“The AI economy in the United States has been growing at an unprecedented rate, but this extraordinary growth is largely invisible in conventional GDP statistics. Treating the AI sector as a coherent economic entity yields preliminary estimates of nominal AI GDP at approximately $250 billion in 2025, growing at roughly 2 600 percent per year in quality-adjusted real terms.” – “Where is AI in GDP statistics?” – May 2026 – Anton Korinek (PIIE) and Patrick McKelvey (Bank of Canada)
Economic statistics are struggling to keep pace with a technology whose productive capacity is compounding across hardware, data centres and algorithms faster than the measurement systems designed in the mid-20th century can register.1 The result is a widening gulf between what is happening inside the AI ecosystem and what appears in national accounts, complicating debates about productivity, inequality and policy at exactly the moment when artificial intelligence is beginning to reshape production methods and business models.4
The invisible boom behind a modest spending line
On the surface, current US spending on AI looks like a sizeable but manageable line item in GDP: on one influential estimate, nominal AI compute outlays amount to about USD 250 billion a year by 2025, covering both inference and model training activities.4,2 That figure encompasses the purchase of specialised chips, cloud compute services, and associated infrastructure that is straightforwardly counted as investment or intermediate consumption in the national accounts.4 In conventional terms, this is the visible part of the AI economy: money changing hands for hardware, data centre capacity and access to models, all recorded using existing categories.
Yet behind that nominal spending path lies an extraordinary explosion in effective AI capacity. When the same researchers treat AI as a coherent production sector and apply quality adjustments for both hardware improvements and algorithmic progress, they find that real AI output is growing not by 140 percent a year but by more than 2 000 percent annually in 2024 and 2025.4,5 In other words, each year’s spending is buying orders of magnitude more capability than the previous year’s, even though the nominal cash flows entering GDP aggregates rise only modestly by comparison.5 The boom is hidden not because it is economically trivial, but because the price-quality relationship is collapsing too quickly for unadjusted statistics to capture.
Three compounding engines of AI output
The backstory to such eye-catching growth rates lies in three distinct but reinforcing processes that reshape what a given dollar of AI spending delivers.4 The first is the physical build-out of data centre capacity. AI-optimised facilities packed with accelerator chips and high-bandwidth networking are being deployed at an accelerating pace, so the raw compute available for training and running models is growing well above 200 percent a year.5,6 This expansion is visible in investment data, but only in a blunt way: the accounts see larger structures and more equipment, not the combinatorial increase in tasks those resources can perform.
The second process is hardware efficiency. Successive generations of AI chips deliver substantially more floating-point operations per second for each dollar of cost and each unit of energy.2,5 Measured in H100-equivalent units, US AI computing capacity is estimated to grow at more than 200 percent per year, significantly outpacing nominal spending growth because each chip class is more powerful than its predecessor at roughly comparable prices.2,5 This dynamic echoes the semiconductor industry’s long history of rapid quality improvements that kept its GDP share modest even as real output exploded, but AI accelerators are doing so in a context where demand for compute is sky-high and model sizes are scaling aggressively.
The third, and most potent, engine is algorithmic progress. Advances in model architectures, training techniques, optimisation and data curation mean that the amount of compute required to achieve a given performance benchmark has been falling sharply, in some estimates by around two-thirds per year.2 Put differently, a fixed quantity of chips running for a given period now delivers far more useful task performance than it did a few years ago. When quality adjustments account simultaneously for (a) more data centre capacity, (b) more powerful hardware, and (c) more efficient algorithms, the implied growth in effective AI output jumps from high triple-digit percentages to the 2 000-2 600 percent range cited in recent work.4,5
Formally, one can think of quality-adjusted AI output Q_t as the product of three components: physical compute capacity C_t, hardware efficiency h_t and algorithmic efficiency a_t. A simple multiplicative representation is Q_t = C_t \times h_t \times a_t. If each term grows at an annual rate g_C, g_h and g_a, then the growth rate of Q_t is approximately g_Q \approx g_C + g_h + g_a for moderate rates; with the extreme compounding observed in AI, the exact expression g_Q = (1+g_C)(1+g_h)(1+g_a) - 1 yields multi-thousand-percent annual increases when all three drivers are simultaneously large.
Why GDP barely moves
National accounts, by design, focus on market transactions valued at current prices, and only selectively adjust for quality improvements.2 When the price of a service falls as its quality rises, the accounts record a combination of higher quantities and lower prices, but the frameworks and data pipelines required to track both accurately for a new technology often arrive late. For AI, the issue is pronounced because per-unit prices for a given capability are dropping almost as fast as underlying capacity is rising.4 If API access to a model that can perform a given benchmark task becomes ten times cheaper in a year, and enterprises spend only modestly more on AI in total, then nominal AI revenues will show only a small increase, even though the effective quantity of AI services they purchase has surged.
This pattern is familiar from historical episodes. The semiconductor sector experienced decades of rapid performance improvements, yet its share of GDP remained modest because each new generation of chips delivered more performance for similar or lower nominal prices.2 Hedonic pricing methods allow statistical agencies to adjust for this by re-expressing prices in terms of constant performance metrics, but applying such methods requires stable benchmarks and extensive data that typically emerge only after technologies have matured. The AI wave is arriving too quickly for existing statistical routines to keep up, leaving much of the quality-adjusted output uncounted in official real GDP figures.4
Defining an “AI GDP” and its boundary
Korinek and McKelvey therefore propose treating AI as a distinct economic entity with its own satellite accounts, yielding an “AI GDP” that complements, rather than replaces, standard aggregates.4,3 The idea is to delineate the production boundary of AI: which activities belong in the AI sector, how their output should be measured, and how to avoid double-counting when AI is embedded in other goods and services. They focus on the production side, aggregating spending on compute for model training and inference, as well as AI-related research and development, and then applying quality adjustments based on API pricing and estimates of algorithmic progress.5,6
This boundary is necessarily provisional. Including only core compute and API-based access captures the narrow AI industry, but much of the economic value from AI will emerge in applying models to domains like healthcare, education, logistics and creative industries. If those downstream uses are counted as ordinary sectoral output without explicit attribution to AI, then AI’s contribution to growth will remain partly hidden even if the upstream AI GDP is measured perfectly. Conversely, if the AI boundary is drawn too broadly, there is a risk of attributing to AI productivity improvements that are jointly driven by complementary investments in human capital, organisational change or non-AI software.
One emerging response in the measurement literature is to distinguish between AI’s productive capacity and its realised utilisation. Capacity can be proxied by compute resources and model capabilities, while utilisation depends on demand, adoption and complementary changes in firms’ processes. This motivates a conceptual gap between potential AI GDP, based on what the technology could deliver if fully deployed, and actual AI-enabled output that shows up in sectoral productivity data.1 The unusually high quality-adjusted growth rates identified in the AI sector look more like capacity expansion than like realised welfare gains; the satellite account framework is a way to track this capacity before it fully diffuses through the economy.
The strategic tension: capacity versus productivity
The divergence between internal AI growth and GDP statistics matters because it shapes how policymakers, firms and the public interpret the technology’s macroeconomic role. On one view, the rapid expansion of AI capacity with limited reflection in aggregate productivity suggests a familiar pattern of lagging diffusion: general-purpose technologies often require time-consuming organisational and human-capital investments before they translate into economy-wide gains. The classic comparison is with electrification, where factories needed decades to reorganise around distributed motors instead of central shafts, during which time headline productivity growth remained subdued.
On another view, the mismatch raises questions about whether current measurement practices are still adequate. If AI is enabling substantial quality improvements in services that are poorly captured by prices or quantities, such as personalised education or medical diagnostics, then real welfare may already be rising faster than official statistics suggest. However, because GDP is anchored to transactions, not to subjective well-being or consumer surplus, an AI-augmented service that remains priced similarly to its predecessor will not add much to measured output even if users derive more value from it.2 This reinforces the importance of clarity about what GDP can and cannot represent.
Strategically, governments face a tension between under-reacting to AI because it is invisible in official numbers and over-hyping it based on internal metrics from the AI industry. An AI sector that is expanding at 2 600 percent per year in quality-adjusted terms looks like a revolution from the perspective of data centre operators and model developers.4,5 From the perspective of macroeconomic analysts focused on trend productivity growth of perhaps 1-2 percent per year, the effects so far look modest. Calibrating regulation, infrastructure policy and workforce programmes in this context is difficult: the technology’s future impact is potentially enormous, but the statistical evidence of current gains is thin.
Debates and objections
The very concept of an AI GDP has sparked several lines of debate. One concern is that quality-adjusted growth rates on the order of thousands of percent may be more a reflection of the chosen metrics than of genuine economic output. Measuring AI in terms of benchmark tasks or API performance might overstate economically relevant progress if those benchmarks do not map cleanly to productivity in real-world workflows. Critics argue that the ability of models to score higher on academic tests or synthetic tasks does not automatically translate into major cost savings or revenue gains for firms.
Another line of criticism questions whether focusing on production-side compute spending misses the demand side of the story. GDP aggregates are meant to reconcile production with income and expenditure; a satellite account that captures only the production capacity of AI might be analytically useful but risks being misinterpreted as a measure of realised welfare. The authors themselves are cautious on this point, emphasising that their quality-adjusted AI GDP should be read as a signal of productive capacity rather than as a replacement for standard welfare concepts.1 From this perspective, the headline growth rates highlight how quickly the technological frontier is moving, not how much better off households currently are.
A third objection is practical. Statistical agencies operate under resource constraints and must prioritise improvements that deliver the greatest benefit for overall data quality. Some observers worry that building specialised AI satellite accounts could divert attention from more pressing tasks, such as better measuring services, intangibles and household production. In response, proponents of AI-focused measurement argue that because AI is highly input-intensive and potentially general-purpose, an early investment in dedicated tracking can prevent larger measurement problems later, particularly if AI-enabled services blur sector boundaries and reconfigure value chains.4
Why it matters for policy and strategy
Despite the conceptual disputes, the attempt to quantify an AI GDP has several concrete implications. For macroeconomic policy, understanding the scale and trajectory of AI investment and capacity is essential for forecasting productivity, inflation and labour market dynamics. If AI capacity is growing far faster than utilisation, there may be a period in which capital deepening outpaces labour adaptation, affecting wage structures and sectoral employment even before aggregate productivity accelerates. Conversely, if AI adoption triggers rapid efficiency gains in certain tasks, it could exert disinflationary pressures in specific service categories, complicating monetary policy calibration.
For innovation and industrial policy, AI satellite accounts can inform decisions about infrastructure, regulation and public R&D support. Knowing whether AI investment is concentrated in a handful of large firms or spread across a wider ecosystem affects concerns about competition and resilience. Tracking the balance between training expenditure and inference-related spending sheds light on whether the frontier is shifting primarily through ever-larger models or through more efficient deployment of existing capabilities. These are questions that conventional sectoral classifications and investment data are not well suited to answer.
For firms, the backstory behind the AI GDP figures highlights the importance of complementarity. The mere existence of rapidly expanding AI capacity does not automatically translate into competitive advantage; what matters is the ability to integrate models into production processes, redesign workflows and manage data effectively. Businesses that treat AI as a drop-in technology may find that the gains visible in benchmark tests do not materialise in their own operations. Those that invest in organisational learning, experimentation and human-machine collaboration are more likely to convert the sector’s quality-adjusted output growth into genuine productivity improvements.
Towards a more nuanced statistical architecture
Ultimately, the story behind the headline estimate of a 250-billion-dollar AI economy growing at thousands of percent per year is about the need for a richer statistical architecture.4,5 Traditional GDP will remain the workhorse indicator for macroeconomic analysis, but its design assumptions are being stretched by technologies that deliver rapid quality improvements at falling prices and that diffuse across sectors in ways that blur the boundaries between producers and users. Satellite accounts for AI, structured around clear production boundaries and transparent quality-adjustment methods, offer a way to track the technology’s evolution without over-claiming about its current welfare impact.
The challenge for researchers and policymakers is to keep the conceptual distinctions clear. AI capacity is not the same as AI usage; AI usage is not the same as productivity; and productivity is not the same as welfare. Yet all four are linked, and their trajectories over the coming decade will shape living standards, industrial structures and geopolitical balances. An analytical framework that isolates AI’s contribution to production, while acknowledging the limits of current data and the uncertainties around mapping benchmarks to economic value, is a crucial step in making sense of a transformation that standard GDP statistics barely register today.1,4
References
1. Where is AI in GDP statistics? | PIIE – 2026-05-18 – https://www.piie.com/publications/policy-briefs/2026/where-ai-gdp-statistics
2. Measuring the AI Economy Before GDP Can See It – Zenodo – 2026-06-01 – https://zenodo.org/records/20500501
3. Is GDP Failing to Capture AI? – Conversable Economist – 2026-06-04 – https://conversableeconomist.com/2026/06/04/is-gdp-failing-to-capture-ai/
4. Where is AI in GDP statistics? – IDEAS/RePEc – 2026-02-02 – https://ideas.repec.org/p/iie/pbrief/pb26-7.html
5. Measuring the AI economy | PIIE – 2026-05-18 – https://www.piie.com/publications/working-papers/2026/measuring-ai-economy
6. Measuring the AI economy – IDEAS/RePEc – 2026-02-02 – https://ideas.repec.org/p/iie/wpaper/wp26-9.html
7. New Research Finds AI Sector Growing Over 2000% Annually Yet … – 2026-05-25 – https://digg.com/ai/x2jl06ob
8. Measuring the AI Economy by Anton Korinek, Patrick McKelvey – 2026-05-26 – https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6813982
