Every week, Pace AI reads hundreds of news items about AI and work. It sorts them into five categories, removes duplicates, classifies each story by type and severity, and calculates a score using fixed rules — no human judgment, no editorial picks.
The result is a single number from 0 to 10 that tells you how much actually happened this week.
Each component tracks a different dimension of AI's impact on work. They're scored independently, then combined.
The heaviest weight, because it's what most people care about.
Tracks layoffs citing AI, hiring freezes, role eliminations, reskilling announcements, and workforce restructuring. Only stories with explicit AI attribution are counted — a company laying off staff without mentioning AI doesn't register.
Evidence quality matters. A single anonymous claim scores very differently from an official company filing or an independent research report. Multiple verified companies in the same week push the score higher.
Tracks real organisations rolling out AI tools to real users. Not announcements. Not pilots. Not "we're exploring AI." Actual deployments with named organisations and measurable scale.
A vague press release about "AI transformation" scores near zero. A named company deploying a specific tool to a specific number of employees scores meaningfully. The system tracks organisations across weeks to avoid counting the same rollout twice.
This one works differently from the others.
The score comes primarily from benchmark data — standardised tests that measure what AI models can actually do. When a new model enters the top rankings or existing models improve significantly on established benchmarks, the score reflects that.
Separately, the system tracks news about features that shipped to real users this week. These "new capabilities" appear on the site alongside the score, showing what's newly possible in practical terms.
Tracks laws passed, executive orders signed, court rulings issued, and enforcement actions taken. Not rhetoric. Not op-eds about what regulation should look like. Actions with legal weight.
A senator giving a speech about AI scores very differently from a court ruling that changes what companies can do. The system also distinguishes between narrow, jurisdiction-specific actions and broad international frameworks. Like Deployment, it tracks regulatory entities across weeks so the same law doesn't get re-scored every time it's mentioned.
Tracks macro-level market signals — analyst forecasts, aggregate investment data, sector-wide spending reports, and ROI studies. The filter is strict: individual company funding rounds don't qualify. Only aggregate or sector-wide data from named research sources.
This component answers: where is the money going, and what are the people who move money saying about AI's trajectory?
The system uses a two-stage process:
This separation is deliberate. The AI is good at reading and categorising. It's not good at deciding what matters. The scoring rules are written by a human and versioned like code.
The five component scores combine into a single index:
| Component | Weight |
|---|---|
| Labor | 30% |
| Deployment | 20% |
| Capability | 20% |
| Regulation | 15% |
| Markets | 15% |
Labor carries the most weight because workforce impact is what working professionals care about most. If a component has no data for a given week, its weight is redistributed proportionally among the others.
The composite is divided by 10 to produce the 0–10 pace reading shown on the site.
The pace reading maps to one of four zones:
Zone thresholds are calibrated against a year of historical data to ensure the distribution feels right — Sprinting should be rare, Walking should be common. If every week is Running, the thresholds need adjusting.
The methodology is versioned. When we change scoring rules, thresholds, or source feeds, we document it and note the version. Historical scores are not retroactively changed — they reflect what the system saw at the time.
We run calibration checks against the full historical dataset whenever we make changes, ensuring scores still make sense across known events.
If a week's score doesn't match what you observed in your industry, we want to hear about it. The system is only as good as its inputs and rules, and both can be improved.