Numbers do not lie, but they can be made to obscure. The AI industry is awash in statistics designed to inflate valuations, justify investments, and manufacture inevitability. This page exists to provide a curated, contextualized reference of the figures that actually matter — with sources, caveats, and the context that press releases leave out.
We update this page monthly. All figures are sourced from public filings, government data, peer-reviewed research, and credible industry analysis. Where estimates diverge significantly across sources, we note the range and explain the discrepancy.
For related analysis, see our AI Regulation Tracker, HUMAIN Tracker, and AI Incident Tracker.
Market Size & Investment
Global AI Market
| Metric |
Value |
Source |
| Global AI market size (2025) |
$244 billion |
IDC |
| Projected global AI market (2026) |
$298 billion |
IDC |
| Projected global AI market (2030) |
$827 billion |
Grand View Research |
| Year-over-year growth rate |
22.1% |
Statista |
| Generative AI market (2025) |
$67 billion |
Bloomberg Intelligence |
| Generative AI projected (2028) |
$185 billion |
Bloomberg Intelligence |
| Enterprise AI spending (2025) |
$166 billion |
Gartner |
| AI share of total IT spending |
8.4% |
Gartner |
Venture Capital & Private Investment
| Metric |
Value |
Year |
| Total AI VC funding |
$97 billion |
2025 |
| Total AI VC funding |
$72 billion |
2024 |
| Total AI VC funding |
$49 billion |
2023 |
| Number of AI funding rounds |
4,200+ |
2025 |
| Median AI Series A |
$18 million |
2025 |
| Median AI Series B |
$52 million |
2025 |
| AI share of total VC funding |
38% |
2025 |
| AI unicorns created |
47 |
2025 |
Top AI Funding Rounds (2025)
| Company |
Amount |
Round |
Lead Investors |
| OpenAI |
$6.6 billion |
Series E |
Thrive Capital, Microsoft, SoftBank |
| Anthropic |
$4.0 billion |
Series D |
Amazon, Google, Salesforce |
| xAI |
$6.0 billion |
Series C |
Valor Equity, Sequoia |
| Databricks |
$3.5 billion |
Late-stage |
Thrive Capital |
| CoreWeave |
$2.0 billion |
Debt + equity |
Magnetar Capital |
| Mistral AI |
$1.1 billion |
Series C |
General Catalyst |
| Perplexity AI |
$900 million |
Series C |
IVP, Institutional Venture Partners |
| Figure AI |
$750 million |
Series B |
Microsoft, NVIDIA, Intel |
Sovereign & Government AI Investment
| Country/Fund |
AI Investment Commitment |
Timeframe |
| United States (federal) |
$32 billion announced |
FY2025-2027 |
| Saudi Arabia (HUMAIN + PIF) |
$100 billion announced |
2025-2030 |
| UAE (MGX + ADIA) |
$30 billion |
2025-2028 |
| China (national + provincial) |
$52 billion estimated |
2024-2027 |
| European Union (AI package) |
$22 billion |
2025-2027 |
| United Kingdom |
$8.5 billion |
2025-2030 |
| India (IndiaAI Mission) |
$1.2 billion |
2024-2029 |
| South Korea |
$7 billion |
2025-2029 |
| Japan |
$6.8 billion |
2024-2028 |
| France |
$3.5 billion |
2025-2030 |
| Canada (Pan-Canadian AI Strategy) |
$1.8 billion (CAD 2.4B) |
2024-2029 |
Context: The HUMAIN announcement of $100 billion represents the single largest sovereign AI commitment by a wide margin, though it includes infrastructure (data centers, energy) alongside pure AI research and development. Whether this capital is deployed effectively — and to whose benefit — is a central question of this publication.
Compute & Infrastructure
GPU Market
| Metric |
Value |
| NVIDIA data center GPU revenue (2025) |
$115 billion estimated |
| NVIDIA AI GPU market share |
85-90% |
| H100 GPUs shipped (2024) |
~2.5 million |
| B200 GPUs shipping (2025) |
Ramping production |
| Average H100 price (cloud rental, per hour) |
$2.50-3.50 |
| Average H100 purchase price |
$30,000-40,000 |
| Google TPU v5p pods deployed |
8,960 chips per pod |
| AMD MI300X revenue (2025) |
$9 billion estimated |
Training Costs
| Model |
Estimated Training Cost |
Year |
| GPT-4 |
$78 million |
2023 |
| Gemini Ultra |
$100+ million |
2023 |
| GPT-4.5 / GPT-5 generation |
$200-500 million (est.) |
2025 |
| Claude 3.5 Sonnet |
$50-80 million (est.) |
2024 |
| Llama 3 405B |
$60 million (est.) |
2024 |
| Frontier model training (2026 est.) |
$500M-1B |
2026 |
Data Center Infrastructure
| Metric |
Value |
| AI data center capacity under construction (US) |
18+ GW |
| New AI data center investment (global, 2025) |
$190 billion |
| Microsoft AI data center spending (FY2025) |
$80 billion |
| Google AI data center spending (2025) |
$75 billion |
| Amazon/AWS AI infrastructure (2025) |
$100 billion |
| Meta AI infrastructure spending (2025) |
$60 billion |
| Number of hyperscale data centers (global) |
1,000+ |
| AI share of total data center capacity |
34% and growing |
Energy & Environmental Impact
| Metric |
Value |
| AI data center electricity consumption (global, 2025) |
134 TWh estimated |
| Projected AI data center electricity (2028) |
325 TWh |
| AI share of global electricity demand |
1.5% (2025), projected 3.5% (2028) |
| Water consumption per ChatGPT query |
~500ml (cooling) |
| Carbon footprint of training GPT-4 |
~5,000 tonnes CO2e (est.) |
| Google total data center water use (2024) |
6.1 billion gallons |
| Microsoft total data center water use (2024) |
2.1 billion gallons |
Context: These environmental figures are among the most underreported aspects of the AI boom. Every query, every training run, every model has a physical footprint. For further analysis, see our AI Statistics environmental section updates.
AI Adoption
Enterprise Adoption
| Sector |
AI Adoption Rate (2025) |
Primary Use Case |
| Financial services |
72% |
Fraud detection, risk modeling |
| Healthcare |
58% |
Medical imaging, drug discovery |
| Manufacturing |
55% |
Predictive maintenance, quality control |
| Retail/e-commerce |
64% |
Recommendation, pricing, customer service |
| Technology |
83% |
Code generation, testing, infrastructure |
| Legal |
42% |
Document review, contract analysis |
| Education |
38% |
Tutoring, assessment, content creation |
| Agriculture |
28% |
Crop monitoring, yield prediction |
| Government |
34% |
Citizen services, fraud detection |
| Energy |
47% |
Grid optimization, predictive maintenance |
Consumer AI Usage
| Metric |
Value |
| ChatGPT weekly active users |
300 million+ |
| Claude monthly active users |
50 million+ (est.) |
| Gemini monthly active users |
120 million+ (est.) |
| Adults who have used generative AI (US) |
68% |
| Adults who use AI tools weekly (US) |
34% |
| Adults who use AI tools daily (US) |
12% |
| Generative AI awareness (global) |
87% |
| Trust in AI-generated content |
31% |
| Tool |
Estimated Users |
Category |
| GitHub Copilot |
1.8 million paid subscribers |
Code completion |
| Cursor |
500K+ users |
AI-native IDE |
| ChatGPT (code use) |
40M+ developers |
Multi-purpose |
| Replit AI |
25 million registered |
Code generation |
| Amazon CodeWhisperer |
1 million+ |
Code completion |
| Tabnine |
1 million+ |
Code completion |
See our AI Tools Database for comprehensive tool listings and reviews.
Workforce Impact
AI Job Market
| Metric |
Value |
Region |
| AI-related job postings (2025) |
420,000+ |
United States |
| AI job postings growth (YoY) |
+31% |
United States |
| AI engineer median salary |
$185,000 |
United States |
| ML engineer median salary |
$165,000 |
United States |
| AI research scientist salary (top labs) |
$300K-700K+ |
United States |
| AI safety researcher salary |
$180K-400K |
United States |
| AI talent shortage (unfilled positions) |
1.2 million |
Global |
AI Compensation at Frontier Labs
| Role |
Compensation Range (Total Comp) |
Notes |
| Research Scientist (OpenAI) |
$400K-900K |
Includes equity |
| Research Scientist (Anthropic) |
$350K-700K |
Includes equity |
| Research Scientist (DeepMind) |
$300K-650K |
Google equity |
| Senior ML Engineer (frontier lab) |
$350K-600K |
|
| AI Safety Researcher |
$180K-400K |
Varies widely |
| AI Policy Researcher |
$100K-200K |
Government roles lower |
Workforce Displacement Estimates
| Source |
Prediction |
Timeframe |
| McKinsey Global Institute |
12 million occupational transitions (US) |
By 2030 |
| World Economic Forum |
85 million jobs displaced, 97 million created |
By 2030 |
| Goldman Sachs |
300 million jobs affected globally |
By 2030 |
| OECD |
27% of jobs at high risk of automation |
Current |
| IMF |
40% of global employment exposed to AI |
Current |
| Brookings Institution |
36 million Americans in high-exposure jobs |
Current |
Context: “Affected” and “displaced” are different things. These projections vary dramatically in methodology and definition. Most serious analyses conclude that AI will transform far more jobs than it eliminates entirely — but the transformation itself can be deeply disruptive.
Safety & Incidents
AI Safety Metrics
| Metric |
Value |
| Documented AI incidents (cumulative) |
847 (INHUMAIN.AI tracker) |
| AI incidents logged (2025) |
312 |
| AI incidents rated “critical” (2025) |
23 |
| Countries affected by AI incidents |
67 |
| AI safety research papers (2025) |
2,400+ |
| AI safety research funding (2025) |
$820 million (est.) |
| Safety funding as % of total AI investment |
~0.8% |
| Companies with published safety policies |
43 (of top 100 AI companies) |
For detailed incident data, see our AI Incident Tracker.
AI Safety Funding Sources
| Source |
Amount (2025) |
Focus |
| Open Philanthropy |
$180 million |
Alignment research, governance |
| UK AI Safety Institute |
$120 million |
Evaluation, testing |
| US AISI (NIST) |
$90 million |
Standards, evaluation |
| Anthropic (internal safety) |
$80 million (est.) |
Constitutional AI, interpretability |
| Google DeepMind (safety) |
$100 million (est.) |
Alignment, governance |
| NSF AI safety grants |
$45 million |
Academic research |
| EU AI Office |
$30 million |
Regulatory capacity |
| Private foundations (other) |
$175 million |
Various |
Regulation
Global Regulatory Status
| Status |
Count |
Examples |
| Binding AI-specific laws enacted |
14 |
EU, China, Brazil, South Korea, Canada |
| Executive orders / national strategies |
30+ |
US, India, Japan, UAE, Saudi Arabia |
| AI regulatory frameworks (non-binding) |
40+ |
OECD, UNESCO, Singapore, Australia |
| Countries with AI safety institutes |
6 |
UK, US, Japan, Singapore, Canada, EU |
| International AI governance forums |
8 |
UN AI Advisory Body, GPAI, AI Safety Summit series |
Enforcement Actions
| Jurisdiction |
Enforcement Actions (2025) |
Largest Fine |
| EU (GDPR, AI-related) |
23 |
$14.5 million |
| EU (AI Act, prohibited practices) |
4 |
Pending |
| Italy (Garante) |
7 |
$15.6 million (OpenAI) |
| China (CAC) |
12 |
Undisclosed |
| US (FTC) |
8 |
$5.8 million (Rite Aid facial recognition) |
| South Korea (PIPC) |
5 |
$4.2 million |
For detailed regulatory tracking, see our AI Regulation Tracker and EU AI Act enforcement guide.
Research & Development
AI Research Output
| Metric |
Value (2025) |
Change (YoY) |
| AI research papers published |
185,000+ |
+18% |
| AI papers on arXiv |
92,000+ |
+22% |
| AI patent applications (global) |
145,000+ |
+25% |
| AI patents granted (US) |
38,000+ |
+30% |
| Countries producing AI research |
120+ |
Stable |
| AI PhD graduates (US) |
2,800+ |
+8% |
Research by Country (Share of Top AI Papers)
| Country |
Share of Top-Cited AI Papers |
Trend |
| United States |
32% |
Stable |
| China |
28% |
Up |
| United Kingdom |
8% |
Stable |
| Germany |
4% |
Stable |
| Canada |
4% |
Stable |
| France |
3% |
Up |
| South Korea |
3% |
Up |
| Israel |
2.5% |
Stable |
| Japan |
2% |
Down |
| India |
2% |
Up |
Model Releases (2025)
| Model |
Organization |
Parameters |
Open/Closed |
| GPT-5 |
OpenAI |
Undisclosed |
Closed |
| Gemini 2.0 Ultra |
Google DeepMind |
Undisclosed |
Closed |
| Claude 4 (Opus) |
Anthropic |
Undisclosed |
Closed |
| Llama 4 |
Meta |
400B+ |
Open weights |
| Mistral Large 3 |
Mistral AI |
Undisclosed |
Open weights |
| Grok-3 |
xAI |
Undisclosed |
Partially open |
| DeepSeek-V3 |
DeepSeek |
685B MoE |
Open weights |
| Qwen 3 |
Alibaba |
110B |
Open weights |
| Command R+ 2 |
Cohere |
Undisclosed |
Closed |
| Phi-4 |
Microsoft |
14B |
Open weights |
Benchmark Saturation
| Benchmark |
Best Score (2023) |
Best Score (2025) |
Human Baseline |
| MMLU |
86.4% |
92.3% |
~89.8% |
| HumanEval (code) |
85.0% |
95.1% |
~95% |
| GSM8K (math) |
92.0% |
97.8% |
~95% |
| ARC-Challenge |
96.3% |
98.7% |
~85% |
| HellaSwag |
95.3% |
98.1% |
~95.6% |
| GPQA Diamond |
41.3% |
65.2% |
~65% (experts) |
Context: Benchmark saturation is a growing problem. When models approach or exceed human baselines on established benchmarks, those benchmarks lose their ability to differentiate capability levels. The field is shifting toward more challenging evaluations (GPQA, SWE-bench, frontier math) and real-world performance metrics.
Key Takeaways
The numbers paint a consistent picture: investment is accelerating, capabilities are advancing, adoption is broadening, and safety infrastructure is not keeping pace. The ratio of safety funding to total AI investment — roughly 0.8% — is perhaps the single most important statistic on this page. It reflects a civilizational bet that the technology being built will not require the safety measures that its own creators acknowledge are necessary.
For context on what these numbers mean for the future, see our AI Doomsday Clock and AI Prediction Scorecard.
This page is updated monthly by the INHUMAIN.AI data team. All figures are best estimates based on available public data. Where proprietary or classified data would provide more accurate figures, we note the limitation. Corrections and updated data points can be submitted through our contact page.