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In Episode 10 of Engineering Alpha in Private Equity, Paul Karner and Dave Mangot dive into the hard data from the 2026 CircleCI State of Software Delivery Report, which analyzed over 28 million CI workflows. While the tech world is celebrating a 59% increase in code throughput due to AI, Dave and Paul reveal a massive P&L red flag: 85% of that new code is getting stuck in "feature branches".
This means the AI isn't generating operational alpha; it is generating expensive, unsold inventory. They break down why only the top 5% of elite engineering teams are actually pushing this code to production, why test failure rates are skyrocketing, and why companies are accidentally paying the equivalent of multiple full-time engineers just to debug AI errors.
Key Takeaways:
The Feature Branch Inventory Trap: Code stuck in a feature branch doesn't generate revenue. It is expended capital sitting as inventory. You only make money when that code ships to production.
The 30% Failure Tax: Because AI generates code so quickly, test success rates have plummeted from 90% down to 70%. For a high-throughput portco, that equals an additional hundreds of hours of debugging every year—the equivalent of many full-time engineers doing nothing but fixing AI mistakes.
Kill the Vanity Metrics: Boards must stop measuring "lines of code" or AI adoption rates. The bottleneck is no longer how fast developers can work; it is whether the underlying systems can keep up and safely deploy that work.
The Elite 5% Divergence: Only the top 5% of software teams have the foundational systems required to actually capture the promised ROI of AI, successfully shipping 25% more code to production.
https://circleci.com/resources/2026-state-of-software-delivery/
