Learning in the AI Era | Future of Software Development | Sonar Summit 2026

The Emergence of a Research-Driven Perspective

Dr. Cat Hicks, a psychological scientist and research architect, brings a critical lens to the software development industry at a pivotal moment. Rather than offering sweeping prescriptions about how AI will transform development, Hicks focuses on what actually matters: understanding how software teams learn and adapt in this new era. Her approach is rooted in empirical research conducted across thousands of developers spanning more than twelve industries, providing data-driven insights into what makes teams successful when facing unprecedented technological change.

The Collective Nature of Innovation

The shift toward agentic coding and AI-assisted development has created what Hicks describes as "a million tiny experiments" happening simultaneously across the industry. Rather than viewing this fragmented approach as problematic, Hicks sees it as the natural mechanism through which technological innovation occurs. The key insight is that individual experimentation only translates to meaningful progress when teams establish mechanisms for collective learning and knowledge sharing. This cumulative culture—where experiments are upleveled from individual to group level—is what historically drives technological advancement.

Redefining Culture Beyond HR Platitudes

Many technical leaders dismiss "culture" as a human resources concern, something to address once the technical problems are solved. Hicks challenges this separation, arguing that culture permeates all aspects of team performance and cannot be treated as supplementary. Her research measures "learning culture" specifically: whether developers feel supported to learn on their teams, whether they can have conversations with managers about learning needs, and whether they share goals with teammates. These seemingly intangible measures produce robust, measurable correlations with both reported productivity and team effectiveness.

Identifying Healthy Learning Signals

Rather than seeking a single metric, Hicks recommends measuring at least several indicators to identify healthy learning cultures. Red flags include industry-wide lip service to continuous learning without structural support, an assumption that learning happens automatically within software teams, and organizational hypocrisy where learning is praised but not resourced. Conversely, healthy learning cultures demonstrate psychological safety—what Hicks describes as "psychological affordances" that signal to team members that it is safe to learn, experiment, and fail in productive ways.

Practical Implications for Technical Leadership

The evidence is clear: scrappy teams with strong mutual support often outperform well-resourced organizations lacking learning culture infrastructure. Technical leaders should prioritize building environments where developers feel psychologically safe to pursue learning opportunities, where conversations about skill development are encouraged, and where the organization demonstrates through action—not just words—that learning is genuinely valued. In the AI era particularly, where the technical landscape shifts rapidly and new skills emerge constantly, these cultural foundations become even more critical to organizational success.

Key Takeaways

• Learning culture is measurable and predictive: Specific indicators of psychological safety and support for learning directly correlate with team productivity and effectiveness across industries
• Culture is not optional: Technical leaders must stop treating learning culture as supplementary and recognize it as core infrastructure for team success, especially during technological transitions
• Individual experiments require collective frameworks: Innovation emerges from collective learning and knowledge sharing, not isolated experimentation
• Psychological affordances matter: Teams succeed when members feel safe to learn, ask questions, and take productive risks without fear of consequences
• Current AI era demands intentional culture work: The rapid evolution of AI tools makes intentional investment in learning culture more critical than ever before