variables: 946941

This table contains all the variables of all the datasets we have at Our World In Data. Note that this includes only the information about the variables like name and default display settings, not the data itself.

Data license: CC-BY

This data as json

id name unit description createdAt updatedAt code coverage timespan datasetId sourceId shortUnit display columnOrder originalMetadata grapherConfigAdmin shortName catalogPath dimensions schemaVersion processingLevel processingLog titlePublic titleVariant attributionShort attribution descriptionShort descriptionFromProducer descriptionKey descriptionProcessing licenses license grapherConfigETL type sort dataChecksum metadataChecksum
946941 Private investment in AI in the United States constant 2021 US$   2024-07-02 19:13:37 2024-07-22 08:46:10     2013-2023 6605   $ 
{
    "unit": "constant 2021 US$",
    "shortUnit": "$",
    "numDecimalPlaces": 0
}
 0     united_states grapher/artificial_intelligence/2024-06-28/ai_private_investment/ai_private_investment#united_states   2 major           Includes companies that received more than $1.5 million in investment (not adjusted for inflation). This data is expressed in US dollars, adjusted for inflation.   
[
    "The source is not clear about the extent to which investment figures cover infrastructure, computational power, and support services required to develop, deploy, and operationalize AI applications",
    "For more information on how the costs to train frontier AI models are distributed refer to the article [How Much Does It Cost to Train Frontier AI Models?](https://epochai.org/blog/how-much-does-it-cost-to-train-frontier-ai-models) by EPOCH."
]
 - 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.     
{
    "note": "This data is expressed in constant 2021 US$. Inflation adjustment is based on the US Consumer Price Index (CPI)."
}
 int 
[]
 04afbd3f7585edd07b48de38a93f5dad dfcdac5ddbd83c3157e235cdfd9a1432

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