85% of Developers Use AI Regularly: What the JetBrains Survey Reveals About 2025

Hello HaWkers, the annual "State of Developer Ecosystem" survey from JetBrains brought impressive data about how artificial intelligence is transforming software development. The most impactful number: 85% of developers now use AI tools regularly in their work.

Are you part of those 85%? If not, how are you preparing for a market where AI is already the norm?

The Survey Numbers

JetBrains surveyed more than 25,000 developers worldwide for their 2025 survey. The results show massive and rapid adoption of AI tools.

Key Findings

AI adoption in development:

85% use AI tools regularly
62% rely on at least one AI code assistant
45% consider AI essential for productivity
78% believe AI improved code quality
23% report 30%+ increase in productivity

Usage Frequency

Frequency	Percentage
Multiple times a day	52%
Daily	23%
Weekly	10%
Occasionally	12%
Never	15%

💡 Insight: More than half of developers use AI multiple times a day, fully integrating it into their workflow.

Most Used Tools

The survey reveals which tools dominate the AI development market.

Code Assistant Ranking

Top 10 most used tools:

GitHub Copilot - 54% of AI users
ChatGPT - 48%
Claude - 32%
Cursor - 18%
JetBrains AI - 15%
Codeium - 12%
Amazon CodeWhisperer - 9%
Tabnine - 8%
Gemini - 7%
Windsurf - 5%

Most Common Use Cases

What devs use AI for:

Use Case	Percentage
Code autocomplete	78%
Explain existing code	65%
Generate unit tests	52%
Debug and error solving	49%
Refactoring	45%
Documentation	42%
Code review	38%
Architecture and design	25%

Productivity Impact

The productivity data is particularly interesting.

Reported Gains

Productivity increase by experience level:

Junior (0-2 years): +35% average
Mid-level (2-5 years): +28% average
Senior (5-10 years): +22% average
Staff/Principal (10+ years): +18% average

Areas with greatest impact:

Boilerplate writing: 65% time reduction
Unit tests: 50% faster
Documentation: 45% time savings
Debug: 40% more efficient
Code review: 30% faster

Code Quality

Quality perception:

45% say AI improved code consistency
38% noticed reduction in trivial bugs
32% report better test coverage
28% observed more complete documentation

However:

22% are concerned about generated code security
18% question accuracy in complex cases
15% noticed introduction of subtle bugs

Job Market Changes

The survey also explores how AI is impacting careers.

Valued Skills

Skills that gained importance with AI:

Prompt engineering: 67% consider important
Critical AI code review: 58%
Systems architecture: 52%
Critical thinking: 48%
Communication: 45%

Skills that lost relative importance:

Syntax memorization: -45%
Boilerplate writing: -42%
Manual documentation: -38%
Basic manual testing: -35%

Impact on Jobs

Market perception:

35% believe AI reduced entry-level jobs
42% see more demand for seniors
55% say AI created new types of jobs
28% changed areas due to AI (e.g., to ML/AI)

Salaries and AI

Correlation between AI use and salaries:

AI Usage Level	Average Salary (USA)
Does not use	$95,000
Basic use	$105,000
Intermediate use	$125,000
Advanced use	$145,000
Expert/Contributor	$175,000

🔥 Highlight: Developers who master AI tools earn on average 50% more than those who do not use them.

How the Best Developers Use AI

The survey identified usage patterns among the most productive developers.

Top Performer Practices

What the top 10% do differently:

Use multiple tools: Combine Copilot, ChatGPT, and Claude
Customize prompts: Create templates for recurring tasks
Always verify: Never blindly trust the output
Learn from AI: Use explanations to improve knowledge
Automate workflows: Integrate AI into CI/CD pipelines

Typical Top Performer Workflow

1. Planning
   ├── Use AI for architecture brainstorming
   └── Validate ideas with own knowledge

2. Development
   ├── Copilot for autocomplete
   ├── Claude for complex logic
   └── Manual review of all code

3. Testing
   ├── AI generates initial tests
   ├── Dev adds edge cases
   └── Minimum coverage: 80%

4. Review
   ├── AI does first pass
   ├── Human dev does final review
   └── Documentation generated with AI + review

Challenges and Concerns

The survey also revealed significant concerns.

Main Challenges

What concerns developers:

Concern	Percentage
Generated code security	45%
Excessive AI dependence	42%
Proprietary code privacy	38%
Accuracy in complex cases	35%
Tool cost	32%
Impact on jobs	28%

Reported Incidents

Problems encountered:

35% have committed code with AI-generated bugs
22% leaked sensitive code to AI tools
18% wasted time with incorrect suggestions
15% had to revert entire features

Trends For 2026

The survey also asked about future expectations.

Developer Predictions

What they expect for the next 12 months:

72% believe AI will be even more integrated
58% expect autonomous code agents
45% predict reduction in junior jobs
62% plan to invest more in AI skills
38% consider moving to AI-related areas

Rising Tools

What devs want to learn:

Claude Code / Claude Artifacts
Cursor with custom models
Autonomous code agents
Local AI tools (privacy)
AI for DevOps and infrastructure

What You Should Do

Based on survey data, here are practical actions:

For Those Who Do Not Use AI Yet

Start now:

Try GitHub Copilot (free trial)
Use ChatGPT/Claude to explain code
Start with simple tasks (tests, docs)
Gradually increase usage

For Those Who Already Use

Level up:

Learn advanced prompt engineering
Try complementary tools
Automate repetitive tasks
Share knowledge with the team

For Those Who Want to Stand Out

Become an expert:

Contribute to open-source AI tools
Create workflows and templates for your company
Document best practices
Train colleagues in effective AI use

Conclusion

The JetBrains survey data is clear: AI in software development is no longer optional. With 85% of developers using these tools regularly, those who do not adapt will fall behind.

Most importantly, it is not just about using AI, but using it effectively. The most successful developers combine AI tools with critical thinking, rigorous validation, and continuous learning.

If you are still hesitant, the time to start is now. The learning curve is smaller than you imagine, and the benefits are real and measurable.

To dive deeper into using AI in development, I recommend checking out the article on GitHub Copilot and AI Tools for Developers where we explore best practices for integrating AI into your workflow.

Let's go! 🦅

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