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Estimated funding raised by privately held AI companies

CSET
See all data and research on:
Artificial Intelligence

What you should know about this indicator

  • This data tracks the flow of external money into private companies. It excludes internal spending on R&D and infrastructure.
  • On the recipient side, this only includes privately held AI companies. It excludes publicly traded companies like "Big Tech".
  • On the investor side, this includes any equity investor — venture capital firms, private equity firms, corporate investors (including "Big Tech" companies), and sovereign wealth funds. Only equity transactions are counted; grants, debt finance, and crowdfunding are excluded.
  • Where deal amounts aren't publicly disclosed — typically around 40% of the total — CSET assigns them an estimated value based on comparable deals.
  • This data only covers specific types of funding, and doesn't reflect total global spending on AI.
Estimated funding raised by privately held AI companies
CSET
Money raised by privately held AI companies through venture capital, private equity, and mergers and acquisitions. This includes CSET's own estimates for deals where the amount wasn't disclosed. Expressed in US dollars, adjusted for inflation.
Source
Center for Security and Emerging Technology (2026); U.S. Bureau of Labor Statistics (2026) – with major processing by Our World in Data
Last updated
April 27, 2026
Next expected update
October 2026
Date range
2016–2025
Unit
constant 2021 US dollars

Sources and processing

This data is based on the following sources

Center for Security and Emerging Technology – Country Activity Tracker: Artificial Intelligence

ETO's Country AI Activity Metrics dataset includes national-level metrics for AI-related research, patents, and private-market investment.

The metrics are derived from a variety of underlying data sources, including ETO's Merged Academic Corpus for research data; The Lens, PATSTAT, and 1790 Analytics for patents; and Crunchbase for company and investment data.

The dataset focuses on countries, not organizations or individuals, and on AI and its subfields. There are many ways to assess countries' AI activities, and the three types of metrics included here, while meaningful, are not exhaustive. The data also has a lag, making counts incomplete for recent years; the lag is especially significant for patent data.

Retrieved on
April 27, 2026
Retrieved from
https://cat.eto.tech/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Emerging Technology Observatory Country Activity Tracker: Artificial Intelligence

ETO's Country AI Activity Metrics dataset includes national-level metrics for AI-related research, patents, and private-market investment.

The metrics are derived from a variety of underlying data sources, including ETO's Merged Academic Corpus for research data; The Lens, PATSTAT, and 1790 Analytics for patents; and Crunchbase for company and investment data.

The dataset focuses on countries, not organizations or individuals, and on AI and its subfields. There are many ways to assess countries' AI activities, and the three types of metrics included here, while meaningful, are not exhaustive. The data also has a lag, making counts incomplete for recent years; the lag is especially significant for patent data.

Retrieved on
April 27, 2026
Retrieved from
https://cat.eto.tech/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Emerging Technology Observatory Country Activity Tracker: Artificial Intelligence

U.S. Bureau of Labor Statistics – US consumer prices

The Bureau of Labor Statistics reports the monthly Consumer Price Index (CPI) of individual goods and services for urban consumers at the national, city, and state levels. CPI is presented on an annual basis, which we have derived as the average of the monthly CPIs in a given year.

Retrieved on
March 20, 2026
Retrieved from
https://www.bls.gov/data/tools.htm
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
U.S. Bureau of Labor Statistics

The Bureau of Labor Statistics reports the monthly Consumer Price Index (CPI) of individual goods and services for urban consumers at the national, city, and state levels. CPI is presented on an annual basis, which we have derived as the average of the monthly CPIs in a given year.

Retrieved on
March 20, 2026
Retrieved from
https://www.bls.gov/data/tools.htm
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
U.S. Bureau of Labor Statistics

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

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Notes on our processing step for this indicator
  • We adjust the data for inflation so that values can be compared across years.
  • Reporting a time series of AI investments in nominal prices would make it difficult to compare observations across time. To make these comparisons possible, one has to take into account that prices change (inflation).
  • It is not obvious how to adjust this time series for inflation, and our team discussed the best solutions at our disposal.
  • It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services purchased through these investments. This would make it possible to calculate a volume measure of AI investments and tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost of some crucial AI technology has fallen rapidly in price.
  • In the absence of a comprehensive price index that captures the price of AI-specific goods and services, one has to rely on one of the available metrics for the price of a bundle of goods and services. Ultimately, we decided to use the US Consumer Price Index (CPI).
  • The US CPI does not provide us with a volume measure of AI goods and services, but it does capture the opportunity costs of these investments. The inflation adjustment of this time series of AI investments, therefore, lets us understand the size of these investments relative to whatever else these sums of money could have purchased.
  • We calculate regional and global totals from the country-level data CSET provides, following our region definitions. Countries that are not covered by the source are excluded from these totals. We do not show Africa's regional total. The source covers too few African countries to produce a meaningful aggregate.

Reuse this work

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Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Estimated funding raised by privately held AI companies”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska, and Max Roser (2023) - “Artificial Intelligence”. Data adapted from Center for Security and Emerging Technology, U.S. Bureau of Labor Statistics. Retrieved from https://auto-epoch.owid.pages.dev:8789/20260504-181956/grapher/private-investment-in-artificial-intelligence-cset.html [online resource] (archived on May 4, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Center for Security and Emerging Technology (2026); U.S. Bureau of Labor Statistics (2026) – with major processing by Our World in Data

Full citation

Center for Security and Emerging Technology (2026); U.S. Bureau of Labor Statistics (2026) – with major processing by Our World in Data. “Estimated funding raised by privately held AI companies – CSET” [dataset]. Center for Security and Emerging Technology, “Country Activity Tracker: Artificial Intelligence”; U.S. Bureau of Labor Statistics, “US consumer prices” [original data]. Retrieved May 8, 2026 from https://auto-epoch.owid.pages.dev:8789/20260504-181956/grapher/private-investment-in-artificial-intelligence-cset.html (archived on May 4, 2026).

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Excel / Google Sheets
=IMPORTDATA("https://auto-epoch.owid.pages.dev/grapher/private-investment-in-artificial-intelligence-cset.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://auto-epoch.owid.pages.dev/grapher/private-investment-in-artificial-intelligence-cset.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

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R
library(jsonlite)

# Fetch the data
df <- read.csv("https://auto-epoch.owid.pages.dev/grapher/private-investment-in-artificial-intelligence-cset.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://auto-epoch.owid.pages.dev/grapher/private-investment-in-artificial-intelligence-cset.metadata.json?v=1&csvType=full&useColumnShortNames=false")
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import delimited "https://auto-epoch.owid.pages.dev/grapher/private-investment-in-artificial-intelligence-cset.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear