November 25, 2025
Inside JetBrains—the company reshaping how the world writes code
By integrating OpenAI models across its tools and workflows, JetBrains is redefining how developers design, reason, and build with AI.
If you don’t write software, you may not know JetBrains.
If you do, you almost certainly use them.
The company sits behind the scenes of modern development—powering the tools used by roughly 15M professional engineers around the world (88 of the Fortune 100) and creators of Kotlin (the official programming language for Android). If you’ve opened IntelliJ, PyCharm, WebStorm, GoLand, or Rider, you’ve used JetBrains.
We sat down with Kris Kang, Head of Product at JetBrains, to explore how the team is using OpenAI models to change how developers build—not to replace what they do, but to raise the ceiling.
“Developers don’t just write code. They review it, reason about it, and design systems. AI can help with the parts beyond simply typing.”
How JetBrains is adopting OpenAI
“+15M developers use JetBrains—and now we’re bringing OpenAI into that workflow” Kang tells us that. The shift isn’t just about automation; it’s also about empowerment. It’s about protecting a dev’s flow, reducing repetitive work, and letting engineers focus on design, architecture, and judgment—the skills that give you longer leverage with AI.
Internally, JetBrains teams are using:
- ChatGPT
- GPT‑5
- Codex
Externally, JetBrains customers can choose GPT‑5 in Junie, the company’s coding agent, and in AI Assistant (for chat assistance).
“We use ChatGPT. We use GPT-5. We use Codex… one of the LLMs of choice for Junie is GPT-5.”
Engineers are already delegating real tasks to agents—and seeing them completed. “I assign increasingly difficult tasks to an agent, backed by GPT‑5—and to my surprise, many of the tasks are completed successfully” says Kang.
JetBrains’ benchmark isn’t speed alone—it’s sustained engineering excellence. “It’s not just about generating code—it has to be safe, readable, and maintainable” Kang continues.
JetBrains considers impact through two lenses:
Speed: Less boilerplate, fewer context switches, faster iteration.
Quality: Readable, reviewable, maintainable code—not clever output that breaks in production.
Leadership lessons from Kris
Start where humans feel friction: Documentation. Tests. Reviews. Hand-offs.
Protect deep work: Context switching kills more than typing speed ever will.
Build hybrid—not replacement—workflows: AI drafts. Humans design and review.
Raise the bar on fundamentals: Well-specified intention and strong architecture become a force multiplier.
Run experiments that compound: Efficient iteration beats instant proof.
“Chat gives you a lift. Agents give you a step-change.”
What’s next
A future where engineers:
- Design systems
- Guide and guardrail agents
- Review and reason more efficiently
- Ship faster with more confidence
Not less work—better work.
“Those who experiment well with AI will see compounding advantages over time.”
Join the new era of work
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