QCon London 2026: The Hidden Power of Boring Problems

2026-03-16 · Source: InfoQ · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

At QCon London 2026, Yinka Omole, Lead Software Engineer at Personio, presented on the "Hidden Power of Boring Problems," advocating for investing in foundational engineering problems over chasing new technologies. The talk highlighted a recurring historical pattern of predictions about the "end of programming," from FORTRAN in the 1950s to AI-generated code in 2025, yet the global developer population grew from 14 million in 2019 to 21 million by 2025. Omole argued that engineering expertise compounds when rooted in stable, underlying concepts like data modeling, reliability, and distributed systems, citing PostgreSQL's architectural resilience and WhatsApp's use of Erlang for high reliability. The presentation also cautioned against the risks of system rewrites, exemplified by Netscape, and demonstrated how simpler architectures, like Amazon Prime Video's shift from serverless to ECS, can significantly improve performance and reduce costs by 90%.

Key takeaway

For AI Architects and Software Engineers evaluating new technologies, you should prioritize understanding and solving fundamental engineering challenges over adopting every new framework. Your long-term career growth and project success will benefit more from mastering concepts like data modeling, distributed systems, and workflow orchestration, as these skills remain valuable even as tools like AI code generators evolve rapidly. Carefully assess if a new technology solves a genuine problem or merely follows a trend, conserving your "innovation tokens" for truly impactful advancements.

Key insights

Focusing on fundamental engineering problems yields more durable expertise than chasing transient technology trends.

Principles

• Expertise compounds on foundational problems.
• Simpler architectures often outperform complex ones.
• Innovation tokens are finite; spend them wisely.

Method

Identify recurring problem classes (e.g., workflow orchestration, state machines) that transcend specific tools and industries to build transferable knowledge.

In practice

• Prioritize correctness and transactional guarantees.
• Evaluate new tech for real problem-solving.
• Avoid full system rewrites when possible.

Topics

• Software Engineering Principles
• System Architecture
• Technology Adoption
• AI Code Generation
• Reliability Engineering

Best for: Software Engineer, AI Engineer, AI Architect

Related on AIssential

• 7 Ways New Engineers Can Flourish in the Age of AI — New engineers must integrate AI as a tool, not a competitor, by mastering fundamentals and developing higher-order skills.
• QCon AI Boston’s Early Program Focuses on the Engineering Work Behind Production AI — Production AI demands robust engineering for reliability, explainability, security, and scalability beyond initial demos.
• BugBash'26 Afternoon of Day 1 — LLMs make code cheap, but core software design, correctness, and reliable system engineering remain human-centric challenges.
• The Organization Is the Bottleneck — Organizational maturity and existing engineering foundations dictate AI coding agent success, not the tools themselves.
• Why Experience Matters Most in the AI Era — AI accelerates code generation but amplifies the need for human judgment and experience to prevent rapid accumulation of technical debt.
• An AI state of the union: We’ve passed the inflection point, dark factories are coming, and automation timelines | Simon Willison — AI has fundamentally reshaped software engineering, making code generation cheap and amplifying experienced engineers' capabilities.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by InfoQ.