AI is providing individual engineers with unprecedented leverage. One person can now deliver work that previously required an entire team.
But there is a key paradox that often gets overlooked: The engineers who will succeed are precisely those who built their skills within cross-functional teams, where they developed a deep understanding of security, infrastructure, business logic, and quality.
Organisations across Singapore and the broader Asia-Pacific region are rushing toward an AI-augmented future built on individual speed and output. Yet that future depends on deep collaboration across disciplines that many teams are abandoning.
Collaboration is the core of modern software development
The fundamental goal of DevSecOps is to establish a collaborative engineering culture that spans the entire software delivery lifecycle, centred on reusability and best practices that improve developer productivity and delivery efficiency. Organisations 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, de-risking software change management without sacrificing stability or security.
Most organisations stop here. They implement the processes, install the tooling, and measure the velocity improvements. But they miss the deeper transformation happening beneath the surface.
Turning individual work into shared intelligence
The collaborative model is fundamentally about learning and knowledge mastery at scale. Research in educational psychology, particularly Bloom’s taxonomy, suggests that people achieve the highest form of mastery through teaching concepts to others.
The dual-gate system reveals its deeper value when 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.
The result is a network effect where each person’s knowledge elevates everyone else’s capabilities. This collaborative culture fosters a learning organisation where every interaction creates teaching opportunities, enabling knowledge transfer and mastery development. This cross-functional expertise, internalised through years of collaborative interaction, sets certain engineers apart.
AI is a partner to humans, 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. AI handles lower-level work and redundant tasks, lowering cognitive load and freeing mental capacity for higher-order thinking, including analysis, evaluation, and creative problem-solving.
Recent findings from the GitLab Global DevSecOps Survey found that although 83% of DevSecOps professionals globally feel that 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 is emerging in executive circles. Some leaders believe AI agents can replace knowledge workers entirely, which shows a fundamental misunderstanding of how people develop expertise.
Even with highly capable AI, human experts remain essential to evaluate outputs across disciplines, establish trust in AI recommendations, and take accountability for production systems.
In fact, the research also found that 40% of DevSecOps professionals agree that AI will accelerate career growth for junior developers.
The argument that “we don’t need junior developers anymore” ignores the need for someone to review, validate, and be accountable for what AI produces. As junior developers write 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. Following that logic, 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 limiting factor: Not AI, but collective knowledge transfer
The real constraint isn’t AI capability, but rather the scarcity of people who can 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 it’s how people develop that breadth of knowledge. The team of one isn’t someone working in isolation. It’s someone who has internalised the collective wisdom of the cross-functional team and can operate with AI augmentation while maintaining human judgment and accountability.
Building AI-ready teams in Asia-Pacific
Across Singapore and the broader Asia-Pacific region, organisations face an important choice. The tempting path is to view AI as a way to cut costs by replacing senior talent with tools. This creates technical debt and fragile systems.
A more sustainable approach recognises that AI strengthens existing capability but cannot replace the cross-functional insights that come from real collaboration. The core paradox is that the very technology that enables engineers to operate independently makes collaborative learning more essential than ever.
In practical terms, this means treating code review, security checks, and architecture discussions as core knowledge-transfer processes. These interactions prepare engineers to operate as a “team of one” with AI augmenting their work. The only way to create people capable of effectively wielding those tools is through cross-functional knowledge transfer embedded in DevSecOps practices. Without this foundation, AI simply accelerates poor decisions.
Productivity, efficiency, and resilience still depend on people who know how to apply AI safely and effectively. The organisations that succeed are those that equip individuals to leverage AI effectively while providing a culture of continuous collaborative learning.
