Quarterly spending on data centres, servers and chips by the five biggest AI builders.
USD, purchases of property and equipment
Quarterly cash spent on property and equipment (data centres, servers, chips, land) by Microsoft, Alphabet, Amazon, Meta and Oracle, from their SEC filings, aligned to calendar quarters. Leased data centres and servers are not included (large for Microsoft and Oracle), so true spending is higher, and not all of it is AI.
Sequoia partner David Cahn's estimate of the revenue the buildout needs, against OpenAI's and Anthropic's annualised revenue.
USD per year, log scale
Two things on one log scale. First, David Cahn's running estimate of the annual revenue AI end-products must generate to justify the data-centre buildout: Nvidia's data-centre revenue run-rate doubled (for the rest of the data-centre cost) and doubled again (for a 50% margin), or, in 2026, double the hyperscalers' data-centre capex. Second, the annualised revenue of OpenAI and Anthropic as reported by the companies or the press, collected by Epoch AI, alone and combined. The two labs count revenue differently: Anthropic books sales through AWS, Google and Microsoft clouds at the full price the customer pays, OpenAI only its own share, so the gap between them looks bigger than it is (counted Anthropic's way, OpenAI was about $70B at end-September 2026, not $50B). Run-rates are mostly press reports, not audited figures, and the combined line pairs each lab's latest figure with the other's most recent one.
The biggest spenders carry lab revenue. A lower high in the top 1% is what a stall looks like.
USD per employee per month · % of non-payroll spend
From Ramp's card and bill-pay data (85,000+ US businesses), among firms that pay for AI: monthly AI spend per employee for the median firm, and the median within the top 10% and top 1% of spenders. Plus AI's share of all non-payroll business spend on Ramp. Ramp's customers lean toward tech-forward firms, and free tools or personal accounts leave no trace, so this is not the whole economy.
Above 100%, capex alone eats all the cash the business makes, before anything goes to shareholders.
%, trailing four quarters, five hyperscalers combined
Combined capital expenditure of Microsoft, Alphabet, Amazon, Meta and Oracle divided by their combined operating cash flow, over the trailing four quarters. Leased capacity is not counted as capex, so the ratio is understated; Oracle is far more stretched than the combined figure shows.
Each quarter's capex, split into what cash flow covered after paying shareholders, what was borrowed, and what came out of savings.
USD per quarter, five hyperscalers, each company computed separately and summed
The capital spending of Microsoft, Alphabet, Amazon, Meta and Oracle, split by where the money came from: operating cash flow left after dividends and buybacks, net new borrowing (bonds, loans and commercial paper issued, minus repayments), and the remainder drawn from cash and investments. The split rests on a rule (cash flow pays shareholders first, then capex), and the biggest new channel, off-balance-sheet vehicles and leases such as Meta's $27B Hyperion deal, is not included at all.
Method
Per company and calendar quarter from SEC XBRL cash-flow statements: retained = operating cash flow − dividends − buybacks; net borrowing = financing cash flow + dividends + buybacks. Capex is covered by retained (if positive), then net borrowing (if positive), and the rest is "From reserves". The three parts are summed across the five companies. A quarter appears once all five have reported.
Global startup funding to AI companies, and AI's share of all venture funding.
USD billions per month · % of global venture funding
Crunchbase's monthly tally of venture funding worldwide raised by companies it classifies as AI, in USD billions, and that amount as a share of all venture funding that month. Single mega-rounds dominate: February 2026 is mostly OpenAI's $110B round (later closed at $122B) and Anthropic's $30B. Crunchbase's "AI" label is broad, and recent months are revised upward as late rounds are reported (February was first reported at $171B).
Share of all startup funding worldwide that goes to AI companies.
% of global venture funding per quarter (Crunchbase)
The share of global venture-capital dollars going to AI companies each quarter, according to Crunchbase. Collected by hand from published reports, so points are irregular, and some come from press coverage rather than the original report.
Longer assumed lives mean lower depreciation, and higher reported profit, today.
years of depreciable life
The useful life each hyperscaler assumes when depreciating its servers and network gear, from the accounting notes of its annual report. Collected by hand from published reports, so points are irregular, and some come from press coverage rather than the original report.
Share of each YC batch that describes itself as AI. Where the next generation of founders is placing its bets.
% of companies in each batch
For each Y Combinator batch, the share of companies whose one-line pitch mentions AI, agents, LLMs, GPT, copilots or machine learning, and the share that do so or carry an AI tag in YC's directory. The CSV also has, per batch, the number of companies and how many are marked inactive, split by AI and non-AI. Recent batches keep growing as more companies launch, and tags are filled in late, so the pitch-text line is the steadier one.
Share of each year's Y Combinator startups with a .ai web address, or "AI" in the company name.
% of YC companies in that year's batches
For all Y Combinator companies from each year's batches, the share whose website is on the .ai domain (the country code of Anguilla, which now earns a large part of its government revenue from AI startups registering it), and the share with "AI" in the company name itself. Websites are as listed today, not at founding, and this year's later batches are still being added.
A counter that resets every time an AI lab raises $10 billion or more in one round.
days since the most recent closed round with ≥ $10B of equity
For each day, the number of days since the most recent funding round of $10 billion or more of equity, closed by an AI lab, in Epoch AI's funding-round dataset (OpenAI, Anthropic, xAI, DeepSeek, Mistral, Zhipu, MiniMax). Rounds are counted when closed, and big rounds often arrive in tranches that may not all be paid in.
How far spending runs ahead of the costs that will hit profits later.
capex ÷ depreciation, trailing four quarters
For each hyperscaler: capital spending over the trailing twelve months divided by depreciation over the same period (fiscal-year totals where quarterly figures aren't reported). "Five-company" combines Microsoft, Alphabet, Amazon, Meta and Oracle. Changes to assumed server lifetimes cause one-off jumps, and leased assets are left out.
Revenue customers have contracted for but not yet received. Much of the recent jump is a handful of AI labs.
USD, remaining performance obligations at quarter end
Remaining performance obligations (RPO): the value of signed customer contracts not yet recognised as revenue, as reported in each company's 10-Q and 10-K. Each company defines backlog differently (Microsoft's covers all commercial contracts, Alphabet's is mostly cloud), Amazon does not report it in a comparable way, and contracts can be cancellable.
IT equipment and software investment's share of US growth; dashed line is total GDP growth.
percentage points of annualised real GDP growth
The contribution of business investment in information-processing equipment (computers, servers, networking) and software to annualised US real GDP growth each quarter, approximated as last quarter's share of GDP times the category's real growth rate. The light line marks total GDP growth. There is no "AI" category in the GDP accounts: this includes ordinary IT spending, and much of the equipment is imported, so the true contribution to US growth is smaller.
Method
contribution_t ≈ (nominal_{t−1} / nominal GDP_{t−1}) × ((real_t / real_{t−1})^4 − 1) × 100, using BEA NIPA series via FRED, as in the St. Louis Fed's "Tracking AI's contribution to GDP growth".
Business spending on computers, networking and software, measured against its dot-com peak.
% of US nominal GDP, quarterly, seasonally adjusted annual rates
US private fixed investment in information-processing equipment (computers, servers, communication equipment) plus software, divided by nominal GDP. This includes ordinary PCs, phones and business software, while data-centre buildings are counted elsewhere.
Nvidia: how long customers take to pay, and unsold stock
Customers paying later and stock piling up is how the 2001 telecom bust began.
days · USD billions
Three numbers from Nvidia's quarterly filings. Days sales outstanding: receivables divided by quarterly revenue, times 91 days, i.e. how long the average sale stays unpaid. Inventory in USD billions. Days of inventory: inventory divided by quarterly cost of revenue, times 91. Inventory rises legitimately ahead of product launches, and receivables jump when sales land late in a quarter, so one quarter proves little.
Equity Nvidia holds in unlisted firms, many of them its own customers, and how much it has written them up.
USD, end of fiscal quarter
Two numbers from Nvidia's balance-sheet notes. Carrying value: equity securities without a readily determinable fair value, i.e. stakes in private companies such as OpenAI, xAI and neoclouds, valued at cost plus adjustments. Cumulative write-ups: the total upward price adjustments Nvidia has booked on those stakes so far. Guarantees and capacity backstops Nvidia has given customers are not included.
AI memory eats ordinary DRAM capacity. A sustained fall would mean the shortage is over.
USD per chip, session average
The daily session-average spot price for mainstream DRAM chips (DDR5 16Gb, DDR4 16Gb and 8Gb) on DRAMeXchange, the reference market run by TrendForce. Spot is a thin market (most memory sells on quarterly contracts), and points before daily collection began are monthly samples.
Price increases are slowing fast. A fall below zero has marked past memory-cycle peaks.
% change quarter on quarter, conventional DRAM contracts (TrendForce)
Quarterly change in contract prices for conventional DRAM as reported by TrendForce: the prices big buyers actually pay, set quarter by quarter (spot prices are the other DRAM card). The latest quarter is a TrendForce forecast, not an actual. Collected by hand from published reports, so points are irregular, and some come from press coverage rather than the original report.
The earliest hard number on chip demand each month.
USD billions per month
South Korea's monthly semiconductor exports in US dollars (mostly memory from Samsung and SK hynix), as published by the Ministry of Trade, Industry and Energy. Dollar values include price effects, so the 2026 memory price spike inflates them. Collected by hand from published reports, so points are irregular, and some come from press coverage rather than the original report.
The whole chip industry's monthly revenue, three-month average.
USD billions per month (3-month moving average)
Worldwide semiconductor sales per month as reported by the Semiconductor Industry Association, compiled by World Semiconductor Trade Statistics (WSTS) as a three-month moving average. Dollar sales include price effects, so a memory price spike inflates them.
US chip production, capacity in use, and what chipmakers charge.
output index · % of capacity · producer price index
Federal Reserve industrial production for semiconductors and other electronic components (NAICS 3344), the share of the industry's capacity in use, and the producer price index for semiconductors (what chipmakers charge, quality-adjusted). US production only: most AI chips are made in Taiwan, so this tracks the broader electronics cycle, not Nvidia's supply.
Monthly construction spending. Data centres are the physical part of the bet that cannot be resold.
USD, seasonally adjusted annual rate
Value of private construction put in place each month for four categories in the Census Bureau's construction-spending survey: data centres, factories for computers, chips and electrical equipment, electric power, and general office buildings. Seasonally adjusted, expressed as an annual rate. Census revises recent months heavily, so the last two or three points will move, and only buildings are counted, not the servers inside.
What it costs to guarantee enough power plants in the largest US grid, where data centres cluster.
USD per megawatt-day, by delivery year
The clearing price of PJM's annual capacity auction (Base Residual Auction) for each delivery year starting June 1. PJM runs the grid for 13 US states including Northern Virginia, the world's largest data-centre market. Collected by hand from published reports, so points are irregular, and some come from press coverage rather than the original report.
Growth in power demand over the past 12 months. Northern Virginia hosts the world's largest cluster of data centres.
% change, trailing 12-month average demand vs the 12 months before
Hourly electricity demand reported to the US Energy Information Administration (form EIA-930), averaged per month and summed over sub-regions: the Dominion zone of PJM (northern and central Virginia, including "Data Center Alley" in Loudoun County), the rest of PJM (13 states from Illinois to New Jersey), and ERCOT (most of Texas). The line is the growth of the 12-month average over the 12 months before. A very hot summer or cold winter still shows through, and EIA data for recent months are preliminary.
Method
Monthly mean of hourly sub-region demand, summed per series; growth = mean(last 12 months) ÷ mean(12 months before) − 1. Monthly averages in GW are in the CSV.
Data updated 3 d ago, checked daily. Last successful fetch never. Source failing, showing last good data. Last error: [grid-demand] downloading 2026 Jul_Dec
RuntimeException: fetch returned no rows @ run-signal.php:28
Turbines for new power plants are sold out years ahead.
GW of backlog plus slot reservations
GE Vernova's gas-turbine backlog plus slot reservations, in gigawatts, as reported in its quarterly results. Collected by hand from published reports, so points are irregular, and some come from press coverage rather than the original report.
The global copper price and the US producer price index for electric power generation, transmission and distribution. Neither price is AI-specific: copper also follows China, EVs and the dollar, and electricity prices follow gas and weather.
Vacancy at record lows: everything built is taken. Overbuilding would show here first.
% of capacity vacant (CBRE)
The share of data-centre capacity standing vacant in North America's primary markets overall and in selected markets, from CBRE's semiannual North America Data Center Trends. Collected by hand from published reports, so points are irregular, and some come from press coverage rather than the original report.
Less than half the capacity announced for 2026 is under construction.
gigawatts
Data-centre capacity announced for completion in 2026 in the US versus the part actually under construction (Sightline Climate), and capacity under construction in North America's primary markets (CBRE). Announced and under-construction figures come from different trackers with different scopes. Collected by hand from published reports, so points are irregular, and some come from press coverage rather than the original report.
Public commits signed by AI coding agents. Coding is where AI does the most paid work. Log scale.
commits per month (GitHub search estimate), log scale
Commits on public GitHub repositories whose message carries a co-author trailer added by an AI coding agent: "Co-Authored-By: Claude" (Claude Code), "Co-authored-by: Copilot" (GitHub Copilot agent) and "Co-authored-by: Cursor Agent". Counted per month with GitHub's commit search. Public repositories only, and users can remove the co-author line, so this undercounts; GitHub's totals for large queries are estimates.
Real weekly usage on the largest public model router, split by whose models served it.
tokens per week (prompt + completion)
Tokens processed per week on OpenRouter, the largest public multi-provider model router, split by the company whose models served them. The five largest model makers in the latest complete week are shown; everything else is "Others". The stack height is all OpenRouter traffic. OpenRouter leans toward developers, coding agents and cheap open-weight models; most enterprise traffic goes straight to the labs or the clouds.
Real daily usage on the largest public model router, top models by tokens.
tokens per day (prompt + completion)
Daily tokens (prompt + completion) for the 20 most-used models on OpenRouter, the largest public multi-provider LLM router, as published on its rankings page. The headline number is the sum across those 20. OpenRouter leans toward developers and cheap open-weight models, and only the top 20 are published.
If free-to-run models take the volume, the labs' pricing power shrinks.
% of weekly OpenRouter tokens served by open-weight models
The share of weekly tokens on OpenRouter served by open-weight models (whose weights anyone can download and run, e.g. DeepSeek, Qwen, Llama, GLM) rather than closed models (OpenAI, Anthropic, Google's Gemini). OpenRouter over-represents open-weight and price-sensitive traffic compared with the market as a whole.
Every app that calls a model API installs one of these. Log scale.
npm downloads per week, log scale
Weekly downloads from the npm registry of the official JavaScript/TypeScript SDKs for OpenAI (openai) and Anthropic (@anthropic-ai/sdk), Vercel's provider-neutral AI SDK (ai), and the Model Context Protocol SDK (@modelcontextprotocol/sdk). Automated installs inflate the numbers, so the trend matters more than the level; JavaScript only.
Pull requests opened by agents themselves, not by humans with AI help.
pull requests per month on public GitHub
Pull requests on public GitHub repositories whose author is a coding-agent app: GitHub Copilot's coding agent, Devin, Cursor, Claude and Google Jules. These are PRs the agent opened itself, not ones a human opened with AI help. Public repositories only, and agents that open pull requests from the user's own account are not seen. Copilot's fall from April 2026 coincides with GitHub moving Copilot to usage-based billing (live 1 June); the agent's commits fell just as much, so it is a real drop, not a counting change.
Incidents posted on the OpenAI and Anthropic status pages per week. A rough gauge of demand running ahead of capacity.
incidents per week (Monday to Sunday)
The number of incidents (outages, elevated error rates, degraded performance) each company posted on its public status page, counted by the week the incident began. The current week is left out until it is over. Each company decides what counts as an incident, so the two are not comparable, and a long outage counts the same as a short one.
Security fallout: flaws exploited, exploits published
Vulnerabilities attackers are actually using, and public exploit code, per month.
per month: vulnerabilities added to CISA's exploited list · GitHub repositories named after a CVE
Two monthly counts. First, vulnerabilities added to the US Cybersecurity and Infrastructure Security Agency's Known Exploited Vulnerabilities catalogue: flaws with evidence of attackers using them in the wild. Second, new public GitHub repositories with "CVE" in their name, which are mostly proof-of-concept exploits and scanners for specific vulnerabilities. CISA lists flaws when it confirms exploitation, often months later, and many GitHub "CVE" repositories are scanners, write-ups or fakes.
Security flaws published per month, with and without the four issuers whose output jumped most.
CVE records published per month (NVD)
The number of CVE records (publicly catalogued software vulnerabilities) published each month in the US National Vulnerability Database, in total, and after removing those assigned by four numbering authorities: VulnCheck, GitHub, Microsoft and the Linux kernel project. The CSV has each issuer's monthly count. A CVE is a record, not a measure of severity, and recent months change as NVD processes records.
Method
NVD CVE API 2.0, totalResults for pubStartDate–pubEndDate per calendar month, with sourceIdentifier filters for the four issuers.
Data updated 3 d ago, checked daily. Last successful fetch never. Source failing, showing last good data. Last error: timed out after 240s
[cve-surge] 2022-01-01: 2207
[cve-surge] 2022-02-01: 2017
[cve-surge] 2022-03-01: 2092
[cve-surge] 2022-04-01: 2063
[cve-surge] 2022-05-01:
Published vulnerabilities that mention prompt injection or the Model Context Protocol, per month.
CVE records per month whose description mentions the phrase (NVD)
CVE records published each month in the US National Vulnerability Database whose description contains "prompt injection" (tricking an AI model with hidden instructions) or "model context protocol" (MCP, the standard that connects AI agents to tools and data). Numbers are small, so a few reports from one researcher can move a month, and keyword matching misses flaws described in other words.
Builder energy: new repositories tagged with AI topics.
public repositories created per month, by topic tag
New public repositories created each month that carry the topic tags llm, ai-agents, mcp or claude-code. Only repositories whose owners add topic tags are counted, and spam inflates the totals.
How many models currently listed on OpenRouter were first added in each month, counting the base model only (no :free or :nitro variants). Delisted models are missing, so earlier months are undercounted.
Share of US businesses with paid AI spend, overall and by lab.
% of businesses on Ramp
The share of US businesses on Ramp's corporate card and bill-pay platform with a paid subscription or API spend on AI products in the month: overall, and for OpenAI and Anthropic separately. Ramp's customers lean toward tech-forward, venture-backed firms, so the level overstates the economy (the Census survey finds about 20%).
What AI replaced: programmers stopped asking each other.
new questions per month
The number of new questions posted on Stack Overflow each month (Stack Exchange API). Moderation changes and the site's own long decline also play a part, not only AI.
Rank by consumer revenue among all iPhone apps; 1 is the top.
rank in the top-grossing chart (1 = highest revenue)
The daily position of the main AI assistant apps in Apple's US top-grossing chart (by in-app revenue) among the top 100 apps. The free-download chart is collected too and is in the CSV. iPhone and US only, ranks rather than revenue, and web subscriptions (most of ChatGPT's and Claude's revenue) are not counted.
% of all job postings on Indeed, 7-day average, weekly
The share of job postings on Indeed that mention AI or generative-AI terms in the title or description, in four countries. "AI" in a job ad can mean building AI, using AI tools or boilerplate.
Microsoft AI Economy Institute's estimate of the share of the world's working-age population that used a generative-AI product in the period. Collected by hand from published reports, so points are irregular, and some come from press coverage rather than the original report.
Headline numbers from the main recurring surveys: Gallup (US workers), the Census Bureau's Business Trends and Outlook Survey (US firms) and PwC's Global CEO Survey. Different surveys ask different people different questions, so the numbers cannot be compared directly. Collected by hand from published reports, so points are irregular, and some come from press coverage rather than the original report.
Cheapest model clearing each benchmark bar, and the price of the best model. Log scale.
USD per million tokens, blended 3:1 input:output, log scale
For each month: the cheapest model on OpenRouter scoring at least 50%, 70% or 85% on GPQA Diamond (a hard graduate-level science benchmark), and the price of the best-scoring model ("Frontier", the cheapest within one point of the top score). Prices are per token, not per task: reasoning models use many more tokens per answer, so the fall in real cost is smaller than shown.
Method
scripts/restore_prices.py: monthly Wayback captures of openrouter.ai/api/v1/models (Jul 2023→), flattened; joined to Epoch AI benchmark CSVs by normalised model name; a model counts from the month its benchmarked version was released.
Median and spread of every listed model's price. Log scale.
USD per million tokens, blended 3:1, log scale
The spread of prices across all paid models listed on OpenRouter each month: the median (line), the middle half (dark band, 25th–75th percentile) and 10th–90th percentile (light band). Every listed model counts equally, however much it is used, and many small open models have been added over time.
Median hourly rental price of a top Nvidia GPU across cloud providers. Falling would mean a glut.
USD per GPU-hour, weekly median across providers
The weekly median hourly rental price of one Nvidia GPU across up to 46 cloud providers: the H100 on demand, the H100 on the cheaper interruptible spot market, and the newer B200 on demand. From getdeploying.com's GPU price index. Listed prices, mostly from small providers; large customers pay much less on long contracts.
Then and now: the Nasdaq in the dot-com and ChatGPT eras
Indexed to 100 at each cycle's start. Thin line: 1995–2002. Bold line: since ChatGPT.
Nasdaq Composite, indexed to 100 at cycle start
The Nasdaq Composite, indexed to 100 at the start of each cycle and plotted by months since that start: January 1995 (the year Netscape went public) for the dot-com era, and 30 November 2022 (ChatGPT's launch) for now. The start dates are a choice, and today's index is far more concentrated in a few profitable firms than in 2000.
Above 100%, US industries crashed 40% or more within two years about half the time.
%, trailing 24-month total return, value-weighted US industry portfolio
Total return over the past 24 months of three US industry portfolios from Ken French's data library (chips, computer hardware, software), value-weighted, including dividends. The CSV also has each industry's return minus the market's over the same window. Industries are defined by old classification codes (Microsoft and Oracle count as software, Nvidia as chips), and the lines show raw returns, not returns above the market.
Method
run-up_t = Π_{k=0..23} (1 + r_{t−k}/100) − 1, using value-weighted monthly returns. Net of market uses Mkt-RF + RF from the Fama-French factor file.
Distance from each stock's all-time high. In 2000–02, the Nasdaq fell 78% and Nvidia 90%.
% below the highest close to date (dividend-adjusted)
For each stock or fund, today's close divided by its highest close so far, minus one: zero at a new high, −50% when it has halved. Nvidia (the main supplier), the VanEck Semiconductor ETF (SMH, the chip industry), Oracle (the most indebted hyperscaler) and CoreWeave (the largest neocloud, listed March 2025). CoreWeave has only traded since March 2025, so its peak is only since its IPO.
Long-term debt of the hyperscalers, Oracle and CoreWeave.
USD, long-term debt as reported (quarter end)
Long-term debt reported by Microsoft, Alphabet, Amazon, Meta, Oracle and CoreWeave at each quarter end. Leases and off-balance-sheet vehicles such as Meta's Hyperion financing are not included, and they are a large and growing share.
Operating profit divided by interest expense each quarter. Below 1×, the interest is paid from new borrowing.
× (operating income ÷ interest expense), per quarter
Quarterly operating income divided by quarterly interest expense for the two most debt-financed AI infrastructure builders that file with the SEC: CoreWeave (the largest neocloud) and Oracle (building data centres for OpenAI). Quarters are aligned to calendar quarters. Operating profit depends on how long GPUs are assumed to last, and one-off charges can distort a single quarter.
CoreWeave's return on its equipment, by GPU lifespan
Annualised operating return on gross equipment, as reported and if GPUs wear out in 5 or 3 years.
% per year, quarterly operating profit annualised ÷ gross property and equipment
CoreWeave's quarterly operating profit before depreciation (EBITDA), annualised, minus a depreciation charge, divided by its gross property and equipment (mostly GPUs and servers). "As reported" uses CoreWeave's own depreciation, based on a six-year life. The other two lines assume the equipment is written off evenly over five or three years. Equipment still under construction and not yet earning is included, which pushes the hypothetical lines down; one young company with few quarters of data.
Money investors borrow against their stock. It peaked right at the 2000 and 2007 tops.
USD billions · % of US GDP
Debit balances in customers' securities margin accounts at FINRA member firms (money borrowed from brokers to buy securities), in USD billions and as a share of US nominal GDP. Other kinds of leverage (leveraged ETFs, options, securities-based loans) are not included.
Assets in funds that deliver two or three times the daily move of chip and tech stocks.
USD, net assets (shares outstanding × market price)
Approximate net assets of three Direxion leveraged ETFs: 3× the daily return of a semiconductor index (SOXL), 3× of a large-cap tech index (TECL), and 2× of Nvidia (NVDU). Shares outstanding come from Direxion's daily holdings file; price from Yahoo Finance. Assets move with prices as well as with money flowing in or out, and collection only began in November 2026.
US households' financial assets held in shares, directly or through funds and pensions.
% of household financial assets, quarterly
Corporate equities held directly and indirectly (through mutual funds, pensions and retirement accounts) by US households and nonprofits, as a share of their total financial assets. From the Federal Reserve's Financial Accounts (Z.1). Covers all stocks, not only AI, and rises automatically when prices rise.
What investors pay per person on the payroll, AI labs vs Cisco at the peak of the dot-com bubble.
USD of post-money valuation (or market value) per employee
Each AI lab's latest post-money valuation from a closed funding round, divided by its latest reported headcount, from Epoch AI's AI Companies dataset. The reference is Cisco at its March 2000 peak: $555.5B of market value and about 34,000 employees. Private valuations are set by the last investor, often with special terms and compute credits attached, so they are not directly comparable to a public market value like Cisco's.
Nvidia's stock-market value next to the yearly output of the world's largest economies.
USD: Nvidia market value today; GDP of each country, latest year
Nvidia's market capitalisation (latest share price × shares outstanding from its most recent 10-Q cover page) placed in the World Bank's list of countries by nominal GDP for the latest available year. Market value is the price of all future profits; GDP is one year's output. The comparison is for scale, not equivalence.
Money on the exact question: a defined AI downturn by each date?
implied probability
Daily prices of the open "AI bubble burst by …?" markets on Polymarket (real money), plus the two most-traded "AI bubble pop" markets on Manifold (play money), read as the crowd's probability. Each market resolves Yes if the AI industry has a defined downturn by its date. Thin markets that a few traders can move, with resolution rules that differ by platform; Manifold uses play money.
What it costs to insure CoreWeave and Oracle debt against default.
5-year credit default swap spread, basis points
Five-year credit default swap (CDS) spreads quoted for CoreWeave and Oracle: the annual cost, in basis points, of insuring their debt against default. Collected by hand from published reports, so points are irregular, and some come from press coverage rather than the original report.
Priced offerings per month; the surge in new stock usually comes late in a boom.
final prospectuses (Form 424B4) per month
The number of final prospectuses (Form 424B4, filed when an offering prices) each month, and the number whose text mentions "artificial intelligence", from SEC EDGAR full-text search. Some follow-on offerings and SPACs are included, and a mention of AI can be boilerplate.
How much a share price jumped when a company renamed itself after the hot technology of the day.
% share-price gain on the news (see each note for the window)
The share-price reaction when a listed company announced a new name tied to the theme of the moment: ".com" in 1998–99 (the academic study's average), blockchain in 2017, AI in 2026. A handful of tiny, thinly traded companies, with price windows that differ by example. Collected by hand from published reports, so points are irregular, and some come from press coverage rather than the original report.
Bank of America's survey of global fund managers, monthly.
% of surveyed fund managers
From Bank of America's Global Fund Manager Survey: the share naming "long semiconductors" as the most crowded trade, and the share naming an AI bubble as the biggest tail risk. Collected by hand from published reports, so points are irregular, and some come from press coverage rather than the original report.
Volatility and credit spreads: how nervous markets are overall.
VIX index · percentage points over Treasuries
Three gauges of market nerves: the VIX (30-day volatility of the S&P 500 implied by options), the extra yield investors demand for US high-yield ("junk") bonds over Treasuries (ICE BofA option-adjusted spread), and Moody's Baa corporate bond yield over the 10-year Treasury (lower investment grade). Economy-wide gauges that any macro shock moves, not only AI.
Open-market sales by executives and directors of four AI leaders, per month.
USD sold per month (shares × price, Form 4 code S)
The dollar value of open-market share sales reported by officers, directors and 10% owners of Nvidia, Broadcom, Palantir and AMD on SEC Form 4, summed per month. Open-market purchases are tallied separately in the stored data (extra.open_market_buys_usd_by_month). Most sales are pre-arranged plans that say little about timing.
AI's share of junk bonds, high-yield issuance and pension equity.
% share
Three shares that measure how concentrated financial risk has become in AI: the junk-rated share of data-centre bonds issued in 2026 (FactSet), AI issuers' share of new US high-yield bonds (Bank of England) and the share of US pension equity holdings in tech (Bloomberg). Three figures from different sources measuring different things. Collected by hand from published reports, so points are irregular, and some come from press coverage rather than the original report.
Company reports mentioning artificial intelligence, trailing 12 months.
10-K and 10-Q filings mentioning the phrase, trailing 12 months
The number of annual (10-K) and quarterly (10-Q) reports filed with the SEC each month whose text contains "artificial intelligence", from EDGAR full-text search. Counts filings that mention the phrase, not what they say about it.
How often people say "AI bubble". Bubble talk tends to peak before the top, not after.
HN stories per week · Wikipedia daily views (7-day average)
Two measures of how loud the bubble conversation is: Hacker News submissions per week matching "AI bubble" (Algolia full-text search), and daily human pageviews of the English Wikipedia article "AI bubble", smoothed over 7 days. Exact-phrase matching misses synonyms, and Wikipedia traffic overall is shrinking.
How often ordinary regional businesses bring up AI when the Fed asks how things are going.
mentions of "artificial intelligence" or "AI" per edition, all 12 districts
The number of times "artificial intelligence" or the word "AI" appears in each edition of the Federal Reserve's Beige Book, which summarises anecdotes from businesses and contacts in all 12 Fed districts, eight times a year. A word count, not sentiment.
Posts in six AI subreddits, each indexed to its 2024 average.
posts per month, each subreddit indexed to its 2024 average = 100
Monthly post counts in six AI subreddits (r/LocalLLaMA, r/ChatGPT, r/OpenAI, r/ClaudeAI, r/singularity, r/artificial), each indexed to its own 2024 average so large and small communities can share an axis. "Blend" is the geometric mean of the six indices. Post counts include removed spam, and the archive can have gaps (a missing month is skipped, not zero).
Wikipedia readers of history's famous manias. People look up the last bubble when they suspect a new one.
monthly pageviews, indexed: 2016–2019 average = 100, log scale
Monthly human pageviews of four English Wikipedia articles about past speculative manias: the Dutch tulip mania of 1637, the South Sea Bubble of 1720, the dot-com bubble of 2000, and Pets.com, its best-known casualty. Each is indexed to its own 2016–2019 average so they share a scale. Wikipedia traffic overall is falling, which pulls every line down over time.
New papers listed on arXiv in machine learning, AI and language.
papers per month, by primary category
Number of papers listed each month in three arXiv computer-science categories: machine learning, artificial intelligence, and computation and language (NLP and language models). Volume is not quality: some of the growth is low-effort or machine-written papers.
Wikipedia readers of each hype term, as a share of its own peak month. Every one so far has faded.
% of the article's peak monthly pageviews
Monthly human pageviews of the English Wikipedia articles on four tech buzzwords, each divided by its own busiest month. NFTs peaked in March 2021, the metaverse in November 2021 (after Facebook renamed itself Meta), prompt engineering in April 2023, and vibe coding in March 2025. Attention is not usage: people stop looking a word up once they know it, and Wikipedia traffic overall is falling.
How many Super Bowl advertisers came from the hot sector of the day. The dot-com and crypto bowls came just before the crashes.
national Super Bowl commercials from the sector
The number of national commercials during the Super Bowl broadcast from companies in the period's hottest sector: dot-coms in January 2000, crypto exchanges in February 2022 and 2023, and AI in February 2026. The 2026 count includes ads made with AI tools, not only ads for AI products, and many 2026 advertisers are among the most profitable companies in the world. Collected by hand from published reports, so points are irregular, and some come from press coverage rather than the original report.
Monthly human pageviews of the English Wikipedia articles for ChatGPT, Claude and Gemini. Each line adds the article's former titles to its current one, since Claude and Gemini were renamed; Wikipedia traffic overall is falling as people ask AI tools instead.