Mapping the AI Terrain: A Deeper Dive with Cohort-based Developer AI usage analysis

1. Recap: Why Go Deeper?

After kickstarting Project Reboot, our initial survey gave us baseline insights. But averages hide variance. To design better interventions, we are looking deeper – by slicing our data into meaningful developer cohorts.

2. The Cohort Method

We plan to group engineers based on some associations such as:

• Tech domain: Mobile, Web, Backend, Infra, etc.
• Editor environment: VS Code, JetBrains, Cursor, etc.
• Role: IC, TL, EM.
• AI tooling preference: Web UI based, IDE integrations based.

• Output contribution based: Less than 25% code is AI generated band, 25-50% code is AI generated band, etc.

And collect data on cohort-specific AI tool usage. This will help us develop a targeted execution plan to achieve higher baseline AI utilization.

3. What We Plan to find

While the org wide assessment provided us with these most common usage intents:

We intend to do an extensive 2-phased cohort analysis to gather more specific and actionable data.

Phases

• Phase 1: Breakdown and determine AI tool use cases per cohort.
• Phase 2: For each cohort, breakdown and analyze usage per quartile.

4. How We Plan to Follow-up

Based on this, we’re:

• Targeting team-specific enablement programs.
• Experimenting with “AI Pairing Hours” in low-usage cohorts.
• Creating a “Best Prompts Repository” per domain.
• Allocating LLM Proxy tool credits by cohort usage growth (Ongoing).

5. What’s Next

We’ll run this cohort analysis again in ~6 weeks. With this data, we aim to not just track progress but build predictive indicators of successful AI adoption.