Loading...
Q1 2026 Rankings
AI and ML engineering teams are moving at an unusual pace this quarter. We are tracking model infrastructure startups with commit velocities that have tripled in under three weeks.
Key Takeaway
In Q1 2026, 9 of 16 tracked ai & machine learning startups show positive engineering acceleration. zapplyjobs leads with 8 commits over 14 days (+999% change). The dominant signal pattern is "Framework migration". Average sector commit velocity is 208 commits per 14-day window. These engineering momentum signals have historically preceded fundraise announcements by three to six weeks.
Data sourced from public GitHub activity. Read our methodology
Sorted by commit velocity change (14-day window, descending). Top 3 highlighted. Data last updated Q1 2026.
Get the free weekly engineering-acceleration rankings, or unlock the full Dashboard for real-time tracking, sector filters, and founder contact data. Beta pricing: €9.97/mo.
Want the signal while you research? Crunchbase/Wellfound badge or GitHub hover lookup — both free.
Best of 2026
Top AI & Machine Learning startups ranked across all 2026quarters →
Q1 2026 vs Q4 2025
How AI & Machine Learningcommit velocity has shifted quarter-over-quarter →
By signal type in this sector
By stage
In Q1 2026, we are tracking 16 ai & machine learning startups with measurable GitHub engineering signals. 9 of 16 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 12 of the tracked companies. The average 14-day commit velocity across the sector is 208 commits, with zapplyjobs leading at 8 commits (+999% change). These patterns have historically preceded fundraise announcements by three to six weeks.
zapplyjobs leads the ai & machine learning sector in Q1 2026 with 8 commits over a 14-day window, representing a +999% change from the prior period. With 3 active contributors, zapplyjobs is showing a "Engineering hiring burst" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.
Among the 16 ai & machine learning startups we track, US accounts for the highest concentration with 5 teams. Startups building AI/ML infrastructure, applications, and tools. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.