Developer Productivity Engineering Blog

We are excited to announce that our company (Gradle, Inc.) will now present ourselves commercially as Gradle Technologies, effective immediately. This strategic update includes a new corporate logo and visual identity system designed to better reflect our evolution from a single-tool steward to a multi-product enterprise partner.

In the GenAI era, faster code creation doesn’t automatically translate into faster delivery, lower risk, or higher business value. In practice, the limiting factor is no longer how fast code is written—it’s how fast it can be validated, integrated, and released. This blog post explores exactly where GenAI is changing the variables and why pipeline acceleration has become a strategic imperative.

In the GenAI era, pipeline acceleration is no longer an optimization. It is a prerequisite for sustaining delivery performance and quality as change-volume scales. Organizations that leverage a Build Artifact CDN and treat build and test infrastructure as strategic systems (rather than utility plumbing) will out-learn and out-deliver those that do not. The alternative—investing in AI tools while retaining legacy build infrastructure—is the worst of both worlds: incurring the cost of AI adoption without capturing its productivity benefits.

If you’re not an IT security expert, then you may be astounded to learn that over 20,000 new software security vulnerabilities were disclosed in just the first half of 2025. Expert or no, new vulnerabilities that threaten your production systems and your software supply chain are increasing exponentially.

The software delivery ecosystem is currently at an inflection point, driven largely by the sheer volume of code generated by GenAI. This shift has magnified challenges for engineering organizations, leading to an explosion in code output, test volume, and overall build complexity, all of which have begun to overload CI pipelines. But there’s good news: The clear path back to CI efficiency is comprehensive caching.

A key theme and major challenge that emerged from DPE Summit 2025 is the Developer Productivity Paradox—developers are using Generative AI to crank out code faster than ever before, but somehow, the metrics aren’t showing an overall productivity improvement.Engineers feel more effective with AI, yet the flood of new code is overwhelming everything downstream, breaking stability and exposing systemic friction.

Did you know that over 20,000 new software security vulnerabilities were disclosed in just the first half of 2025? And since February 2025, the volume of disclosed vulnerabilities has increased by more than 3x.

Although AI is opening up vast opportunities for innovation, it’s also introducing new risks, and in some cases, being used to attack the software toolchain itself. As a result, we need a more comprehensive approach that extends far beyond traditional security measures. For DevOps and software supply chain professionals, understanding and implementing robust Governance, Risk, and Compliance (GRC) is becoming essential for protecting an organization’s most critical business systems.

Taking measurements first to establish a base state before attempting to improve performance applies not just to optimizing application performance in production, but to optimizing the entire developer experience. Let’s apply that powerful performance engineering mindset to our internal processes and our path to production.

When you put an application in production today, being able to observe its real-time behavior is a non-negotiable requirement. Yet this is rarely the case for our internal software systems that we use to build and release these same production applications.

I see a persistent and dangerous blind spot in the industry in overlooking the operational health of the software toolchain itself. We should view every component related to the build, tests, CI pipelines, local build systems, and DevOps tools—what I refer to collectively as the toolchain—as production systems.