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The Fast and the Curious

“In the AI age, the ability to learn faster than the rate of change is the only sustainable advantage.” — Adapted from Eric Ries

We hire people who are both fast and curious—those who ship today while learning for tomorrow.

Speed is our advantage in the rapidly evolving AI landscape:

  • Weekly Releases: We ship updates to Jan every week, not every quarter
  • Rapid Experimentation: Test ideas with real users, not focus groups
  • Fail Fast, Learn Faster: Kill features that don’t work, double down on what does
  • User Feedback Loop: From idea to user’s hands in days, not months
  • Bias for Action: See a problem? Fix it. Don’t wait for permission.
  • MVP Mindset: Launch at 80% perfect, iterate to 100%
  • Quick Decisions: Make reversible decisions quickly, deliberate only on irreversible ones
  • Async by Default: Don’t let time zones slow us down

We’ve shipped:

  • Major features in days that others debate for months
  • Model support hours after release, not weeks
  • Bug fixes while users are still typing the report

In AI, yesterday’s breakthrough is today’s baseline:

  • New Models Weekly: Understand and integrate the latest AI advances
  • Cross-Domain Knowledge: From quantization techniques to UI design
  • Community Learning: Our users teach us as much as we teach them
  • Open Source Study: Learn from the best codebases in the world
  • Why Over What: Don’t just implement—understand the reasoning
  • Question Everything: “Why do we collect user data?” led to our privacy-first approach
  • Learn in Public: Share discoveries with the community
  • Teach to Learn: Explaining concepts deepens understanding
  • No Experts: In a field moving this fast, everyone is learning
  • Share Knowledge: Daily discoveries in our Discord channels
  • Document Learning: Today’s experiment is tomorrow’s documentation
  • Celebrate Questions: The “stupid” question often reveals the biggest insight
  • Onboarding: New hires teach us fresh perspectives
  • Community Education: Blog posts, tutorials, and demos
  • Code as Teaching: Well-commented code educates future contributors
  • Failure Stories: Share what didn’t work and why
  • GitHub Velocity: Frequent commits, quick iterations
  • Project Completion: Finished projects, not just started ones
  • Response Time: Quick to engage, quick to deliver
  • Adaptation Speed: How fast do you integrate feedback?
  • Side Projects: What do you build for fun?
  • Learning Artifacts: Blogs, notes, or projects showing learning
  • Question Quality: Do you ask insightful questions?
  • Knowledge Breadth: Interests beyond your specialty
  • Models improve monthly
  • User expectations evolve weekly
  • Competition ships daily
  • Standards change quarterly

If we’re not fast and curious, we’re obsolete.

  • Fast: Users expect immediate responses, not cloud latency
  • Curious: Supporting every model requires understanding each one
  • Fast: Privacy bugs need instant fixes
  • Curious: New quantization methods need quick adoption

Fast + Curious creates exponential growth:

Ship Fast → User Feedback → Learn →
Ship Smarter → More Users → More Learning →
Ship Even Faster → Compound Growth

Each cycle makes us:

  • Faster at shipping
  • Better at learning
  • More valuable to users
  • More attractive to talent
  • You’ve shipped something this week (not this year)
  • You’ve learned something new today (not last month)
  • You see a Jan issue and think “I could fix that”
  • You read our codebase and think “I could improve that”
  • You use Jan and think “It could also do this”

If you join Jan as someone fast and curious, in a year you’ll be:

  • Faster: Shipping features you can’t imagine today
  • Smarter: Understanding AI at a level that surprises you
  • Connected: Part of a global community of builders
  • Impactful: Your code running on millions of devices

We don’t hire for what you know today. We hire for how fast you’ll know what matters tomorrow.

In the race to build open superintelligence, the fast and curious don’t just keep up—they set the pace.


“At Jan, we measure progress in iterations per week, not years of experience.”