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Summary

Comprehensive compilation of AI economic data showing $202.3B VC investment (2025), 30% of jobs potentially automatable by 2030, and McKinsey's estimate of $1.6-4.4T annual economic value from GenAI. Data is extensively sourced but mostly aggregates existing reports without original analysis or clear prioritization implications.

Economic & Labor Metrics

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Economic & Labor

LessWrong

ai-transition-model-parameters

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Key Links

Source	Link
Official Website	bls.gov
Wikipedia	en.wikipedia.org

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: Crunchbase 2025 Analysis↗, KPMG Venture Pulse↗

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: Stanford AI Index 2025↗, McKinsey State of AI 2025↗

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: Stanford AI Index 2025↗

Generative AI Specific

2023: $24 billion in global GenAI VC funding
2024: $45 billion (nearly double)
Major rounds: OpenAI ($40 billion at $300B valuation), Anthropic ($13 billion)

Sources: Stanford AI Index↗, CNBC↗

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: CNBC OpenAI↗, CNBC Anthropic↗

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: Forge Global Analysis via CNBC↗

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: Precedence Research↗, DemandSage↗

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: National University AI Job Statistics↗, ITIF Analysis↗

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: McKinsey Future of Work↗, Nexford University↗

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: BLS Employment Projections↗, St. Louis Fed Analysis↗

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: McKinsey Reports↗

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: AI Job Displacement Analysis↗

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: ITIF Analysis↗

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: Veritone Q1 2025 Analysis↗

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: SQ Magazine↗, AI Job Creation Statistics↗

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: Edison and Black↗, McKinsey Estimates↗

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: World Economic Forum↗, BLS Projections↗

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: McKinsey Economic Potential of GenAI↗, World Economic Forum↗

Value Concentration

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

Source: McKinsey Report↗

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: Penn Wharton Budget Model↗, Marketing AI Institute↗

Current Adoption Gap

99% of executives aware of AI; 92% planning to increase investment
Only 1% of organizations have achieved mature AI deployment

Source: McKinsey↗

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: DemandSage↗, McKinsey State of AI↗

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: ISG Enterprise AI Report↗, McKinsey↗

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: McKinsey State of AI↗

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: Netguru AI Adoption Statistics↗

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: Aristek Systems↗, McKinsey↗

Generative AI Market

2025: $59.01 billion
2031 projection: $400 billion (37.57% annual growth)

Source: DemandSage↗

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: McKinsey SaaS AI Era↗

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: McKinsey State of AI↗, Axis Intelligence↗

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: McKinsey State of AI 2024↗

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: IntegraNXT ROI Analysis↗

Manufacturing-Specific

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

Source: Sightsource Manufacturing ROI↗

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: Hypersense AI Adoption Trends↗

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: McKinsey↗, Virtasant↗

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: Agility at Scale↗

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: Glassdoor↗, Qubit Labs↗

By Industry (Top 5)

Media & Communication: $187,908
Information Technology: $165,652
Manufacturing: $139,738
Management & Consulting: $139,340
Aerospace & Defense: $131,685

Source: Qubit Labs Salary Guide↗

Growth Trajectory

August 2022: $231,000
March 2023: $268,000
March 2024: $300,600 (senior positions)

Source: NetCom Learning↗

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: Glassdoor ML Engineer↗, Levels.fyi↗, Indeed↗

By Experience

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

Source: DataCamp ML Salaries↗

Growth

2023 average: $131,000
2024 average: $166,000 ($35,000 increase)

Source: Glassdoor via DataCamp↗

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: BLS Data Scientists↗, Glassdoor Data Scientist↗, Levels.fyi↗

Senior Data Scientist

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

Source: Glassdoor Senior Data Scientist↗

Job Growth

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

Source: BLS Data Scientists Outlook↗

Compensation Trend

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

Source: AI Engineer Salary 2025↗

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: OECD Risk of Automation↗, CESI OECD Analysis↗

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: OECD Comparative Analysis↗

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: Minnesota DEED Automation Study↗

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: St. Louis Fed Analysis↗

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: BLS AI Impacts↗, Final Round AI↗

BLS-Identified Declining Occupations

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

Source: BLS Employment Projections↗

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: BLS Projections↗, National University↗

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: OECD What Happened to High-Risk Jobs↗

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: OECD Methodology↗

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: IMF Future of Growth↗

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: IMF Economic Outlook↗

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: Our World in Data↗

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: McKinsey Economic Potential↗

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: BLS Employment Projections 2024-2034↗

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: BLS Labor Force Projections↗

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: BLS Industry Projections↗, BLS AI Impacts↗

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: BLS AI Case Studies↗

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

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

Data Limitations

Private company data: Valuations based on funding rounds, not observable metrics
Corporate R&D: Private AI spending likely underreported
Job quality: Metrics track quantity but not compensation/conditions of new jobs
Indirect effects: Spillover impacts on non-AI sectors difficult to measure
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

Related Metrics Pages

Compute & Hardware - Infrastructure costs and capacity
AI Capabilities - Performance metrics related to economic value
Lab Behavior - Corporate practices affecting these metrics
Governance & Policy - Regulations affecting labor markets

Last Updated: December 2025

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