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Annual private investment in artificial intelligence

See all data and research on:
Artificial Intelligence

What you should know about this indicator

World aggregate does not include data for Micronesia, Tonga, Samoa, Kiribati, Fiji, Papua New Guinea, Palau, Tuvalu, Gibraltar, Jersey, Kosovo, Moldova, Isle of Man, Andorra, Montenegro, San Marino, Liechtenstein, Monaco, Vatican City, Afghanistan, Kyrgyzstan, Laos, Hong Kong, Bhutan, Brunei Darussalam, Maldives, Syria, North Korea, Myanmar, Timor-Leste, Nepal, Turkmenistan, Palestine, Yemen, Kuwait, Cape Verde, Equatorial Guinea, Swaziland, Namibia, Central African Republic (the), Angola, Ethiopia, Niger, Benin, Gabon, Gambia, Rwanda, Burkina Faso, São Tomé and Príncipe, Burundi, Guinea, Guinea-Bissau, Cameroon, Sierra Leone, Lesotho, Somalia, Chad, Liberia, Libya, South Sudan, Congo, Sudan, Malawi, Togo, Mali, Djibouti, Mauritania, Eritrea, Mozambique, Comoros, Antigua and Barbuda, Bolivia, Suriname, Nicaragua, Bahamas, Saint Vincent and the Grenadines, Grenada, Guyana, Haiti, Honduras, Cuba, Turks and Caicos Islands, Saint Lucia, and Dominica.

Annual private investment in artificial intelligence
Only includes private-market investment flows, such as venture capital; excludes all investment in publicly traded companies, such as the "Big Tech" firms. This data is expressed in US dollars, adjusted for inflation.
Source
Center for Security and Emerging Technology (2024); U.S. Bureau of Labor Statistics (2024) – processed by Our World in Data
Last updated
July 16, 2024
Next expected update
July 2025
Date range
2013–2023
Unit
constant 2021 US$

Sources and processing

This data is based on the following sources

The research data in CAT (Country Attributes and Topics) is derived from ETO's Merged Academic Corpus (MAC), which contains detailed information on over 270 million scholarly articles worldwide. CAT uses only AI-related articles from the MAC. Articles are attributed to countries based on the author organizations listed in each article's metadata. An article is attributed to a country if it lists at least one author affiliated with an organization in that country.

The top ten authors for each country are identified based on the number of citations to articles they released while affiliated with institutions in that country. CAT classifies articles into AI subfields using subject assignment scores in the MAC. Articles are assigned to up to three subfields based on their scores.

CAT includes patent data from 1790 Analytics and Dimensions, and it counts AI-related patent families, including patent applications and granted patents. Patents are attributed to the country where they are filed, not necessarily the inventor's nationality. CAT also uses Crunchbase data to identify AI-related companies based on various criteria and includes investment metrics for these companies.

The data in CAT is updated at least once a quarter, with plans for more frequent updates in the future.

Retrieved on
July 16, 2024
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 (Center for Security and Emerging Technology, 2024)

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
May 16, 2024
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.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

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Notes on our processing step for this indicator
  • Reporting a time series of AI investments in nominal prices (i.e., without adjusting for inflation) means it makes little sense to compare observations across time; it is therefore not very useful. To make comparisons across time possible, one has to take into account that prices change (e.g., there is inflation).
  • It is not obvious how to adjust this time series for inflation, and we debated it at some length within our team.
  • It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services that these investments purchased. This would make it possible to calculate a volume measure of AI investments, and it would 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 for 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. In the end 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.

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  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
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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: Annual private investment in artificial intelligence”, 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://ourworldindata.org/grapher/private-investment-in-artificial-intelligence-cset [online resource]
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 (2024); U.S. Bureau of Labor Statistics (2024) – processed by Our World in Data

Full citation

Center for Security and Emerging Technology (2024); U.S. Bureau of Labor Statistics (2024) – processed by Our World in Data. “Annual private investment in artificial intelligence” [dataset]. Center for Security and Emerging Technology, “Country Activity Tracker: Artificial Intelligence”; U.S. Bureau of Labor Statistics, “US consumer prices” [original data]. Retrieved July 26, 2024 from https://ourworldindata.org/grapher/private-investment-in-artificial-intelligence-cset