Last updated: June 2026. Reviewed by Report AI editorial. Every figure on this page is linked to its primary source.
STATE OF AI 2024 — KEY DATA POINTS
78%
Orgs using AI
by year-end 2024
Stanford HAI / McKinsey
71%
Using GenAI regularly
up from 33% (2023)
Stanford HAI
$252.3B
Corporate AI investment
2024 (+26% YoY)
Stanford HAI
2024 was the year AI adoption stopped being a question. Organizational AI use jumped from 72% in early 2024 to 78% by year-end, regular generative-AI use roughly doubled to 71%, and corporate AI investment hit $252.3 billion (Stanford HAI; McKinsey). Model capability surged on every benchmark, the gap between leading labs narrowed, and the cost of running AI fell off a cliff. This page is the sourced, year-specific picture of where AI stood in 2024.
Executive summary
- Adoption inflected. Organizational AI use rose from 72% (early 2024) to 78% (late 2024); regular generative-AI use rose from 65% to 71% in the same year.
- Money followed. Corporate AI investment hit $252.3B (+26% YoY); newly funded generative-AI startups nearly tripled.
- Capability surged. SWE-bench solve rates jumped from 4.4% (2023) to 71.7% (2024); MMMU +18.8 pp, GPQA +48.9 pp.
- Economics shifted. AI inference costs fell ~280-fold from late 2022 to late 2024 — from ~$20 to ~$0.07 per million tokens.
Adoption climbed all year
2024 was the inflection year for organizational AI. McKinsey’s State of AI showed organizational use rising from 72% in early 2024 to 78% by late 2024, while regular generative-AI use rose from 65% to 71% over the same window — nearly double the 33% recorded in 2023 (McKinsey). Stanford HAI’s 2025 AI Index confirmed the same numbers and called the curve faster than the early uptake of either the PC or the internet (Stanford HAI).
| Metric | 2023 | Early 2024 | Late 2024 | Source |
|---|---|---|---|---|
| Organizations using AI (≥1 function) | 55% | 72% | 78% | Stanford HAI / McKinsey |
| Regularly using generative AI | 33% | 65% | 71% | Stanford HAI / McKinsey |
For the multi-year view see our State of AI: A Year-by-Year Timeline (2022–2026) and current data in AI Adoption Statistics 2026.
Investment passed a quarter-trillion dollars
Capital scaled with adoption. Corporate AI investment reached $252.3 billion in 2024, a 26% increase over 2023, while the number of newly funded generative-AI startups nearly tripled (Stanford HAI). The flow was uneven — a handful of frontier labs absorbed a disproportionate share — but the headline number framed 2024 as the year AI funding stopped being cyclical and became structural. For the deeper investment picture, see AI Investment & Funding Statistics 2026.
Models got dramatically more capable
2024 produced the largest one-year capability jumps Stanford HAI had ever recorded. On SWE-bench (a real-world coding benchmark) solve rates jumped from 4.4% in 2023 to 71.7% in 2024; on MMMU (multimodal) scores rose 18.8 percentage points; on GPQA (graduate-level QA) +48.9 points (Stanford HAI). The leading-lab gap narrowed in parallel: by January 2024 the top closed-weight model led the best open-weight by 8.0%; by February 2025 the gap was 1.7%.
| Benchmark | 2023 | 2024 | One-year gain |
|---|---|---|---|
| SWE-bench (coding) | 4.4% | 71.7% | +67.3 pp |
| GPQA (graduate QA) | — | — | +48.9 pp |
| MMMU (multimodal) | — | — | +18.8 pp |
For 2026 benchmark coverage and the broader competitive landscape, see AI Models & Benchmarks Statistics 2026.
Inference cost fell off a cliff
One number explains much of the year’s adoption surge: the cost of AI inference dropped roughly 280-fold between late 2022 and late 2024 — from about $20 per million tokens to around $0.07 (Stanford HAI). Use cases that were uneconomic at the start of 2023 became cheap by the end of 2024.
Frequently asked questions
How much did AI adoption grow in 2024?
Organizational AI use rose from 72% in early 2024 to 78% by year-end, and regular generative-AI use rose from 65% to 71% — nearly double the 33% recorded in 2023, per McKinsey and Stanford HAI.
How much was invested in AI in 2024?
Corporate AI investment reached $252.3 billion globally in 2024, a 26% increase over 2023; the number of newly funded generative-AI startups nearly tripled, per Stanford HAI.
What was the biggest model-capability story of 2024?
SWE-bench coding solve rates jumping from 4.4% to 71.7% in a single year was the headline; multimodal (MMMU +18.8 pp) and graduate-level reasoning (GPQA +48.9 pp) saw the next-largest one-year gains.
How much did AI cost in 2024?
AI inference cost fell about 280-fold between late 2022 and late 2024 — from roughly $20 to $0.07 per million tokens — reshaping the economics of nearly every AI use case.
Data sources & methodology
- McKinsey QuantumBlack — The State of AI, March 2024 + November 2024 surveys.
Verified data points: organizational AI use 72% (early 2024) → 78% (late 2024); regular GenAI 65% → 71%; nearly doubling from 33% in 2023.
Source: mckinsey.com/quantumblack/state-of-ai - Stanford HAI — AI Index Report 2025.
Verified data points: $252.3B corporate AI investment in 2024 (+26% YoY); SWE-bench 4.4% → 71.7%; MMMU +18.8 pp; GPQA +48.9 pp; ~280× inference cost decline.
Source: hai.stanford.edu/ai-index/2025-ai-index-report
Related pages: State of AI: Year-by-Year Timeline · AI Adoption Statistics 2026 · AI Investment & Funding Statistics 2026 · AI Models & Benchmarks Statistics 2026
Related Reports & Resources
Other reports in this cluster (Year-in-Review): State of AI: A Year-by-Year Timeline (2022–2026) — every year of the AI adoption climb in one place, with the milestone for each.
Spotlight data points: 78% org AI use, 71% GenAI use (Stanford HAI) · $252.3B corporate AI investment, +26% YoY (Stanford HAI) · SWE-bench 4.4% → 71.7% in one year.
Background reading: AI Adoption Statistics 2026 · AI Investment & Funding 2026 · AI Model Benchmarks 2026 · Generative AI Statistics 2026.
Key concepts: Generative AI · AI Inference · Tokens.
Browse the category: All Statistics & Reports → Year-in-Review cluster.