{"id": 1026207, "name": "Estimated investment - Field: All", "unit": "constant 2021 US$", "createdAt": "2025-04-22T20:22:24.000Z", "updatedAt": "2025-10-22T09:06:39.000Z", "coverage": "", "timespan": "2014-2023", "datasetId": 7064, "shortUnit": "$", "columnOrder": 0, "shortName": "estimated_investment__field_all", "catalogPath": "grapher/artificial_intelligence/2025-04-18/cset/cset#estimated_investment__field_all", "dimensions": {"years": {"values": [{"id": 2014}, {"id": 2016}, {"id": 2019}, {"id": 2020}, {"id": 2021}, {"id": 2022}, {"id": 2017}, {"id": 2018}, {"id": 2023}, {"id": 2015}]}, "entities": {"values": [{"id": 16, "name": "Albania", "code": "ALB"}, {"id": 17, "name": "Algeria", "code": "DZA"}, {"id": 21, "name": "Argentina", "code": "ARG"}, {"id": 22, "name": "Armenia", "code": "ARM"}, {"id": 23, "name": "Australia", "code": "AUS"}, {"id": 24, "name": "Austria", "code": "AUT"}, {"id": 25, "name": "Azerbaijan", "code": "AZE"}, {"id": 27, "name": "Bahrain", "code": "BHR"}, {"id": 28, "name": "Bangladesh", "code": "BGD"}, {"id": 29, "name": "Barbados", "code": "BRB"}, {"id": 30, "name": "Belarus", "code": "BLR"}, {"id": 4, "name": "Belgium", "code": "BEL"}, {"id": 31, "name": "Belize", "code": "BLZ"}, {"id": 194, "name": "Bermuda", "code": "BMU"}, {"id": 35, "name": "Bosnia and Herzegovina", "code": "BIH"}, {"id": 36, "name": "Botswana", "code": "BWA"}, {"id": 37, "name": "Brazil", "code": "BRA"}, {"id": 39, "name": "Bulgaria", "code": "BGR"}, {"id": 42, "name": "Cambodia", "code": "KHM"}, {"id": 44, "name": "Canada", "code": "CAN"}, {"id": 197, "name": "Cayman Islands", "code": "CYM"}, {"id": 172, "name": "Chile", "code": "CHL"}, {"id": 171, "name": "China", "code": "CHN"}, {"id": 170, "name": "Colombia", "code": "COL"}, {"id": 143, "name": "Cote d'Ivoire", "code": "CIV"}, {"id": 165, "name": "Croatia", "code": "HRV"}, {"id": 163, "name": "Cyprus", "code": "CYP"}, {"id": 162, "name": "Czechia", "code": "CZE"}, {"id": 161, "name": "Denmark", "code": "DNK"}, {"id": 160, "name": "Dominican Republic", "code": "DOM"}, {"id": 201, "name": "Ecuador", "code": "ECU"}, {"id": 65, "name": "Egypt", "code": "EGY"}, {"id": 259, "name": "El Salvador", "code": "SLV"}, {"id": 156, "name": "Estonia", "code": "EST"}, {"id": 158, "name": "Ethiopia", "code": "ETH"}, {"id": 155, "name": "Finland", "code": "FIN"}, {"id": 3, "name": "France", "code": "FRA"}, {"id": 152, "name": "Georgia", "code": "GEO"}, {"id": 6, "name": "Germany", "code": "DEU"}, {"id": 150, "name": "Ghana", "code": "GHA"}, {"id": 149, "name": "Greece", "code": "GRC"}, {"id": 148, "name": "Guatemala", "code": "GTM"}, {"id": 138, "name": "Hungary", "code": "HUN"}, {"id": 207, "name": "Iceland", "code": "ISL"}, {"id": 137, "name": "India", "code": "IND"}, {"id": 136, "name": "Indonesia", "code": "IDN"}, {"id": 135, "name": "Iran", "code": "IRN"}, {"id": 134, "name": "Iraq", "code": "IRQ"}, {"id": 2, "name": "Ireland", "code": "IRL"}, {"id": 133, "name": "Israel", "code": "ISR"}, {"id": 8, "name": "Italy", "code": "ITA"}, {"id": 132, "name": "Jamaica", "code": "JAM"}, {"id": 14, "name": "Japan", "code": "JPN"}, {"id": 130, "name": "Jordan", "code": "JOR"}, {"id": 131, "name": "Kazakhstan", "code": "KAZ"}, {"id": 129, "name": "Kenya", "code": "KEN"}, {"id": 208, "name": "Kuwait", "code": "KWT"}, {"id": 122, "name": "Latvia", "code": "LVA"}, {"id": 124, "name": "Lebanon", "code": "LBN"}, {"id": 119, "name": "Lithuania", "code": "LTU"}, {"id": 210, "name": "Luxembourg", "code": "LUX"}, {"id": 118, "name": "Madagascar", "code": "MDG"}, {"id": 116, "name": "Malaysia", "code": "MYS"}, {"id": 212, "name": "Malta", "code": "MLT"}, {"id": 213, "name": "Mauritius", "code": "MUS"}, {"id": 113, "name": "Mexico", "code": "MEX"}, {"id": 112, "name": "Mongolia", "code": "MNG"}, {"id": 110, "name": "Morocco", "code": "MAR"}, {"id": 5, "name": "Netherlands", "code": "NLD"}, {"id": 106, "name": "New Zealand", "code": "NZL"}, {"id": 103, "name": "Nigeria", "code": "NGA"}, {"id": 66, "name": "North Macedonia", "code": "MKD"}, {"id": 102, "name": "Norway", "code": "NOR"}, {"id": 217, "name": "Oman", "code": "OMN"}, {"id": 101, "name": "Pakistan", "code": "PAK"}, {"id": 100, "name": "Panama", "code": "PAN"}, {"id": 98, "name": "Paraguay", "code": "PRY"}, {"id": 97, "name": "Peru", "code": "PER"}, {"id": 96, "name": "Philippines", "code": "PHL"}, {"id": 11, "name": "Poland", "code": "POL"}, {"id": 95, "name": "Portugal", "code": "PRT"}, {"id": 93, "name": "Puerto Rico", "code": "PRI"}, {"id": 226, "name": "Qatar", "code": "QAT"}, {"id": 92, "name": "Romania", "code": "ROU"}, {"id": 12, "name": "Russia", "code": "RUS"}, {"id": 227, "name": "Saint Kitts and Nevis", "code": "KNA"}, {"id": 90, "name": "Saudi Arabia", "code": "SAU"}, {"id": 89, "name": "Senegal", "code": "SEN"}, {"id": 88, "name": "Serbia", "code": "SRB"}, {"id": 233, "name": "Seychelles", "code": "SYC"}, {"id": 86, "name": "Singapore", "code": "SGP"}, {"id": 85, "name": "Slovakia", "code": "SVK"}, {"id": 83, "name": "Slovenia", "code": "SVN"}, {"id": 81, "name": "South Africa", "code": "ZAF"}, {"id": 127, "name": "South Korea", "code": "KOR"}, {"id": 9, "name": "Spain", "code": "ESP"}, {"id": 141, "name": "Sri Lanka", "code": "LKA"}, {"id": 10, "name": "Sweden", "code": "SWE"}, {"id": 7, "name": "Switzerland", "code": "CHE"}, {"id": 198, "name": "Taiwan", "code": "TWN"}, {"id": 76, "name": "Tajikistan", "code": "TJK"}, {"id": 64, "name": "Tanzania", "code": "TZA"}, {"id": 75, "name": "Thailand", "code": "THA"}, {"id": 73, "name": "Trinidad and Tobago", "code": "TTO"}, {"id": 71, "name": "Tunisia", "code": "TUN"}, {"id": 70, "name": "Turkey", "code": "TUR"}, {"id": 68, "name": "Uganda", "code": "UGA"}, {"id": 67, "name": "Ukraine", "code": "UKR"}, {"id": 72, "name": "United Arab Emirates", "code": "ARE"}, {"id": 1, "name": "United Kingdom", "code": "GBR"}, {"id": 13, "name": "United States", "code": "USA"}, {"id": 63, "name": "Uruguay", "code": "URY"}, {"id": 238, "name": "Venezuela", "code": "VEN"}, {"id": 84, "name": "Vietnam", "code": "VNM"}, {"id": 355, "name": "World", "code": "OWID_WRL"}, {"id": 60, "name": "Zambia", "code": "ZMB"}, {"id": 80, "name": "Zimbabwe", "code": "ZWE"}]}}, "descriptionShort": "Only includes private \u2014 market investment such as venture capital; excludes all investment in publicly traded companies, such as \"Big Tech\" firms. This data is expressed in US dollars, adjusted for inflation.", "descriptionProcessing": "- 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).\n- It is not obvious how to adjust this time series for inflation, and our team discussed the best solutions at our disposal.\n- 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.\n- In the absence of a comprehensive price index that captures the price of AI \u2014 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).\n- 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.\n- World aggregate does not include data for Micronesia, Tonga, Samoa, Kiribati, Fiji, Papua New Guinea, Palau, Tuvalu, Bermuda, Armenia, Belarus, Georgia, Gibraltar, Jersey, Kosovo, Moldova, Isle of Man, Iceland, Albania, Andorra, Bosnia and Herzegovina, Malta, Montenegro, San Marino, North Macedonia, Liechtenstein, Monaco, Vatican City, Guernsey, Afghanistan, Kyrgyzstan, Bahrain, Laos, Bangladesh, Lebanon, Bhutan, Maldives, Cambodia, Syria, Tajikistan, Cyprus, Mongolia, North Korea, Myanmar, Timor-Leste, Nepal, Turkmenistan, Pakistan, Palestine, Iraq, United Arab Emirates, Uzbekistan, Kazakhstan, Qatar, Vietnam, Yemen, Kuwait, Algeria, Cape Verde, Equatorial Guinea, Swaziland, Namibia, Central African Republic (the), Angola, Ethiopia, Niger, Benin, Gabon, Nigeria, Botswana, Gambia, Rwanda, Burkina Faso, Ghana, S\u00e3o Tom\u00e9 and Pr\u00edncipe, Burundi, Guinea, Senegal, Guinea-Bissau, Seychelles, Cameroon, Sierra Leone, Lesotho, Somalia, Chad, Liberia, Libya, South Sudan, Congo, Madagascar, Sudan, C\u00f4te d'Ivoire, Malawi, Togo, Mali, Djibouti, Mauritania, Uganda, Egypt, Mauritius, Tanzania, Zambia, Eritrea, Mozambique, Zimbabwe, Comoros, Antigua and Barbuda, Bolivia, Suriname, Nicaragua, Dominican Republic, Bahamas, Ecuador, Paraguay, Barbados, Saint Vincent and the Grenadines, El Salvador, Belize, Grenada, Saint Kitts and Nevis, Guatemala, Guyana, Haiti, Honduras, Trinidad and Tobago, Jamaica, Venezuela, Puerto Rico, Cayman Islands (the), Turks and Caicos Islands, Saint Lucia, and Dominica.", "type": "int", "grapherConfigIdETL": "01965f2a-8586-7581-bfd3-aa081c02b4b5", "dataChecksum": "11907232626952021580", "metadataChecksum": "1048884339455138168", "datasetName": "Country Activity Tracker: Artificial Intelligence", "updatePeriodDays": 365, "datasetVersion": "2025-04-18", "nonRedistributable": false, "display": {"name": "All", "unit": "constant 2021 US$", "shortUnit": "$"}, "schemaVersion": 2, "presentation": {"titlePublic": "Annual private investment in artificial intelligence (estimated)", "topicTagsLinks": ["Artificial Intelligence"]}, "descriptionKey": ["The data likely underestimates total global AI investment, as it only captures certain types of private equity transactions, excluding other significant channels and categories of AI \u2014 related spending.", "The dataset only covers private \u2014 market investment such as venture capital. It excludes non \u2014 equity financing, such as debt and grants, and publicly traded companies, including major Big Tech firms. As a result, significant investments from public companies, corporate R&D, government funding, and broader infrastructure costs (like data centers and hardware) are not captured, limiting the data's coverage of global AI investments.", "The data's \"World\" aggregate reflects the total investment represented in the data, but may not represent global AI efforts comprehensively, especially in countries not included in the data.", "Companies are classified as AI \u2014 related based on keyword and industry tags, potentially including firms not traditionally seen as AI \u2014 focused while missing others due to definitional differences.", "Many investment values are undisclosed, so the source relies on median values from similar transactions, introducing some uncertainty. Additionally, investment origin is attributed to company headquarters, which may overlook cross \u2014 border structures or varied investor origins.", "One \u2014 time events, such as large acquisitions, can distort yearly figures, while broader economic factors like interest rates and market sentiment can influence investment trends independently of AI \u2014 specific developments."], "origins": [{"id": 3435, "title": "Country Activity Tracker: Artificial Intelligence", "description": "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.\n\nThe 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.\n\nCAT 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.\n\nThe data in CAT is updated at least once a quarter, with plans for more frequent updates in the future.", "producer": "Center for Security and Emerging Technology", "citationFull": "Center for Security and Emerging Technology (2025). Emerging Technology Observatory Country Activity Tracker, Artificial Intelligence.", "attributionShort": "CSET", "urlMain": "https://cat.eto.tech/", "urlDownload": "https://zenodo.org/records/14522783/files/cat.zip?download=1", "dateAccessed": "2025-04-18", "datePublished": "2025", "license": {"url": "https://eto.tech/tou/", "name": "CC BY-NC 4.0"}}, {"id": 3393, "title": "US consumer prices", "description": "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.", "producer": "U.S. Bureau of Labor Statistics", "citationFull": "U.S. Bureau of Labor Statistics", "urlMain": "https://www.bls.gov/data/tools.htm", "dateAccessed": "2025-04-12", "datePublished": "2025", "license": {"url": "https://www.bls.gov/opub/copyright-information.htm", "name": "Public domain"}}]}