Outlook</R>*

**Compensation Trend**
- Worldwide AI talent demand driving 5-9% compensation increases (early 2024 to mid-2025)

*Source: <R id="7ef75d3927178357">AI Engineer Salary 2025</R>*

**Data Quality**: High for BLS and major salary aggregators. Tech company total comp (stock, bonuses) may exceed base salary significantly.

---

## Automation Risk Scores

### Risk by Occupation Type

**OECD Analysis (2024)**
- **Overall average**: 9% of jobs highly automatable (across 21 OECD countries)
- **High risk definition**: >25 out of 100 skills/abilities easily automatable
- **Updated 2023 estimate**: 27% of jobs at high automation risk (OECD average)

*Sources: <R id="4dd560a2becd896d">OECD Risk of Automation</R>, <R id="bcecce4fa2fefab7">CESI OECD Analysis</R>*

**Cross-Country Variation**
- **Korea**: 6% high automation risk
- **Austria**: 12% high automation risk
- **United States**: 9% (vs. earlier 47% estimates using occupation-based approach)

*Source: <R id="4dd560a2becd896d">OECD Comparative Analysis</R>*

**US-Specific Risk Distribution**
- **12.6% of workers** (~19.2 million): High or very high risk
- **39%**: Little or no risk
- **Remainder**: Slight or moderate risk

*Source: <R id="e92bd3d9eb6b3a88">Minnesota DEED Automation Study</R>*

**Federal Reserve Analysis (2022-2025)**
- Occupations with higher AI exposure experienced larger unemployment increases
- **Correlation coefficient**: 0.47 between AI exposure and unemployment growth
- **Computer/mathematical occupations**: ~80% AI exposure score, steepest unemployment rises

*Source: <R id="075aac90b6b8460f">St. Louis Fed Analysis</R>*

### High-Risk Occupations

**Specific Job Categories**
- Service, sales, and office jobs (highest risk category)
- Computer programmers
- Accountants and auditors
- Legal and administrative assistants
- Customer service representatives
- Models, technical writers, broadcast announcers

*Sources: <R id="e331256e28403b8d">BLS <EntityLink id="E512">AI Impacts</EntityLink></R>, <R id="fced7006cd38a439">Final Round AI</R>*

**BLS-Identified Declining Occupations**
- Procurement clerks
- Credit authorizers
- Customer service representatives
- Nonmedical secretaries
- Bank tellers (-15% by 2033)
- Cashiers (-11% by 2033)

*Source: <R id="e331256e28403b8d">BLS Employment Projections</R>*

### Low-Risk Occupations

**Protected Categories**
- Healthcare professionals (+17-30% growth projected)
- STEM professionals
- Construction and skilled trades
- Personal services (food service, medical assistants, cleaners)
- Personal financial advisors (+17.1% growth 2023-2033)
- Database administrators (AI supports, demand outweighs automation)

*Sources: <R id="e331256e28403b8d">BLS Projections</R>, <R id="9f9735edfba1b066">National University</R>*

### Historical Employment Growth Despite Automation

**OECD Findings (Within-Country)**
- Jobs at high risk: 6% employment growth (2012-recent)
- Jobs at low risk: 18% employment growth
- Low-educated workers increasingly concentrated in high-risk occupations

*Source: <R id="0f360bea8367b6b7">OECD What Happened to High-Risk Jobs</R>*

**Methodological Note**
Task-based approaches (OECD) yield lower risk estimates than occupation-based approaches (Frey & Osborne). Workers within same occupation often perform different tasks, reducing uniform automation risk.

*Source: <R id="4dd560a2becd896d">OECD Methodology</R>*

**Data Quality**: Moderate to high. Methodological differences create wide variation (9% vs. 47% estimates). Task-based approaches considered more accurate.

---

## GDP & Macroeconomic Indicators

### GDP Per Capita Growth & AI Contribution

**IMF Perspective (2024)**
- **Baseline forecast**: Global growth at 3.1% (5 years out) - lowest in decades
- **Key driver of slowdown**: Widespread decline in total factor productivity
- **Contributing factors**: Capital/labor misallocation, demographic pressures, reduced private investment
- **AI as solution**: "Best chance at relaxing supply-side constraints" and reversing productivity decline

*Source: <R id="855234aec7f69630">IMF Future of Growth</R>*

**Potential AI Impact**
- Could produce "major sustained surge in productivity" reversing downward trend
- Urgent reforms needed to leverage AI for productivity gains
- Without policy action or tech advances, medium-term growth falls below prepandemic levels

*Source: <R id="68951477f38ac666">IMF Economic Outlook</R>*

**Current GDP Per Capita Trends**
- Large inequality between countries
- Poorest countries: under \$1,000/year average income
- Rich countries: 50x+ higher (\$50,000+/year)

*Source: <R id="4baa5e93c716716c">Our World in Data</R>*

**McKinsey GDP Growth Estimates**
- **Generative AI impact**: 1.5-3.4 percentage point increase in average annual GDP growth (developed world, next decade)
- **Combined automation**: 0.2-3.3 percentage points annual productivity boost

*Source: <R id="5d69a0f184882dc6">McKinsey Economic Potential</R>*

**Data Quality**: Low to moderate. AI-specific GDP contribution difficult to isolate. Estimates based on modeling rather than observed data.

### Labor Force Participation Rate

**Overall US Trends (BLS 2024-2034 Projections)**
- **Total employment**: 175.2 million projected by 2034
- **Growth rate**: +3.1% (2024-2034), slower than 13.0% growth (2014-2024)
- **New jobs**: 5.2 million over decade
- **Main driver**: Healthcare and social assistance sector

*Source: <R id="ac97a109486292aa">BLS Employment Projections 2024-2034</R>*

**Participation Rate Trends**
- **Overall**: Continual decline projected
- **Primary cause**: Demographic shifts (aging population, older individuals less likely to work)
- **Prime-age participation**: Further decline projected through 2033
  - Driven by men's rate (declining for decades)
  - Women's rate more stable

*Source: <R id="08973e0c4ac54944">BLS Labor Force Projections</R>*

**Sector-Specific Impacts**
- **Declining sectors**: Retail trade (-1.2%, most job losses), manufacturing (automation adoption), mining/oil & gas (-1.6%, productivity gains from robotics/drones)
- **Growing sectors**: Healthcare (+17-30% for professionals), STEM roles, personal financial advisors (+17.1%)

*Sources: <R id="ef020d882d6579a6">BLS Industry Projections</R>, <R id="e331256e28403b8d">BLS AI Impacts</R>*

**AI's Mixed Effect**
- Some occupations see productivity-driven growth limits (technical writers, customer service reps)
- Others see AI as supportive tool increasing demand (database administrators, architects/engineers)
- Overall: Structural change and disruption expected for decades

*Source: <R id="e331256e28403b8d">BLS AI Case Studies</R>*

**Data Quality**: High for BLS demographic projections. AI-specific impact on participation rates difficult to isolate from other trends.

---

## Key Uncertainties & Data Gaps

### Major Uncertainties

1. **Productivity Translation**: Wide variance in estimates (0.07% to 3.4% GDP growth)
2. **Adoption Speed**: Only 1-4% of companies at mature deployment despite high awareness
3. **Job Displacement Timeline**: Projections range from 9% to 47% of jobs at risk
4. **ROI Realization**: 74% of companies yet to show tangible value despite investment
5. **Geographic Distribution**: Will AI benefits concentrate or distribute globally?

### Data Limitations

1. **Private company data**: Valuations based on funding rounds, not observable metrics
2. **Corporate R&D**: Private AI spending likely underreported
3. **Job quality**: Metrics track quantity but not compensation/conditions of new jobs
4. **Indirect effects**: Spillover impacts on non-AI sectors difficult to measure
5. **Definitional issues**: What counts as "AI" varies across studies and companies

### Update Frequency

- **Investment data**: Quarterly (VC), Annual (corporate capex)
- **Employment data**: Monthly (BLS), Annual (projections)
- **Salary data**: Annual (BLS), Continuous (Glassdoor, Levels.fyi)
- **Market size**: Annual estimates, Semi-annual updates
- **Productivity/GDP**: Quarterly (GDP), Annual (productivity estimates)

---

## Sources Summary

### Primary Data Sources
- **Stanford AI Index Report** (annual): Investment, corporate spending, research trends
- **McKinsey State of AI** (annual): Adoption, productivity, economic impact
- **Bureau of Labor Statistics** (continuous): Employment, wages, projections
- **OECD Reports** (periodic): Automation risk, international comparisons
- **IMF/World Bank** (quarterly/annual): Macroeconomic indicators, GDP
- **Crunchbase/PitchBook** (continuous): Venture capital, private market valuations

### Salary & Compensation
- **Glassdoor, Levels.fyi, Indeed, PayScale** (continuous): Real-time salary data
- **BLS Occupational Outlook** (annual): Official government wage statistics

### Market Research
- **Gartner, Forrester, IDC** (periodic): Enterprise adoption and spending
- **Goldman Sachs, Morgan Stanley** (periodic): Market analysis and projections

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## Related Metrics Pages

- **<EntityLink id="E65">Compute & Hardware</EntityLink>** - Infrastructure costs and capacity
- **<EntityLink id="E50">AI Capabilities</EntityLink>** - Performance metrics related to economic value
- **<EntityLink id="E184">Lab Behavior</EntityLink>** - Corporate practices affecting these metrics
- **<EntityLink id="E154">Governance & Policy</EntityLink>** - Regulations affecting labor markets

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**Last Updated**: December 2025

**Next Review**: March 2026 (post-Q1 2025 data releases)