Rene van Osnabrugge traces the evolution of the software crisis from the birth of software engineering to the rise of DevOps and the modern challenges that AI now addresses in the development lifecycle.

The Software Crisis Never Ended, It Just Evolved

Published by Rene van Osnabrugge on December 1, 2025

In 1968, in Garmisch, Germany, leading computing minds gathered at a NATO conference to discuss the growing crisis in software development—a crisis defined by unpredictable project outcomes, missed deadlines, and failed systems. Unlike physical engineering, software lacked standardized development practices, fostering chaos as its importance grew in governmental and organizational infrastructures.

From Waterfall to Agile and DevOps

Early interventions led to the discipline of software engineering and rigid structured lifecycle approaches such as the SDLC and Waterfall model. These methods created order but proved too inflexible as organizations faced frequently changing requirements.

Responses included iterative models (Spiral), risk management frameworks, and eventually Agile methodologies that humanized development and improved feedback cycles. The introduction of Scrum fostered business collaboration, and DevOps further unified development and operations, enabling end-to-end integration, automation, and accelerated delivery.

Modern Complexity and the Role of AI

Despite these advances, today’s problems have evolved. Teams grapple with ever-expanding complexity, cloud-native architectures, automation pipelines, monitoring, compliance, security, and infrastructure as code. The developer’s workload continues to grow as responsibilities multiply.

A key shift in the contemporary era is the emergence of artificial intelligence, especially large language models, as active participants in managing software complexity. AI can:

Absorb and interpret application and infrastructure context
Summarize technical decisions and automate documentation
Design and optimize CI/CD pipelines
Bridge communication gaps between roles and teams
Surface architecture and spot inconsistencies
Mine requirements and facilitate collaboration

Yet, engineering discipline remains essential; AI complements but doesn’t replace human judgment. AI enables scalability so that teams can redirect focus from overwhelming complexity to essential decision-making.

Conclusion

The original software crisis has become a challenge of complexity and overload. As software continues to grow in scope, the tools, processes, and practices evolve. With AI’s integration into the lifecycle, there is finally potential to more effectively manage this complexity. The journey isn’t about escaping the crisis but learning to outgrow it through smarter collaboration and technical innovation.

This post appeared first on “René van Osnabrugge’s Blog”. Read the entire article here