# Anthropic发布Claude使用日志报告：AI进入工作的早期传感器

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-06-27 06:05
- AIHOT 分数：70
- AIHOT 链接：https://aihot.virxact.com/items/cmqvi01pp0dagsl807jyzrfmf
- 原文链接：https://x.com/rohanpaul_ai/status/2070629603178356850

## AI 摘要

Anthropic发布“Cadences”报告，分析近1万名Claude用户的匿名对话。个人提示词周末从35%升至近50%；食谱请求下午6点达峰值（平均值2.3倍）；新闻早7点峰值；商务邮件集中在10-11点；睡眠建议凌晨3-5点；美国税务请求在申报截止日前飙升8倍后骤降。周末Claude Code工作从后端转向AI agent设计、量化交易和游戏。93%对话产生清晰输出，最常见为解释（17%）、文档/报告（15%）和指导（11%）。高薪职业对话所用token数是低薪职业的约2.07倍。

## 正文

Claude’s new usage logs now read like an early sensor for how AI is entering work.

Anthropic just published its new “Cadences” report review, anonymized conversations from almost 10,000 Claude users.

• Personal prompts rises from 35% on weekdays to nearly 50% on weekends.

• Recipe requests peak at 6pm and become 2.3x more common than average.

• News prompts peak at 7am, while business emails peak around 10-11am.

• Sleep advice clusters before dawn, with people most often seeking it around 3-5am.

• Tax requests in the US spiked 8x right before the filing deadline, then collapsed almost immediately.

• Weekend Claude Code work shifts away from backend architecture and API debugging toward AI agent design, quant trading, and gaming.

• Work done through Claude at nights and weekends skews toward higher-wage occupations, not lower-wage clerical tasks.

• Claude now produces a clear output in 93% of chat and Cowork conversations.

• The most common Claude outputs are explanations 17%, documents/reports 15%, and guidance 11%.

• Marketing content, blogs, and database queries are among the most work-heavy outputs, each around 80%+ work-related.

• Creative writing, guidance, and recipes are mostly personal, each above 80% personal use.

• Work conversations most often produce documents/reports 20%, while personal conversations most often produce explanations 25% and recommendations 22%.

• Higher-wage work burns more compute, with top-wage occupation conversations using about 2.07x as many tokens as bottom-wage ones.

• App-building conversations use more than 3x the median tokens, while basic explanations use about 1/5 of the median.