Methodology
Every number links to a published study. We show ranges because these are estimates — not stopwatch measurements.
How time spent is estimated
We sum gaps between your messages when gaps are under 30 minutes, cap each session at 2 hours, and assign a 1-minute minimum per conversation. This reflects estimated active time — not official session logs from AI providers.
How time saved is estimated
Each conversation is classified into a task type (writing, coding, email, etc.). We apply a minutes-saved multiplier from peer-reviewed research, scaled by assistant output length and your skill level from the quiz.
| Category | Savings | Source |
|---|---|---|
| writing | 40% | Noy & Zhang, Science 2023 |
| 31% | Jaffe et al., Microsoft 2024 | |
| coding | 26% | Cui et al., Management Science 2026 |
| support | 15% | Brynjolfsson/Li/Raymond, QJE 2025 |
| analysis | 25% | Dell'Acqua et al., HBS/BCG 2023 |
| translation | 30% | Macken et al., EC DGT 2020 |
| research | 40% | Noy & Zhang 2023 (writing proxy) |
| meeting notes | 67% | Cisco Webex AI 2024 |
| brainstorm | 30% | Dell'Acqua et al. 2023 (centaur) |
| image gen | 80% | Industry estimate — flagged |
| learning | 25% | Weighted average |
| other | 15% | Conservative default |
Calibration quiz
Before your wrap, eight questions calibrate estimates to your profile. They adjust a base skill multiplier with modifiers for primary use, replacement vs augmentation, verification habits, work context, and coding-on-mature-codebase (METR 2025).
- Replacement ratio: mostly replacing existing work (×1.0) vs mostly new tasks you wouldn't attempt (×0.55).
- Verification: careful review adds time back (×0.9 always) vs rarely checking (×1.03, wider confidence band).
- Primary use alignment: savings boost when conversation category matches your stated primary use.
- Mature codebase: extra downward adjustment on coding conversations when you work on familiar repos as an expert.
Skill multipliers
- novice: ×1.5
- intermediate: ×1
- expert: ×0.6
- expert mature_code: ×0.4
Counter-evidence we disclose
- METR 2025: Experienced open-source developers were ~19% slower on their own mature repos with AI, while believing they were faster. Expert + mature-code personas use a 0.4× multiplier.
- Dell'Acqua jagged frontier: Tasks outside AI capability saw 19% lower accuracy — harm invisible in exports.
- Perceived vs measured: Self-reported time savings often exceed measured savings (Jaffe et al., Microsoft 2024).
Platform data gaps
- Claude: No per-message model in exports.
- Gemini: Flat activity log; threads are reconstructed heuristically.
- Grok: No model variant per message.
- ChatGPT: No token counts; o-series reasoning time not visible.