What happens when a developer adopts AI coding assistants? To answer this question with data rather than anecdotes, we analyzed @restlessronin's GitHub commits across two periods: six months before using AI tools (Jun-Nov 2022) and six months with AI assistance (Nov 2024-Apr 2025).
After filtering out documentation-only commits to ensure a fair comparison of coding activity, the data reveals a fundamental transformation in development patterns.
Aggregate Productivity Summary
Summary of code productivity metrics comparing Pre-AI and Recent-AI periods.
| Metric | Pre-AI | Recent-AI |
|---|---|---|
| Date Range | Jun 2022 - Nov 2022 | Nov 2024 - Apr 2025 |
| Active Days | 101 | 144 |
| Total Commits | 457 | 836 |
| Commits per Day | 4.52 | 5.81 |
| Total Repositories | 16 | 19 |
The aggregate metrics show substantial gains: 43% more active coding days, 83% more code commits, and consistently higher daily output. But these numbers only hint at the deeper behavioral shifts revealed in the detailed visualizations.
Development Patterns: From Sporadic to Sustained
Commit Activity Timeline
Commit Activity Timeline
The visual contrast is striking. The Pre-AI period displays the familiar rhythm of traditional development: intense coding sessions followed by quiet periods, with notable gaps between productive days. The AI-assisted period shows a dramatically different pattern—nearly continuous activity with commits distributed throughout each working day.
This transformation suggests AI tools reduce the activation energy required to start coding and maintain momentum throughout sessions.
Commit Frequency: Doubling Daily Output
Daily Commit Distribution
Commits per Day
Number of code commits made each active coding day
The distribution shift is remarkable. Pre-AI, the median sits at 3 commits per day—reflecting the traditional pattern of extended work followed by substantial commits. With AI assistance, the median doubles to 6 commits, with the upper quartile reaching 10-20 daily commits. The log scale reveals extreme days with 30+ commits, a frequency virtually absent in the pre-AI data.
Note: Box plots show quartiles (25th, 50th, 75th percentiles) with markers at the 5th and 95th percentiles, whiskers are to the min-max. The underlying histogram provides additional distribution detail.
These aren't trivial commits padding the statistics. The pattern indicates reduced friction in the development cycle—less time stuck on implementation details, more time making progress.
Work Duration: Same Hours, More Output
Coding Time
Coding Time
Estimated coding time based on commit timestamps
Both periods show similar session lengths clustering around 200-400 minutes (3-7 hours). The productivity gains don't come from longer hours—they come from more efficient use of the same time investment. This sustainability is crucial: AI tools enhance productivity without demanding burnout-inducing schedules.
Flow State Indicators: Compressed Iteration Cycles
Time Between Commits
Individual Commit Intervals
Distribution of time between all consecutive commits showing coding rhythm patterns
This metric best captures the "flow state" effect. The log scale shows Pre-AI median intervals around 20 minutes between commits, with many gaps stretching to hours. Post-AI, the median compresses below 10 minutes, and the entire distribution tightens. Maximum intervals shrink from 8+ hours to under 3 hours.
The data quantifies what developers subjectively experience: AI tools eliminate the "stuck" moments. Instead of 30-minute detours through documentation or Stack Overflow, developers get immediate suggestions and maintain momentum.
Code Volume: Doubled Output at Consistent Quality
Lines of Code Changed
Lines of Code
Total lines changed (additions + deletions) per day
The log scale reveals significant output increases. Pre-AI shows a median around 200 lines changed daily. With AI tools, this doubles to approximately 400 lines. The upper ranges diverge even more dramatically: AI-assisted days reach 2,000-10,000 lines changed, while pre-AI peaks around 1,000.
Combined with the commit frequency data, this indicates each commit maintains appropriate scope while total productive output doubles.
Temporal Patterns: All-Day Productivity
Commits by Hour of Day
Hourly Distribution
When commits happen throughout the day across all periods
Pre-AI shows concentrated afternoon productivity (12-4 PM peak)—a common pattern when cognitive load limits productive hours. AI assistance spreads productivity across the entire workday. Morning hours become equally productive, and evening sessions (6-10 PM) show particular growth.
This temporal spread suggests AI tools preserve cognitive resources by handling routine tasks, enabling sustained productivity across more hours.
Commits per Active Hour
Commit Intensity
Distribution of commit frequency within active coding hours
Within active hours, productivity intensity doubles. Pre-AI shows 1-2 commits per active hour. With AI, the median shifts to 2-3 commits, with upper ranges reaching 6-9 commits per hour. This isn't frantic activity—it represents smooth, continuous progress enabled by reduced implementation friction.
Key Findings
The data reveals AI's impact on development work through multiple reinforcing effects:
Quantitative Gains:
- 43% increase in active coding days
- 83% increase in total commits
- 2x increase in lines of code changed
- 50% reduction in median time between commits
Qualitative Patterns:
- Sustained daily activity replacing sporadic bursts
- Consistent productivity across all working hours
- Maintained session lengths without burnout
- Preserved commit quality despite increased frequency
The evidence suggests AI coding assistants function as "friction reducers" rather than code generators. By eliminating small obstacles—syntax lookups, boilerplate generation, implementation details—they enable developers to maintain flow states that were previously difficult to sustain.
For developers evaluating AI tools, these patterns suggest focusing on workflow integration rather than raw code generation capabilities. The greatest gains come from tools that reduce the micro-frictions interrupting development flow, enabling the sustained productivity patterns visible in this data.