Breaking Changes Are Broken Semver

In this article, we address the most contentious parts of the SemVer standard to understand how you can trade off backward compatibility and upgradability with modernization and iterability. This TensorBlue analysis is based on reporting and source material from InfoQ (https://www.infoq.com/articles/breaking-changes-are-broken-semver/).

What Happened

InfoQ Homepage Articles Beyond API Compatibility: Understanding the Full Impact of Breaking Changes

Beyond API Compatibility: Understanding the Full Impact of Breaking Changes

The widespread use of SaaS APIs has exposed an inconsistent approach to handling major version updates and breaking changes.

API publishers primarily focus on API backward-compatibility when addressing potential disruptions.

Given that modern applications integrate various SaaS APIs, it is vital for API publishers to consider more than just basic API issues, including performance, dependency, wireformat compatibility, and more.

Failing to do so could lead to customers losing trust in versioning as a communication tool for changes, forcing API publishers to support older versions to support stragglers and further contribute to bad versioning.

The goal is not to achieve compatibility in every aspect, but to identify and clearly communicate the aspects that matter most to the customer segment you care about.

Seasoned software engineers are intimately familiar with the concept of versioning software releases. Versions are the cornerstone of API evolution and change management. Semantic Versioning (SemVer) has emerged as the universal standard for communicating and managing API changes. While most parts of semantic versioning have held up to the test of time, there’s one aspect that’s truly been challen

Implications for Product and Engineering Teams

For TensorBlue readers, the useful question is not just what happened, but how this changes product architecture, engineering priorities, AI delivery, observability, team workflows, or executive decision-making.

• Review whether this changes your AI roadmap, platform architecture, or engineering operating model.
• Identify the specific workflow, reliability, governance, or developer-productivity lesson that applies to your organization.
• Convert the lesson into a small production experiment with measurable quality, latency, cost, adoption, or risk metrics.
• Document source assumptions clearly so teams do not overgeneralize from incomplete public information.

TensorBlue Takeaway

The practical opportunity is to turn this signal into a concrete implementation decision: better AI systems, stronger product instrumentation, more reliable automation, and clearer technical governance. Teams that connect public technology shifts to their own delivery systems will move faster without adding unnecessary complexity.