Engineering Alpha in Private Equity is a podcast about how software engineering and data science excellence create operational alpha. Hosted by Dave Mangot, the author of DevOps Patterns for Private Equity, who has worked with operating teams at Thoma Bravo, Hg Capital, and other top-tier PE firms. Co-hosted by Paul Karner, PhD, an economist with two decades inside PE-backed companies. Each episode explores the intersection of technology decisions and investment outcomes.
Engineering Alpha in Private Equity
Claim This Podcastby Paul Karner and Dave Mangot
5.0(1 reviews)
13 episodes
Updated Daily
Accepts GuestsHas Sponsors
Podcast Overview
Engineering Alpha in Private Equity is a podcast about how software engineering and data science excellence create operational alpha. Hosted by Dave Mangot, the author of _DevOps Patterns for Private Equity_, who has worked with operating teams at Thoma Bravo, Hg Capital, and other top-tier PE firms. Co-hosted by Paul Karner, PhD, an economist with two decades inside PE-backed companies. Each episode explores the intersection of technology decisions and investment outcomes.
Language
🇺🇲
Publishing Since
5/1/2026
1 verified contact email on file for Engineering Alpha in Private Equity
Pitch yourself as a guest, propose sponsorships, or reach out directly to the host.
Recent Episodes
August 5, 2026
Doing Math on Opinions: Why Bug Counts Aren't Science
Paul and Dave sit down with tech veteran and quality pioneer Elisabeth Hendrickson (former VP of R&D for Data Products at Pivotal). Together, they shatter one of the most common metrics used in private equity boardrooms: the bug count. Elisabeth explains why tracking and graphing bugs is actually just a statistical illusion—doing "math on human opinions" rather than measuring actual technical quality. The team discusses how quality directly impacts EBITDA through customer churn and outages, why your executive leadership team is your real "head of quality," and how automated testing serves as the ultimate guardrail to keep expensive AI agents from wrecking your codebase. Key Takeaways: The "Opinion Math" Trap: Why counting and graphing bugs to measure stability isn't science. Elisabeth reveals why these charts have zero impact on actual business outcomes and how they are easily manipulated. Quality is EBITDA: Quality is simply "value". If your portfolio company is suffering from high customer churn and frequent outages, they have a technical quality problem that is actively eroding your investment margins. The Executive Head of Quality: Real quality is determined by the systems designed by executive leadership (CTOs and VPs of Engineering), not by an isolated QA department manual-testing finished code. Keeping AI Agents Honest: AI agents have no real memory and will take shortcuts that break yesterday’s features. Robust, automated tests are the only way to keep agents honest and protect your codebase.
July 28, 2026
3 a.m., the 2x Productivity Cap, and the ceiling of AI Automation
In Episode 11 of Engineering Alpha in Private Equity, Paul Karner and Dave Mangot react to a recent Anthropic ad showcasing Claude Code automatically fixing a production bug at 3 a.m. While the ad promises a "find it, fix it, and ship it" utopia, Dave and Paul deconstruct why this is a dangerous fantasy for most mid-market PE-backed companies. They reveal why CTOs who have trained their engineering teams on AI are hitting a hard "2x productivity cap" — generating code faster, but failing to actually ship it. This episode is a roadmap for the actual systemic changes (like platform engineering and automated testing) required to break through that cap and deliver the financial ROI promised to the board. Key Takeaways: The 2x Productivity Cap: Why simply handing your engineers AI tools will cap out at 2x productivity. Without systemic redesigns, engineers just write code faster, piling up expensive, unshipped "inventory". The Hidden Prerequisites: Agents cannot fix your systems if they can't read your logs. The Anthropic ad accidentally proves that elite platform engineering (like robust Kubernetes environments) is the mandatory foundation for AI success. The Submarine Rule (Is it Safe?): Why you should never let an AI agent independently "find it, fix it, and ship it" to production. Elite engineering leaders treat AI like a submarine crew: the agent must propose a fix and explain why it is safe before a human authorizes the deployment. Delivering Board Promises: The foundational building blocks discussed in this episode are the exact investments required to get past the 2x plateau and actually deliver the EBITDA gains promised in the investment thesis.
July 9, 2026
The 85% Inventory Trap: What 28 Million Workflows Reveal About AI ROI
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/
13 total episodes available
Similar Podcasts
Discover related shows you might enjoy
Deep-dive analytics for Engineering Alpha in Private Equity
Frequently asked questions
Have a different question and can't find the answer you're looking for? Reach out to our support team by sending us an email and we'll get back to you as soon as we can.
- What is Engineering Alpha in Private Equity?
- How often does this podcast release new episodes?
This podcast updates daily.
- Where can I listen to this podcast?
This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
Information about guest appearances is not available.
Legal Disclaimer
Pod Engine is not affiliated with, endorsed by, or officially connected with any of the podcasts displayed on this platform. We operate independently as a podcast discovery and analytics service.
All podcast artwork, thumbnails, and content displayed on this page are the property of their respective owners and are protected by applicable copyright laws. This includes, but is not limited to, podcast cover art, episode artwork, show descriptions, episode titles, transcripts, audio snippets, and any other content originating from the podcast creators or their licensors.
We display this content under fair use principles and/or implied license for the purpose of podcast discovery, information, and commentary. We make no claim of ownership over any podcast content, artwork, or related materials shown on this platform. All trademarks, service marks, and trade names are the property of their respective owners.
While we strive to ensure all content usage is properly authorized, if you are a rights holder and believe your content is being used inappropriately or without proper authorization, please contact us immediately at hey@podengine.ai for prompt review and appropriate action, which may include content removal or proper attribution.
By accessing and using this platform, you acknowledge and agree to respect all applicable copyright laws and intellectual property rights of content owners. Any unauthorized reproduction, distribution, or commercial use of the content displayed on this platform is strictly prohibited.