The next wave of DevSecOps: Team of one, manager of many

AI is creating unprecedented leverage for individual engineers. Individual team members can now accomplish what once required entire teams. But here's the paradox everyone is missing: the engineers who will build these solo empires aren’t just expert coders. They've spent years in collaborative teams, absorbing knowledge across security, infrastructure, business logic, and quality assurance.

The software industry is racing toward a future of AI-augmented individual capability. Yet the foundation for this future is the very thing many organizations are abandoning: deep, cross-functional collaboration. Understanding this contradiction reveals the real role of AI in software delivery.

Collaboration as a foundation

The fundamental 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 centers on reusability and best practices that directly improve 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 de-risks 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 the velocity improvements. But, they miss the deeper transformation happening beneath the surface.

The knowledge transfer engine

The collaborative model is fundamentally about learning and knowledge mastery at scale. Research in educational psychology, particularly Bloom's Taxonomy of Learning, suggests that the highest form of mastery is achieved through teaching concepts to others.

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 requirements
• The architect understands product priorities while sharing knowledge about technical constraints
• The junior developer learns patterns from seniors while bringing fresh perspectives on tooling

This 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 fosters a learning organization in which every interaction creates opportunities for teaching and accelerated growth.

When you view DevSecOps through this lens, code review becomes a teaching moment. Security scans are a learning opportunity. Every interaction in the system enables knowledge transfer and mastery development. This is what sets certain engineers apart: They’ve internalized knowledge from adjacent domains through years of collaborative interaction.

The team of one: AI as a peer, not a replacement

The natural evolution of this collaborative model is the "team of one," a knowledge worker augmented by AI that enables unprecedented autonomy and efficiency. The promise is compelling. Every engineer gains AI peers that handle lower-level work, such as remembering, understanding, and basic application of 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 feel that AI will significantly change their role within the next five years, 76% agree that AI will actually create the need for more engineers, not fewer.

However, a dangerous counter-narrative is emerging in executive circles. Some leaders believe highly capable 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 disciplines
• Establish trust in AI recommendations
• Provide domain-specific judgment
• Take accountability for production systems

In fact, GitLab’s research found that 40% of DevSecOps professionals agree that Al 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. Junior developers aren't just writing code — they’re learning to evaluate it across multiple domains, building the judgment needed to verify AI outputs.

The opposite argument — that AI might replace experienced architects and senior developers — is equally problematic. This logic suggests we could 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 real constraint: Scarcity of collective wisdom

The real constraint isn't AI capability. It's the scarcity of people who can actually operate as that "team of one." 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 collaborative model from the original DevSecOps goal remains essential because this is the mechanism through which people develop that breadth of knowledge. The team of one isn't someone working in isolation. It's someone who has 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 forward

Organizations face a critical choice. The tempting path is to view AI as a cost-reduction strategy by 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 is a tool that amplifies existing capability but cannot replace the judgment that comes from deep, cross-functional mastery.

The companies that will win are those that double down on collaborative learning while simultaneously investing in AI augmentation. They understand that creating a team of one requires first creating a team that teaches each individual across multiple domains. They recognize that the code review process helps to transfer the knowledge needed to use AI tools effectively. They invest in building knowledge-transfer systems that create engineers capable of operating autonomously, having learned from the collective.

This is the paradox of the AI age in software delivery. As our AI tools become increasingly capable, the value of collaborative learning becomes even more pronounced. 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 improve productivity, increase 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 team of one when augmented by AI. Ultimately, the real competitive advantage isn't AI; it's the people who know how to effectively apply it.