ANTHROPIC JUST RELEASED INTERNAL DOCUMENT: HOW THEIR OWN TEAM USES AI FOR WORK
Anthropic (the company that created Claude, ChatGPT's main competitor) just published an internal document sharing how 10 teams in the company use Claude Code for their daily work.
I read all 22 pages. There's one case study that made me stop and think:
THE MARKETING TEAM HAS ONLY 1 PERSON
Anthropic's Growth Marketing team has exactly 1 person. Doesn't know code. Responsible for Google Ads, Facebook Ads, email marketing, SEO, app store.
Previously, creating Google Ads content took 2 hours each time. Had to write each headline (30 character limit), each description (90 character limit), for hundreds of different campaigns.
Now this person uses Claude Code to:
- Read CSV file containing hundreds of old ads with performance metrics
- Automatically analyze which ads are underperforming
- Automatically generate hundreds of new variants, within character limits
=> From 2 hours to 15 minutes. 10x speed increase.
NOT STOPPING THERE
This person also built a Figma plugin to generate 100 ad image variants in half a second. Previously had to copy-paste manually, taking hours.
Then built an MCP server connecting to Meta Ads API, to analyze Facebook Ads campaign performance right in Claude without switching between platforms.
Reminder: this person doesn't know code 😬.
NOT JUST MARKETING
The document also shares case studies from 9 other teams:
- Legal team: Lawyer who doesn't know code built an app to support communication for family members with illness, in 1 hour
- Design team: Designers implement interfaces themselves, fix code themselves instead of waiting for programmers. 2-3x faster
- Finance team: Finance staff writes requests in plain English, AI runs data queries and outputs Excel
- Security team: Debug infrastructure issues from 15 minutes to 5 minutes
- Data Science team: Wrote a 5,000-line TypeScript app despite "almost not knowing JavaScript"
KEY LESSONS LEARNED
Common points among all 10 teams:
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No one uses AI in a "ask 1 question, get 1 answer" way. They create automated workflows for entire processes
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Claude.md file (file guiding AI on work context) is the most important thing. Teams with detailed Claude.md use AI much more effectively
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AI gets it right on first try only about 1/3 of the time. But total time saved is huge because when right, it's many times faster
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Don't try to fix errors when AI messes up. They save current version first, let AI try, then check. If wrong, restore old version, let AI try again from scratch. Faster than editing AI-generated results.
=> The line between "knows code" and "doesn't know code" is blurring very quickly. The issue is no longer whether you know programming, but whether you can accurately describe what you need.
PS: Original PDF file and translation in comments below
Link: https://www-cdn.anthropic.com/58284b19e702b49db9302d5b6f135ad8871e7658.pdf
