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National accounts do not currently isolate AI as a separate input, so statisticians cannot read AI’s contribution directly from a single productivity figure. Instead, they compare measured output with labour and capital, locate AI-related production and spending across existing industries and asset categories, and use industry-level analysis to estimate possible effects. Those estimates can show evidence consistent with AI-related gains, but they do not establish how much productivity growth AI caused.
What do national accounts measure when they measure productivity?
National accounts organize information about a country’s production, income, spending, investment and employment. Productivity statistics use parts of those accounts to compare what an economy produces with the labour and capital used to produce it. They help answer whether output is rising faster than measured inputs—not, by themselves, which technology caused the change.
Labour productivity is commonly expressed as output per hour worked. Its growth can be decomposed into two broad sources: capital deepening, when the amount of capital available per hour of work increases, and multifactor productivity (MFP), the change in output not explained by measured changes in labour and capital. The OECD’s 2026 Compendium of Productivity Indicators emphasizes that MFP is a residual, not a technology meter.
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MFP can reflect technological change and efficiency improvements, but also management practices, learning, changes in labour quality that the data do not capture, and other influences. An increase in MFP therefore cannot simply be labelled “AI productivity.” Nor does a rise in labour productivity prove that AI caused it: output per hour can change for several reasons, including changes in capital per worker.
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Is AI showing up in productivity statistics yet?
AI-related activity already appears in the accounts, but it is spread across existing categories rather than recorded under one AI heading. The U.S. Bureau of Economic Analysis (BEA) says there is no direct U.S. national-accounts line item for AI; the measured contribution must therefore be inferred from other data.
Production, purchases and uses associated with AI can be recorded across software, computer and data services, research and development (R&D), information and communications technology (ICT) equipment, and related services. These categories also include activity that is not AI. A rise in software investment, for instance, does not tell statisticians how much of that software is AI or what productivity effect it produces.
This is why the answer to “is AI in the statistics?” has two parts. AI-related production and spending may be present in the underlying accounts, but their presence does not mean that the accounts separately identify AI or measure its causal effect on productivity. In its February 2026 paper, Early Estimates of the Impact of AI Within BEA’s Industry Economic Accounts, BEA presents indirect estimates rather than a direct AI measure.
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There is no single method that cleanly isolates AI’s causal contribution to national productivity. Analysts combine productivity accounting with evidence about industries, investment and technology use, while making assumptions about what counts as AI-related and when adoption began.
1. Compare output with measured inputs
The first step is to measure output and the labour and capital used to produce it. Analysts can then examine labour productivity and decompose its growth into capital deepening and MFP. This establishes where measured change appears in the accounts, but it cannot identify AI as the cause: MFP includes influences beyond AI, and capital deepening covers capital that is not necessarily AI-related.
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2. Find AI-related production and spending in existing categories
Researchers map activity that could be associated with AI across industries and types of spending. BEA describes a thematic, or satellite-account, approach that would reorganize and disaggregate existing national-account data to make AI-related production, investment and use more visible, while retaining the core accounting framework. Such an account would improve visibility; it would not automatically turn every associated change in output into an AI-caused productivity gain.
3. Decide whether purchases are investment or intermediate consumption
AI-related expenditure may involve software, databases, ICT equipment, R&D, data, workforce skills and computing infrastructure. National accounts distinguish fixed investment from intermediate consumption according to the asset boundary and economic ownership—not simply whether a purchase is expensive, useful for a long time or described as a technology expense.
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4. Relate industry adoption or exposure to productivity outcomes
Researchers can compare industries’ AI adoption or exposure with measured productivity, or use models based on industry accounts. These analyses can provide evidence consistent with an AI-related effect. Their conclusions depend, however, on such choices as the measure of exposure, the period assumed for adoption, input coverage and the structure of the model. A correlation between exposure and productivity is not, on its own, proof of causation.
BEA’s February 2026 early estimates illustrate why assumptions matter: the findings are less robust under an alternative assumption about when AI became pervasive. The result should therefore be read as an estimate conditional on a model specification and timing assumption, rather than as a definitive number for AI’s contribution to U.S. productivity.
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| Approach | What it can show | What it cannot establish by itself |
|---|---|---|
| Productivity decomposition | How output growth relates to measured labour and capital, including the contributions of capital deepening and MFP. | How much of MFP was caused by AI. |
| Industry comparison | Whether industries with more AI adoption or exposure have different measured productivity outcomes. | That AI, rather than other differences between industries, caused the outcome. |
| Industry-account model | An estimate of effects under a specified model and assumptions about inputs, timing and AI-related activity. | A model-independent, directly observed AI contribution. |
| Thematic account | A more visible organization of AI-related production, investment and use within existing accounting data. | A causal productivity estimate solely by virtue of reorganizing the data. |
The distinctions in the table matter when comparing estimates. An estimate may vary because of its productivity metric, the labour and capital included, the treatment of cloud services and other purchases, the assumed adoption date, or the way prices and depreciation are handled. A headline number without those details can suggest more certainty or comparability than the method supports.
Why can AI’s costs appear before its productivity benefits?
AI adoption can require more than buying a piece of equipment or subscribing to software. Businesses may also invest in data, training, software development and organizational change. Some of these intangible inputs are difficult to observe or classify consistently, making it harder to measure the full cost of adoption.
If costs are recorded while benefits are delayed, early productivity statistics may show the effort of adoption before they show any improvement in output. Workers may need time to learn new systems; workflows may need redesign; complementary software or data may need to be developed. The OECD’s 2025 and 2026 productivity compendia discuss the difficulty of observing intangible investment and the possibility that this can understate adoption costs or delay the appearance of gains in measured productivity.
That timing problem does not prove a productivity payoff will eventually occur. It means only that short-term figures may not capture all relevant costs or benefits at once. Analysts must distinguish that possibility from evidence that a particular adoption has raised output per input.
What changes under the 2025 System of National Accounts?
The 2025 System of National Accounts (SNA) expands the treatment of intangible assets, notably data and other intellectual-property products. This should provide a framework better suited to some forms of intangible investment relevant to AI. It does not immediately create a comprehensive AI productivity measure: implementation takes time, and the OECD’s 2026 compendium expects statistics under the new framework with sufficient country coverage for comprehensive cross-country analysis no earlier than the early 2030s. That is a forecast, not a guaranteed release date.
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The SNA also supplies a statistical definition of AI. The OECD’s 2025 compendium reports it as “a computer program operating a system capable of recognition, reasoning, communication, and prediction, simulating human recognition, reasoning, and communication”. This is a definition for statistical purposes; broader policy or public discussions may use different definitions. A consistent statistical definition helps set boundaries, but applying it still requires data that identify relevant production, spending and use.
What is the United States measuring, and what is the UK developing?
Country approaches illustrate the gap between estimating effects and building a clearer statistical account. BEA has published early U.S. estimates based on industry economic accounts, while ONS is developing a UK AI thematic account. Their work should not be treated as directly comparable measures of national AI productivity: they differ in purpose and evidence, and the UK work remains experimental.
| Agency and work | Status and scope | Important qualification |
|---|---|---|
| BEA, Concepts and Challenges of Measuring Production of Artificial Intelligence in the U.S. Economy (January 2025) | Discusses how AI production is currently spread across existing categories and the option of a thematic account. | Identifying AI-related production is a measurement challenge; the paper does not establish a direct AI productivity line item. |
| BEA, Early Estimates of the Impact of AI Within BEA’s Industry Economic Accounts (February 2026) | Reports early, indirect estimates using industry economic accounts. | Findings depend on assumptions, including the assumed timing of AI becoming pervasive; an alternative timing assumption yields less robust findings. |
| ONS, Measuring artificial intelligence in the UK economy using a thematic account (September 2026) | Developing a UK AI thematic account and assessing the available data landscape. | ONS says the work is not official statistics and that existing data granularity makes AI’s contribution difficult to isolate. |
ONS’s stated roadmap is to continue scoping and data-landscape work through the first quarter of 2027, followed by planned compilation of experimental estimates of AI compute-service supply and its contribution to UK GDP for a reference year in current prices. This is a plan that may change, and experimental estimates should not be mistaken for finalized official statistics.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do recent productivity figures say about AI?
The OECD reports that labour productivity growth across OECD countries was 1.2% in 2024, up from 0.6% in 2023. Its 2026 compendium says this development may be consistent with an AI-enhancing role, but that interpretation is far from conclusive and carries substantial uncertainty. These rates describe measured labour productivity growth, not the portion attributable to AI.
A national productivity figure covers the combined effects of many activities and forces. Even if AI contributes to gains in some businesses or industries, an aggregate figure cannot identify that contribution without additional evidence and assumptions. Conversely, a small or absent change in the aggregate does not, by itself, show what happened in a particular firm or sector.
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How should readers interpret an estimate of AI productivity?
Before treating a reported number as an AI effect, check what the number actually measures and what had to be assumed to produce it. A useful estimate should make its coverage and limitations clear enough for readers to distinguish measured productivity from estimated attribution.
- Direct or indirect: Is AI separately identified in the accounts, or inferred from broader data? Current U.S. national accounts do not provide a direct AI line item.
- What counts as AI-related: Does the estimate cover software, data, R&D, hardware, cloud services, training and organizational changes, or only part of that activity?
- How spending is classified: Are purchases treated as fixed investment or intermediate consumption, and how does economic ownership affect the classification?
- Which productivity measure is used: Is the analysis about output per hour, MFP or another outcome, and which labour and capital inputs are measured?
- How attribution is made: Does the method compare industry adoption or exposure, or rely on a model? What alternative timing and model assumptions were tested?
- How comparable the results are: Are the geography, reference period, price treatment, depreciation assumptions and accounting framework aligned?
The central distinction is between observing productivity change and attributing it. National accounts can provide the output, labour and capital framework for studying AI, and thematic accounts can make related activity more visible. But no current measure cleanly separates AI’s causal contribution from the other factors that shape productivity.
Frequently Asked Questions
Does a national productivity figure measure the productivity of an individual AI tool?
No. National accounts aggregate production and inputs across businesses and industries; they do not provide a score for the productivity of a particular model, application or worker using it. A tool-level evaluation requires evidence at a more detailed level than a national productivity statistic.
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Yes. Production can be included in existing industry and product categories even when statisticians cannot separately identify its AI component. Inclusion in GDP is not the same as a measured estimate of AI’s effect on productivity.
Does the OECD’s AI definition cover every public or policy use of the term?
No. The definition cited in the OECD’s 2025 productivity compendium is a statistical definition for measurement purposes. Policymakers and researchers may use broader or otherwise different definitions, so an estimate’s definition matters when comparing it with other work.
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