The Top AI Tool for Devs Isn't GitHub Copilot, New Report Finds

Key Concepts

• AI adoption among developers
• AI tools for coding assistance (Cursor, GitHub Copilot, etc.)
• Impact of AI on developer productivity
• AI's role in the software development lifecycle (SDLC)
• AI's influence on junior developer roles
• Essential skills for developers in the age of AI (critical thinking, architecture)
• Managing AI agents
• Measuring the impact of AI
• Ethical and security concerns related to AI

Scott Kerry's Journey to Lead Dev

Scott Kerry, currently the editor-in-chief of Lead Dev, entered journalism a decade ago without a specific beat in mind. He initially covered corporate travel, writing about airline loyalty points for Business Traveler magazine. He transitioned to technology journalism after observing the startup boom in London and Silicon Valley. His first role in tech was at Tech World, covering the UK startup scene. He then moved into the enterprise SaaS space, covering companies like Salesforce, SAP, and Oracle, which led to his interest in software engineering. He later joined Info World, focusing on developers and writing about topics like Heroku, Docker, and Kubernetes. Three years ago, he joined Lead Dev to build out the media side of their business, which focuses on helping software engineers improve their management and leadership skills.

AI Impact Report 2025: Topline Findings

Lead Dev conducted a survey (sample size of nearly 900) to assess the impact of AI on developers. The report, "AI Impact Report 2025," revealed that AI usage among developers is nearly ubiquitous, with only 2% of respondents having no plans to use AI in their development process. The report aimed to understand how AI has changed team building, time allocation, and job satisfaction for engineers.

AI Adoption Rate

• Ubiquitous Usage: Only 2% of developers surveyed have no plans to use AI.
• Outliers: The 2% who aren't using AI likely work in small teams with significant influence or in heavily cautious organizations with legal/security concerns.

Popular AI Tools

• Cursor: The most popular coding assistance tool.
• GitHub Copilot: Second most popular, potentially neck-and-neck with Cursor if the survey question were refined.
• Models: Codeex, Gemini, and other models that developers tweak for their needs.
• Challenger Tools: Windurf, Jet Brains, Tab 9, Devon, and Augment have smaller market share despite being vocal in the market.
• Amazon Q: Surprisingly low adoption rate (2%) given Amazon's investment.

Investment in AI Tools

The majority of AI investments are happening at the code editor or model level. Some organizations are heavily investing (over $100,000 per year) in security and observability tools, reflecting a skew towards professional development in larger organizations. Smaller organizations are giving teams credit cards to experiment with different tools.

Investment Allocation

• Code Editor/Model Level: Majority of investments.
• Security and Observability: Significant investments (>$100k/year) by some organizations.
• Smaller Organizations: Credit cards for experimentation.

AI Usage Beyond Coding

While 48% of respondents use AI for automated code generation, other areas like testing, QA, and IT operations have lower adoption rates. 32-39% use it for summarizing meetings, writing documentation, content research, and learning new concepts. Code reviews (17%), fixing bugs (11%), data analysis, testing, QA, and IT operations have even lower usage.

AI Applications

• Automated Code Generation: 48%
• Non-Coding Tasks: 32-39% (summarizing meetings, documentation, research)
• Code Reviews: 17%
• Fixing Bugs: 11%
• Testing/QA, IT Operations: Low adoption

Impact on Productivity

59% of developers reported feeling at least a little more productive with AI, but 26% were unsure. This uncertainty is linked to the difficulty in measuring engineering productivity and the lack of clear metrics to evaluate AI's impact.

Productivity Perceptions

• More Productive: 59%
• Unsure: 26%
• Challenge: Measuring the impact of AI on productivity.

Impact on Junior Developer Roles

Respondents expect fewer available jobs for junior engineers due to AI. This raises concerns about how junior engineers will gain the experience needed to become senior engineers.

Job Market Impact

• Fewer Junior Roles: Expected by respondents.
• Concerns: How to develop senior engineers without junior roles.

Essential Skills for Developers

Critical thinking was identified as the most necessary skill for developers going forward, followed by architectural knowledge. This reflects a concern that AI tools may hinder the development of critical thinking skills and highlights the importance of designing and managing complex systems.

Key Skills

• Critical Thinking: Most important skill.
• Architectural Knowledge: Second most important.
• Managing AI Agents: A majority (60%) of respondents expect to focus on developing this skill in the next year.

Changes Since the Survey

If the survey were conducted today, the ranking of AI tools would likely shift due to the rapid pace of adoption and experimentation. The sentiment regarding the impact on junior developer roles would likely be even more negative. Optimism about AI would likely have increased until the launch of GPT-5.

Potential Shifts

• Tool Rankings: Would change due to rapid adoption.
• Junior Roles: Sentiment would be more negative.
• Optimism: Would have increased until GPT-5.

Biggest Challenge: Measuring Impact

The biggest challenge experienced with AI adoption is the lack of clear metrics to evaluate its impact on productivity and quality. This concern outweighs security, hallucinations, and legal concerns.

Top Challenge

• Lack of Metrics: To evaluate AI's impact on productivity/quality.

Scott Kerry's AI Usage

Scott uses AI for meeting summarizations, finding it useful for staying informed about meetings he can't attend. He resists using AI for transcription, believing that manual transcription is essential for journalists to fully understand their interviews and identify key quotes. He draws a parallel to engineering, where learning through pain and trial and error is crucial for developing expertise.

Personal AI Use

• Meeting Summarizations: Using Gemini for quick insights.
• Resists AI Transcription: Believes manual transcription is essential for journalists.

Conclusion

The AI Impact Report 2025 highlights the widespread adoption of AI among developers and its potential impact on productivity, job roles, and essential skills. While AI offers numerous benefits, challenges remain in measuring its impact, addressing ethical concerns, and ensuring the development of critical thinking skills. The rapid pace of change in the AI landscape requires continuous monitoring and adaptation.