AI Dark Output: The Visible Cost of Invisible Output

May 29, 2026

During the 1980s and 90s, macroeconomic data could not detect the contribution of the emerging computer revolution. Famously, Robert Solow quipped “You can see the computer age everywhere, but in the productivity statistics.” And yet, despite the dot com boom and bust the Magnificent 7 now have a market cap 1.8x that of Europe. A similar issue is arising with AI where the macroeconomic data is not yet equipped to capture the value produced by AI while the headlines, public sentiment, and governments around the world are quick to capture the costs incurred in dollars, watts, gallons and jobs. Matt Drach had an interesting take separately from us on this.

A boring 2013 methodology revision added R&D and investment in intellectual property to GDP accounting boosting total production for the 1990s by ~$3.6T. In the official accounts it was spread evenly, so the growth rate only rose marginally, but it amounted to nearly 30% of full year 2000 GDP. The magnitude of the measurement problem from AI dwarfs prior measurement issues, we call the work AI does that national accounts can’t currently see Dark Output. Even more of the new output from AI is likely to be invisible as it is clustered in the service sector where national statistics have longstanding issues with capturing productivity growth.

Incoming Fed Chairman Kevin Warsh acknowledged as much in December 2025 “If you’re looking at the data, my view is you’re backward looking. You’re going to be late. You’re not going to realize the country is able to have non-inflationary growth faster. So you’re going to have to make a bet.” With the transition of AI growth to more active capital market funding, any measures that fail to show results from AI will be scrutinized for signs of a bubble.

Dark Output

AI output will be real before it is measurable. We can capture token spend, and we can capture jobs lost. But unless AI’s output is sold at a visible price, only token spend is captured in GDP. Normally when the price of something collapses, we can see this deflation and call the results productivity. Due to well-known difficulties in the service sector (see Appendix 1), GDP will record those as declines, and prices may even show inflation. Like the dark energy that makes up our universe, Dark Output will likely only be visible in its effects on other elements of the economy and not through direct observation. One of the most visible effects is job displacement which we are now tracking on our Dark Output Monitor.

Source: SemiAnalysis Tokenomics: Dark Output

We are at risk of having an event on the scale of the Industrial Revolution where most of the new output is invisible even as businesses spend increasingly large amounts on AI services.

Types of Dark Output

Dark output is AI-enabled economic value that exists but is not visible, or is badly distorted, in GDP, prices, labor statistics, or industry accounts. We categorize this into two buckets:

Substitution dark output is work that was previously done by humans and is now done by AI. In our Dark Output Monitor we have identified roughly $1.5T in tasks that current generation AI could substantially augment or automate.

New dark output is new work done by AI that wasn’t previously being done by humans (probably because it was too expensive to do until AI made it cheap). In the long run this is likely to be much larger than the substitution side.

In both cases, value exists despite the statistical system failing to see it. This is not a unique problem (see Appendix 1).

Source: SemiAnalysis Tokenomics: Dark Output

Substitution Dark Output

An example of substitution Dark Output is a simple legal document which in theoretical GDP should have the same inflation adjusted value to a user whether a lawyer drafts it or AI drafts it. But service sector GDP and inflation is hard to estimate (see Appendix 2), there is no ’unit’ of legal services, just lawyers’ receipts and surveys of firms for the cost of services rendered. When AI takes over the task, the receipts vanish as the cost is absorbed in tokens, and when government officials survey lawyers on the cost of services they may find that the average price has gone up, as the simplest documents are now completed by AI and not lawyers. From the perspective of GDP, the transaction has effectively vanished except for a few dollars of tokens sitting in an unrelated sector of the economy.

For Tokenomics subscribers, we track the frontier of tasks that market signals show current AI has the potential to replace. These tasks, depending on how they are performed by AI may vanish from the national accounts all together (see the Dark Output Monitor section below).

Other than housing, most services are measured in the national accounts through this sort of receipts and list prices system which backs into ‘quantity’ of tasks being done by dividing spend by price. This sort of accounting doesn’t allow for productivity gains. When the accounts record lower receipts, they will read this out as an output decline.

Source: BEA NIPA annual via Haver USNA, 2025. Healthcare wage-anchored = physicians + dental + paramedical + nursing homes (excludes hospitals, transaction-priced via DRG). Market-priced rest includes housing imputed rent and FISIM, which are imputed but not AI-relevant

A basic will as seen in the figure below has fallen in price for generations as technology changed the process of creation, but because it was gradual, the induced error was less extreme. A drop from $400 to $150 in 30 years is less than 5% a year. A drop from $150 to $0.50 in a year is more than a 99% cost decrease. One introduces bias, the other vanishes from the dataset. Legal services prices were only added to the CPI in 1987, and since then the price index is up 4.6x (as of September 2024). The price index is effectively an employment cost index because there is no accounting for the increased productivity.

Source: SemiAnalysis. Illustrative. Anchors are representative costs deflated to 2025 USD: medieval parchment scribe (Pirenne, Economic and Social History of Medieval Europe), Renaissance notary (Mokyr, The Lever of Riches), 1900 attorney from BLS historical occupational wages, 1990 solo-practitioner Martindale-Hubbell billing rate × 3 hours, 2010 LegalZoom basic product, and 2026 frontier-model API cost for ~5,000 words. The 2026 figure assumes no lawyer review — adding one hour of review (~$300) would put it near the LegalZoom band; the chart shows the ceiling on unattended drafting cost, not the all-in legal-services cost.

New Dark Output

In contrast, new Dark Output is work that did not happen before AI made it cheap enough to do. No wage bill disappears because no firm or household would have paid a human to do that work at prevailing prices. For example, when literature reviews fall from $2,000 to $2, we do not do the same number and pocket the savings, we do them before every project! Summarizing the last six months of emails on a theme in your inbox is useful. Running an academic literature review before an interview is useful. Both can create real value, but neither leaves a clean economic trace beyond the tokens, API calls, cloud spend, or subscription that made the task cheap enough to run.

There are anecdotal signs that a large fraction of current token spend is for new work that wasn’t previously paid for rather than replacing existing work. But the exact magnitude is opaque as it sits behind the anonymizing curtain of tokens. Identifying if a specific AI task is creating value and how much would likely be difficult even if you had the full conversation trace, as it is the national accounts will at best see AI revenue.

Captured AI Output

A final category of AI output is work that was previously done by humans and now is done by AI, but that can still charge the same amount as before. This captured AI will only exist where companies have genuine market power, and can protect prices in the face of declining costs of production. Consider two scenarios, first a firm that used to buy a $10,000 HR service from an outside provider now buys that HR service for $10,000 from an AI HR provider. In that case the output still is captured in national accounts and all that disappeared was the wages and workers. In the second version that $10,000 service is now done internally for $10 of tokens. In that scenario GDP has declined by $9,990 despite the same work being done.

Why Services aren’t like Goods

Manufacturing automation gave statisticians something to count. If machinists got better at making screws, the factory would report they made more screws, at lower costs, or better margins. Real GDP could rise because it was based on the quantity of output. So as the price of screws fell by 99+% over the past 6 centuries, we can count that the quantity of screws also went up on the order of 10 billion times. Real GDP correctly captures this as growth and productivity

Source: Real price of a single common iron or steel screw in 2025 USD with order-of-magnitude estimates of global production. Pre-1900 figures are reconstructed from craft-shop prices, journeyman wage rates, and industrial-revolution case histories (Adam Smith, Wealth of Nations; Robert Allen, The British Industrial Revolution; Mitchell, British Historical Statistics); modern endpoint via Home Depot / McMaster-Carr retail. CPI deflation via MeasuringWorth.com.

We lack a functional vocabulary for units of services, and mental work. As useful as it would be, there is no measure of ‘mind power’ that does for AI what horsepower did for the Industrial Revolution. Horsepower gave people a way to compare machine output with animal and human labor. Tokens do not do that. A million tokens can produce junk, a useful email summary, a legal document, or a decision that changes a company’s strategy. The economic value depends on the output, not the token count.

Finger Prints of Dark Output

A common observation in AI commentary is that junior staff are being displaced from routine work first. The corollary is that average wages in exposed occupations can rise because the lower-paid workers leave the sample. The cheapest workers disappear from the data. No one got a raise, and yet wages rose.

Employment in the most AI exposed sectors of the economy is falling relative to the broader economy. Yet those same underperforming segments are showing relative wage increases.

Source: SemiAnalysis Tokenomics: Dark Output

Source: SemiAnalysis Tokenomics: Dark Output

This mismatch between employment and wages is one fingerprint of AI displacement we track in our Dark Output dashboard. This is not a direct measurement of dark output, but the sort of odd measurement artifact that dark output would create as it became more prevalent.

An initial sign of new dark output is the heavy prevalence of Token usage in segments of the economy that are not showing signs of rapid labor deterioration. In Anthropic’s Economic Index from March 2026 they show 37% of tokens are being used in computers and mathematics and yet the contribution to GDP from investment in software has not broken from its pre-AI trend and wasn’t even at an all time high.

Source: SemiAnalysis Task Benchmark (T4+ verification, April 2026) overlaid on the Anthropic Economic Index (September 2025). DWA classification via O*NET; usage shares from Anthropic AEI public data.

Why We Use Market Signals, Not Benchmarks

Benchmarks answer the wrong question, and they answer it late. Expert evaluations ask whether AI can satisfy an evaluator under test conditions, often an evaluator who expects expert work. They are expensive, slow, subjective, and backward-looking because expert time is scarce. Labor augmentation and displacement does not require AI to beat the best lawyer, analyst, or engineer. It requires AI to be good enough, cheap enough, and reliable enough to aid or replace the worker who would have do

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