Every conversation about AI in software delivery circles back to the same core idea: Give engineers better AI tools, and they’ll need fewer teammates. It’s a compelling, but misguided, narrative.AI has raised the bar for what a single developer can accomplish, while simultaneously raising expectations for what that developer must know. An engineer using AI to generate infrastructure code still needs to evaluate its security. One using AI for security scanning still needs to understand the business logic behind what’s being scanned.The more AI handles, the broader the judgment required to verify its output.“Better software won’t emerge from better tooling, but from better teams.”The organizations that benefit the most from AI investment will deliberately strengthen their collaborative processes, including cross-functional code reviews, knowledge-sharing opportunities, and structured mentoring, so that individual engineers develop the multidomain expertise AI demands but can’t provide on its own.Better software won’t emerge from better tooling, but from better teams.Collaborative groundwork drives successThe primary goal of DevSecOps is to establish a collaborative engineering culture that spans the entire software delivery lifecycle, from business strategy to technical implementation. This culture highlights reusability and best practices that directly strengthen developer productivity and delivery efficiency. Organizations achieve this through a dual-gate system:Human consensus-based code reviews ensure knowledge transfer and maintain quality standards across disciplines.Automated quality and security gates catch issues before they reach production.This approach balances speed with control. It mitigates risk in software change management while ensuring that acceleration doesn’t come at the expense of stability or security.Most organizations stop here. They implement the processes, install the tooling, and measure improvements in velocity. Still, they miss the more profound transformation happening beneath the surface.Systems for knowledge sharingThe collaborative model fundamentally enables learning and knowledge growth at scale. Research in educational psychology, particularly in Bloom’s Taxonomy of Learning, suggests that teaching concepts to others develops the highest level of competency.This is where the dual-gate system reveals its deeper value. Code reviews become structured knowledge transfer sessions. Each person operates as the knowledge expert in their domain while learning from adjacent domains:The security engineer reviewing code teaches secure development practices while learning about business requirementsThe architect understands product priorities while sharing knowledge about technical constraintsThe junior developer learns patterns from seniors while bringing fresh perspectives on toolingThis creates a network effect where each person’s knowledge elevates everyone else’s capabilities. Expertise flows in all directions across the organization. This collaborative culture creates a learning organization in which every interaction serves as a teaching opportunity and accelerates growth.“This is what sets certain engineers apart: They’ve internalized knowledge from adjacent domains through years of collaborative interaction.”When you view DevSecOps through this lens, code review becomes a teaching moment. Security scans offer a learning opportunity. Every interaction in the system enables knowledge transfer and expertise development. This is what sets certain engineers apart: They’ve internalized knowledge from adjacent domains through years of collaborative interaction.The self-sufficient developer: AI as an ally, not a replacementThe natural evolution of this collaborative model is the “self-sufficient developer,” a knowledge worker augmented by AI that enables unprecedented autonomy and efficiency. The promise continues to be compelling. Every engineer gains AI allies that handle lower-level work, such as remembering, understanding, and applying basic concepts. Teaching an agent to perform these redundant tasks dramatically lowers cognitive load, freeing mental capacity for higher-order thinking, including analysis, evaluation, and creative problem-solving.This is how AI can amplify human capabilities rather than replace them. Recent GitLab research found that although 83% of DevSecOps professionals believe AI will significantly change their role within the next five years, 76% agree that AI will create the need for more engineers, not fewer.However, a dangerous counter-narrative develops in executive circles. Some leaders believe competent AI agents can replace knowledge workers entirely. This represents a fundamental misunderstanding of how people develop expertise.Even with highly capable AI, you still need human experts who can:Evaluate outputs across multiple disciplinesEstablish trust in AI recommendationsProvide domain-specific judgmentTake accountability for production systemsIn fact, GitLab’s research found that 40% of DevSecOps professionals agree that AI solutions will actually accelerate career growth for junior developers.“The argument that ‘we don’t need junior developers anymore’ ignores the fact that someone still needs to review, validate, and take accountability for what AI produces.”The argument that “we don’t need junior developers anymore” ignores the fact that someone still needs to review, validate, and take accountability for what AI produces. Junior developers aren’t just writing code; they’re learning to evaluate it across multiple domains and build the judgment needed to verify AI outputs.The opposite argument, that AI might replace experienced architects and senior developers, proves equally problematic. This logic suggests we skip foundational learning entirely and restructure computer science education to focus only on prompting AI agents. But without understanding what good code looks like across security, infrastructure, and business domains, how would these graduates know whether AI outputs are correct? Both extremes miss the point.The critical gap: Insufficient collective wisdomThe primary constraint isn’t AI capability. It’s the lack of people who can actually operate as that “self-sufficient developer.” You need engineers with sufficient skills across multiple domains to effectively evaluate AI outputs in security, infrastructure, quality, and business logic. And you need educators who understand how to develop these multi-skilled practitioners.“The self-sufficient developer isn’t someone working in isolation.”The collaborative model from the original DevSecOps goal remains essential because it is the mechanism through which people develop breadth of knowledge. The self-sufficient developer isn’t someone working in isolation. Such individuals have internalized the collective wisdom of the cross-functional team and can now operate with AI augmentation while maintaining the judgment and accountability that only human expertise provides.The path forwardOrganizations face a critical choice. The tempting path views AI as a cost-reduction strategy, replacing expensive senior talent with cheaper tools and whoever can operate them. This path leads to brittle systems, technical debt, and ultimately failure.The sustainable path recognizes that AI operates as a tool that amplifies existing capability but cannot replace the judgment that comes from deep, cross-functional understanding.The companies that will win are those that double down on collaborative learning while simultaneously investing in AI augmentation. They understand that creating a self-sufficient developer requires first creating a team that teaches each individual across multiple domains. They recognize that the code review process provides the knowledge needed to use AI tools effectively. They invest in building knowledge-transfer systems that create engineers capable of operating autonomously by learning from the collective.“The companies that will win are those that double down on collaborative learning while simultaneously investing in AI augmentation.”This demonstrates the paradox of the AI age in software delivery. As our AI tools become increasingly capable, the value of collaborative learning becomes even more vital. The only way to create people capable of effectively wielding those tools is through the cross-functional knowledge transfer enabled by DevSecOps.The goal hasn’t changed. We still need to enhance productivity, improve efficiency, and reduce risk. What’s changed is our understanding that achieving those goals at scale requires both collaborative learning and AI augmentation, not a choice between them.The future belongs to organizations that build cultures where everyone teaches, everyone learns, and everyone becomes capable of operating as a self-sufficient developer when augmented by AI. Ultimately, the real competitive advantage isn’t AI; it’s the people who know how to apply it effectively.The post One developer, team power: The future of AI-driven DevSecOps appeared first on The New Stack.