“Imperfect measures of AI productive capacity are far more informative than the implicit assumption embedded in conventional projections-that the AI sector’s productive capacity is small and slow-growing. Fiscal authorities could use such measures to stress test projections about the labor tax base; central banks could…” – “Where is AI in GDP statistics?” – May 2026 – Anton Korinek (PIIE) and Patrick McKelvey (Bank of Canada)
Macroeconomic policy is being steered by models that quietly embed an assumption about artificial intelligence: that the sector is economically small, its capacity expands slowly, and its contribution to the tax base and inflation dynamics will remain marginal for years to come.1 In parallel, AI investment, compute capacity, and quality-adjusted output have begun to grow at extraordinary rates that are largely invisible to the national accounts used by fiscal authorities and central banks.1,3 The underlying tension is not just one of measurement technique but of strategic blindness: policy frameworks calibrated to a pre-AI economy are extrapolating forward as though the production frontier itself were not being shifted by an emerging general-purpose technology.
The core issue is a widening gap between the productive capacity of the AI sector and its measured footprint in GDP statistics.1,3 When quality-adjusted AI output grows at rates that would be implausible for any traditional industry, this is not a minor statistical curiosity; it is a signal that the informational content of standard projections is degrading.3,5 Legislatures drafting medium-term budget frameworks, and central banks publishing fan charts for growth and inflation, are implicitly conditioning on a world in which the AI production function is both small in scale and smooth in its evolution. If that premise is wrong, the entire configuration of projected labour income, tax receipts, and output gaps may be systematically biased.
The factual backdrop: explosive AI output, invisible in GDP
Over the past few years, high-end estimates of AI sector activity in the United States have suggested nominal output on the order of USD 250 billion in 2025, comparable to the scheduled passenger airline industry.1,3,6 More striking than the level is the growth rate of quality-adjusted AI output. By treating AI as a coherent production sector and adjusting for improvements in model quality at fixed prices, Korinek and McKelvey estimate that quality-adjusted AI production expanded at more than 2 000 percent per year in 2024 and 2025.1,3,5 These numbers are driven by three compounding forces: rapid expansion of data-centre and compute capacity, hardware efficiency gains, and algorithmic progress that dramatically improves output per unit of hardware.1,3
National statistics offices, however, were not designed to track such activity.1,3 Conventional GDP accounting captures the AI boom only indirectly: as investment in structures and equipment, as intermediate inputs to other industries, and as service purchases by firms and consumers. Many of the most important gains show up as quality improvements or consumer surplus rather than observed market transactions. The result is that the data streams feeding macroeconomic models depict an economy with modest technology-driven productivity improvements, even as AI developers scale capacity in ways that historically have been associated with major general-purpose technological shifts.3,5
This disconnect is why the authors argue for an “AI GDP” framework and satellite accounts that explicitly measure AI production and capacity.1,3 Their empirical work shows that once AI is treated as a distinct sector with its own capital stock, intermediate inputs, and quality-adjusted output, the growth dynamics look radically different from the rest of the economy. For policymakers, the lesson is not that headline GDP should be replaced, but that relying on projections which implicitly assume a small, slow-moving AI sector is no longer tenable.1,3
Productive capacity versus realised output
The statement about “imperfect measures of AI productive capacity” turns on a crucial distinction between two concepts that macroeconomic models often conflate when technologies are stable: productive capacity and realised output. Productive capacity refers to what the AI sector could produce at current prices and technology if it were fully utilised, given existing compute stock, model architectures, and available data.1,5 Realised output is what is actually being produced and sold at a point in time, which depends on demand, regulatory constraints, infrastructure bottlenecks, and organisational readiness across the wider economy.
In conventional macroeconomics, realised output Y_t is typically modelled relative to a potential output Y_t^*, with an output gap x_t = (Y_t - Y_t^*)/Y_t^*. For most sectors, capacity grows relatively smoothly, and potential output is estimated using trend filters or production functions with modest capital-deepening and productivity terms. The implicit assumption in many forecasting frameworks is that the AI sector contributes only a small increment to aggregate Y_t^*, so that treating capacity as a smooth extrapolation of past trends is adequate.
Once AI capacity begins to grow at rates exceeding 2 000 percent in quality-adjusted terms, that assumption breaks down.1,3,5 Even if only a fraction of that capacity is deployed into new products, automation tools, and complementary capital, the path of potential output could deviate markedly from trend. A production function that includes AI capital K_t^{AI} alongside traditional physical and human capital may need to be written as something like Y_t = A_t F(K_t^{AI},K_t^H,L_t), where K_t^{AI} is growing at extraordinary rates and A_t itself partly reflects AI-driven spillovers. Ignoring this term or extrapolating it linearly is no longer a neutral simplification.
This is why even imperfect estimates of AI capacity can be more informative than implicitly assuming capacity is trivial. An imperfect measure at least anchors projections to a dynamic that recognises the scale and direction of change. In contrast, a baseline that effectively sets K_t^{AI} \approx 0 or grows it as a modest share of aggregate capital builds in a structural misrepresentation of the economy’s production frontier.1,7
From measurement gap to policy gap
If official statistics understate the growth of AI productive capacity, a policy gap follows.3,10 Fiscal and monetary authorities are tasked with stabilising the economy, financing public goods, and safeguarding financial stability in the face of shocks. Their tools and frameworks are calibrated around relationships between output, employment, inflation, and asset prices that assume gradual technological progress. When a technology arrives that can simultaneously automate cognitive tasks, create new service categories, and compress the time needed to design and deploy software, those relationships become unstable.
One channel is aggregate supply. Suppose AI diffusion accelerates between 2026 and 2030, with AI-enhanced processes raising effective labour productivity in certain sectors by large multiples. If models underestimate the expansion of productive capacity, central banks may misinterpret disinflationary pressures as evidence of weak demand rather than a positive supply shock, potentially leading to policy that is too accommodative or too tight depending on the sign of the misreading.10 A parallel risk exists on the fiscal side: if projected tax bases are derived from historical elasticities of labour income to GDP, they may fail to account for a shift in value creation from wages to AI-mediated capital income.
Financial stability is another concern. Massive investment in data centres, high-end chips, and AI-native firms is expanding the AI capital stock in ways that could resemble past investment booms.2,5,10 Without explicit measures of sectoral productive capacity and utilisation, regulators may struggle to gauge whether valuations reflect reasonable expectations of future cash flows or a speculative overshoot. Imperfect but transparent measures of AI capacity would allow stress tests to incorporate scenarios in which utilisation stalls, regulatory constraints bite, or technical progress slows, affecting both earnings and collateral values.
Stress testing the labour tax base
The quote points explicitly to one of the most immediate fiscal applications: stress testing projections for the labour tax base. Tax systems in advanced economies rely heavily on taxes on labour and consumption, with labour often providing between 40 and 60 percent of total revenue when payroll and personal income taxes are combined. If AI capacity enables rapid automation of tasks, especially in high-wage professions, the composition of tax bases could shift towards capital income and rents linked to data, intellectual property, and platform control.
Imperfect measures of AI capacity can inform scenario analysis even before comprehensive AI satellite accounts exist. Consider a simple mapping from AI capacity to potential labour displacement: if AI-driven tools can, at full deployment, perform a fraction \phi of tasks currently performed by workers in certain occupations, and if the effective AI capacity index C_t is growing at an exponential rate, then plausible stress scenarios can be constructed around the trajectories of \phi C_t relative to current labour inputs. Fiscal authorities can then simulate paths in which the labour share of income declines by, say, 5 to 15 percentage points over one or two decades, and examine the consequences for personal income tax and social insurance contributions.
Such stress tests do not require precise predictions about which jobs will be automated in which year. They require a disciplined way of linking the growth of AI capacity to enveloping ranges of labour income outcomes. Even if the underlying AI capacity index is built from noisy proxies-data-centre investment, GPU shipments, estimated algorithmic efficiency gains, and model deployment metrics-its imperfections are transparent and can be bracketed with sensitivity analysis.1,5,7 That is more informative than assuming, as many baseline projections still do, that labour’s share of income and the elasticity of taxable wages to GDP will remain approximately constant.
Central banks and AI-adjusted output gaps
Central banks face a different but related challenge. Standard New Keynesian frameworks rely on estimates of potential output and output gaps to guide interest rate policy. When AI capacity increases rapidly, the shape of potential output becomes more uncertain. If AI raises trend productivity growth, then what appears as cyclical weakness might actually be a benign reflection of the economy adjusting to a higher productivity path. Conversely, if AI-driven sectoral shifts create pockets of structural unemployment, traditional Phillips curve relationships between slack and inflation may weaken.10
Incorporating AI capacity measures into monetary policy models could take several forms. One is to extend production functions to include AI capital explicitly, with separate utilisation rates for that capital. Another is to augment the information set used for estimating potential output with AI-specific indicators, treating them as leading signals of future supply shifts. Even a rudimentary AI capacity index-constructed from investment, compute, and benchmark performance measures-could help central banks distinguish between inflation dynamics driven by demand fluctuations and those driven by AI-enabled supply changes.1,3,10
This matters for interest rate paths and communication strategies. If AI capacity is expected to unleash significant deflationary pressure in certain sectors while boosting demand for complementary skills and capital elsewhere, central banks must decide how to respond to a more uneven and possibly more volatile pattern of relative price changes. Failing to recognise AI as a material driver of potential output and productivity risks miscalibrating both policy stance and forward guidance.10
The strategic tension: ignorance versus imperfect information
The phrase “imperfect measures” acknowledges that any attempt to quantify AI productive capacity at this stage will be fraught with conceptual difficulties. Where exactly should the boundary of the AI sector be drawn-only foundation model developers, or also downstream firms building domain-specific applications? How should quality be adjusted when models differ along dimensions that are difficult to aggregate? How should non-market outputs, such as open-source models and freely available tools, be treated?1,5,7
Yet the alternative is not a world of perfect accuracy; it is a world of structurally embedded ignorance. When conventional projections assume that AI capacity is small and slow-growing, they effectively fix technology parameters that may in fact be changing rapidly. The strategic choice is between embracing a noisy, revisable set of AI-specific metrics or relying on models that treat a potentially transformative technology as a footnote. Korinek and McKelvey argue that the former is superior precisely because it allows policymaking to be conditioned on explicit assumptions that can be scrutinised, updated, and stress-tested.1,3
This is analogous to the evolution of macro-financial surveillance after the global financial crisis. Before 2008, many macro models either omitted financial frictions or represented them in highly stylised ways, effectively assuming that the financial sector’s capacity to generate credit and risk was constrained and well-behaved. Post-crisis, central banks and international institutions built macro-prudential frameworks, stress testing regimes, and detailed sectoral accounts to monitor systemic risks. These tools are imperfect by design, but they are grounded in an explicit recognition that ignoring financial capacity dynamics is unacceptable. AI capacity measurement occupies a similar conceptual role for the production side of the economy.7,10
Debates and objections
There are, however, serious debates around the measurement approach and its policy uses. One line of criticism questions whether quality-adjusted AI output growth figures in the 2 000 to 2 600 percent range are economically meaningful. Skeptics argue that adjusting for model capabilities at fixed prices may overstate the contribution to welfare and productivity if users’ willingness to pay does not rise in proportion to benchmark scores.5,7 They caution that capacity measures built on technical performance metrics risk becoming detached from the pace of real-world diffusion, organisational change, and complementary investment.
Another objection concerns the mapping from sectoral AI capacity to aggregate outcomes. Critics note that productive capacity in the AI sector does not automatically translate into realised productivity gains across the economy. Bottlenecks in regulation, trust, data access, and skills could delay deployment for years. From this perspective, the danger is not that conventional projections underestimate AI’s impact but that they might overreact to capacity signals that are only slowly realised in output and employment.5,7
These critiques underscore the need to treat AI capacity measures as inputs to scenario analysis rather than as point forecasts. Imperfect measures can still be used to generate bounded scenarios: a low-deployment path in which only a small share of capacity is applied to economically significant tasks, a central path with gradual diffusion, and high-deployment paths in which adoption accelerates non-linearly. Fiscal and monetary authorities can then design policies that are robust across these scenarios rather than optimised for a single assumed trajectory.1,7,10
Why the measurement choice matters now
The timing of this measurement agenda is not incidental. If AI capacity continues to expand at recent rates, the gap between what AI could do and what it is currently doing will grow rapidly. That capacity-realisation gap carries both upside and downside risks. On the upside, if deployment accelerates, economies could experience a wave of productivity growth that eases fiscal pressures and raises living standards. On the downside, if deployment is uneven or concentrated in ways that displace labour without adequate redistribution, the tax base could become more volatile and more reliant on capital taxation, wealth taxes, or new instruments targeted at AI-intensive firms.1,3,5
Policymakers therefore face interlocking strategic questions. How should social insurance systems and tax codes be redesigned to remain solvent if labour income becomes a less reliable base? What mix of labour, consumption, and capital taxation can sustain revenue without unduly discouraging innovation? How should central banks adjust their analytical toolkits to handle economies in which potential output and sectoral composition are shaped by a rapidly evolving AI sector? None of these questions can be addressed adequately if the AI sector is treated as a black box whose size and capacity are left unspecified.
Imperfect measures of AI productive capacity offer a way out of that impasse. They allow fiscal authorities to run stress tests in which the labour tax base is eroded under different deployment scenarios, prompting early consideration of alternative revenue sources and automatic stabilisers.1,10 They enable central banks to explore how AI-driven supply shifts could affect inflation dynamics, wage bargaining, and asset prices, informing both baseline projections and tail-risk planning.10 And they provide a common reference point for debates about regulation, competition policy, and industrial strategy, even if the underlying figures are subject to revision.
In the longer run, the development of AI-focused satellite accounts and an “AI GDP” framework is likely to transform how we think about the structure of the economy.1,3,5 What begins as a set of rough capacity indicators can evolve into a more comprehensive picture of the AI value chain, from compute infrastructure and foundation models to domain-specific applications and labour-AI complementarities. The statement that imperfect measures are more informative than implicit assumptions is therefore not only a comment on current data gaps; it is a call to rebuild the informational foundations of macroeconomic policy before the AI economy grows large enough to turn today’s measurement gap into tomorrow’s policy failure.
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. Where is AI in GDP statistics? – IDEAS/RePEc – 2026-02-02 – https://ideas.repec.org/p/iie/pbrief/pb26-7.html
3. AI may already be adding billions to the economy-without … – Fortune – 2026-06-02 – https://fortune.com/2026/06/02/ai-may-already-be-adding-hundreds-of-billions-to-the-economywithout-showing-up-in-the-data/
4. [PDF] IMPACT: The Bank of Canada’s International Model for Projecting … – https://www.bankofcanada.ca/wp-content/uploads/2020/03/tr116.pdf
5. Measuring the AI economy | PIIE – 2026-05-18 – https://www.piie.com/publications/working-papers/2026/measuring-ai-economy
6. Decline Is Still a Choice | AEI – American Enterprise Institute – https://www.aei.org/articles/decline-is-still-a-choice/
7. Measuring the AI Economy Before GDP Can See It – Zenodo – 2026-06-02 – https://zenodo.org/records/20500501
8. The Short Report: April 22, 2026 – Research Money – 2026-04-22 – https://researchmoneyinc.com/article/the-short-report-april-22-2026
9. Anton Korinek | PIIE – 2026-02-03 – https://www.piie.com/experts/senior-research-staff/anton-korinek
10. AI futures: Planning for transformative scenarios before they hit | PIIE – 2026-02-03 – https://www.piie.com/blogs/realtime-economics/2026/ai-futures-planning-transformative-scenarios-they-hit
11. [PDF] 26-7 Where Is AI in GDP Statistics? Filling the Measurment Gap – 2026-05-05 – https://www.piie.com/sites/default/files/2026-05/pb26-7.pdf
12. Machines of mind: How generative AI will power the coming … – 2023-05-07 – https://www.brookings.edu/?p=1687743&post_type=article&preview_id=1687743
