Correlate AI industry trends with market performance — layoffs, investment, jobs, and index returns in one view.
Each series normalised to a 0–100 scale. Tooltip shows original values.
Performance indexed to 100 at January 2020. Toggle indices to compare.
This first table includes layoffs across all industry sectors, including records without a specific industry classification. Pearson's r measures the strength and direction of the linear relationship between two series, ranging from −1 (perfect inverse) to +1 (perfect agreement), with 0 meaning no linear relationship. Each value uses completed seasonal periods from Jan 2020 onward where both the all-sector layoff count and comparison series have data.
| Series | Correlation (r) | Strength | Direction | Seasons/Years (n) |
|---|---|---|---|---|
| Calculating… | ||||
The base layoff series in this table is the monthly total across every sector. Market, unemployment, and jobs correlations use seasonal (quarterly) averages to reduce month-to-month noise. AI Investment is recorded annually, so its correlation uses annual all-sector layoff and investment totals.
Pearson's r compares layoffs recorded in the Technology industry with AI investment, AI-focused and broad market indices, US unemployment, and new AI job postings. Seasonal calculations exclude the current incomplete quarter; annual investment calculations exclude the current incomplete year.
| Series | Correlation (r) | Strength | Direction | Seasons/Years (n) |
|---|---|---|---|---|
| Calculating… | ||||
Technology-sector layoffs are identified by the Technology industry classification. Market, unemployment, and jobs correlations use seasonal averages; AI Investment uses annual totals.
Data are fetched automatically using AI agents from trustworthy internet public sources. Layoffs are calculated by summing reported employees affected across published records in each month. The Technology-sector series uses the same calculation but only includes records classified as Technology. New AI Jobs counts listings collected by AI Storm; it does not represent every AI vacancy in the economy.
AI Investment is the annual global total measured in constant 2021 US dollars. It covers qualifying private-company equity deals and excludes internal R&D and public-company spending; some undisclosed deal values are estimated. The global record is used directly so country and regional rows are not added again. US unemployment is a monthly harmonised rate, seasonally and calendar adjusted, expressed as a percentage of the labour force.
Market data contains monthly observations for the S&P 500 index, XLK as the S&P 500 Information Technology sector proxy, and BOTZ as a robotics and artificial-intelligence ETF proxy. Charts use each month's closing value.
The monthly view uses the monthly values described above. In the seasonal view, January–March form Q1, April–June Q2, July–September Q3 and October–December Q4. Layoffs and jobs are summed within each quarter, market series use the quarter-end close, unemployment uses the average available monthly rate, and the annual investment value remains unchanged throughout its year.
The yearly view sums layoffs and jobs, uses the final available market close of the year, averages monthly unemployment, and uses the single published annual investment total. The current month, current quarter or current year is removed from its respective view because it may still be incomplete.
Each visible series is independently converted to a 0–100 index using (value − series minimum) ÷ (series maximum − series minimum) × 100. This compares the shape and direction of change, not absolute scale: 100 means that series' highest displayed observation and 0 means its lowest. Tooltips retain the original count, dollar value, rate or market close.
Unlike min-max normalisation, this chart divides every monthly close by that series' January 2020 close and multiplies by 100. A reading of 150 means a 50% gain from the baseline; 80 means a 20% decline. The newest month is excluded.
Pearson's r measures whether two series tend to move together over time. A value close to +1 means that periods with high layoffs also tend to have high values in the other series — they rise and fall together. A value close to −1 means the opposite: high layoffs tend to coincide with low values in the comparison series. A value near 0 suggests no consistent linear relationship between the two.
To illustrate: if S&P 500 showed r = −0.45, that would indicate a moderate inverse relationship — seasons with higher layoff activity have tended to coincide with lower index levels. If New AI Jobs showed r = +0.30, that would suggest a weak positive relationship — layoff-heavy seasons have a slight tendency to also see more new AI job postings, perhaps reflecting industry churn rather than overall contraction. These are hypothetical readings to show how to interpret the numbers; refer to the table for the current values.
The strength labels classify the magnitude of r: Strong (|r| ≥ 0.70), Moderate (0.50–0.69), Weak (0.30–0.49), Negligible (< 0.30). A strong correlation does not imply causation — it means the two series have moved in a related pattern over the observed period, which may reflect shared economic conditions rather than a direct link.
Each table uses the same method, but the first includes layoffs from every industry while the Tech Sector table includes only Technology records. For market, unemployment and jobs rows, each series is averaged across the available months in a completed quarter, then Pearson's r is calculated only from quarters where both series have values. AI Investment is annual, so that row compares annual layoff totals with annual World investment totals and excludes the current year. The Seasons/Years (n) column is the number of paired observations used; a higher n generally makes the result more stable.