Engineering Alpha in Private Equity

Paul Karner and Dave Mangot
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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.

  1. 2 days ago

    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.

    Doing Math on Opinions: Why Bug Counts Aren't Science

  2. 28 Jul

    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.

    3 a.m., the 2x Productivity Cap, and the ceiling of AI Automation

  3. 9 Jul

    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/

    The 85% Inventory Trap: What 28 Million Workflows Reveal About AI ROI

  4. 29 Jun

    The AI "Hoax," Economic Accounting, and Nobel-Winning ROI

    In Episode 9 Paul Karner and Dave Mangot tackle a recent Fortune interview (https://fortune.com/2026/06/21/nobel-laureate-daron-acemoglu-ai-productivity-capitalism-democracy/) with Nobel Prize-winning economist Daron Acemoglu, who argues that the massive productivity gains promised by AI are harder to achieve than presumed. Paul puts on his PhD economist hat to break down what this skepticism means for private equity deal teams trying to manage their AI budgets. He introduces the concept of "economic accounting"—understanding the counterfactual of what a company could achieve without AI simply by adopting solid engineering foundations. For operating partners, the takeaway is clear: preparing a portfolio company for AI requires cleaning up data, mapping workflows, and establishing guardrails. Even if the AI hype is overstated, doing this foundational work will inherently make the company more profitable. Key Takeaways: The AI "Hoax" Analogy: Preparing for AI forces organizations to implement best practices. Even if AI doesn't yield AGI-level miracles, those foundational improvements directly increase EBITDA and profitability. Economic vs. Financial Accounting: CFOs must look at the "counterfactual": evaluating what productivity gains are actually coming from the AI versus what gains are just the result of getting the company's operational house in order. Ending the Token Free-For-All: Moving from a subsidized "token-maxing" phase to a mature operating model requires pointing a sustainable budget only at areas where AI truly creates unique value. Multiple Expansion: Deal teams that stop blindly "rubbing AI on everything" and strategically direct dollars toward genuine tech efficiencies will see the results directly in their exit multiples.

    The AI "Hoax," Economic Accounting, and Nobel-Winning ROI

  5. 23 Jun

    Token Economics, Data Moats, and the Future of Tech Due Diligence

    In Episode 8, Paul Karner and Dave Mangot are joined by Dan Bender, Kirby Montgomery, and Jason Langenauer from the global tech due diligence firm Code & Co. (https://www.codeandco.com/) The team breaks down how the rise of AI has fundamentally changed the diligence process for private equity investors. The conversation shifts away from the hype of AI and dives straight into the P&L consequences of "token economics". The Code & Co. team explains why blindly throwing AI at a problem will wreck a software company's gross margins, why proprietary data is the only genuine moat left in the age of commoditized coding, and why a CTO's cultural skepticism toward AI is now considered a material investment risk. Key Takeaways: The Death of Zero Marginal Cost: Why the traditional SaaS model (where adding a new user costs almost nothing) is dead if your portfolio company is burning through expensive LLM tokens for every transaction. Token Economics & P&L: How to prevent margin erosion by matching the right AI model to the right problem (e.g., using a fraction-of-the-cost model like Haiku for basic tasks instead of the most expensive models). The Data Moat: Software development is becoming commoditized; clean, proprietary data is the only true competitive advantage that competitors cannot replicate. The 12-Month Sell-Side Shift: Why operating partners need to shift their sell-side tech diligence left, looking at the plumbing 12 months before going to market to build a convincing narrative around AI defensibility.

    Token Economics, Data Moats, and the Future of Tech Due Diligence

  6. 18 Jun

    Double-Speed Baseball and the new SaaS Multiple

    In Episode 7, Paul Karner and Dave Mangot unpack a core thesis of the show: why "agentic proficiency" is the modern equivalent of the SaaS multiple. While the ultimate goal of any portfolio company remains the same — satisfying user needs to generate revenue and EBITDA— the mechanics of how we deliver that value have fundamentally changed. Dave and Paul break down how AI agents drastically lower the marginal cost of delivering software and why achieving daily deployment is the absolute prerequisite for Product-Led Growth (PLG). The message is clear: if your portfolio companies aren't using agentic workflows to get more "at-bats" in the market, they are going to be left behind by the compounding advantages of elite performers. Key Takeaways: The New Valuation Lever: Just as the industry previously rewarded the shift from legacy architectures to the cloud with massive SaaS multiples, the next wave of outsized exit multiples will go to organizations that master agentic proficiency. Unblocking Product-Led Growth (PLG): You cannot execute a successful PLG strategy if your ability to ship software is slower than your ability to learn what the market wants. Agentic proficiency removes the shipping bottleneck, allowing product teams to iterate daily. Double the "At-Bats": If two portfolio companies are competing to generate EBITDA and revenue, the agentically proficient company gets twice as many opportunities to deploy revenue-generating features and capture market share. The Compounding Advantage: Getting 1% better every day through continuous, agent-assisted shipping creates a compounding effect. This operational leverage causes elite organizations to drastically diverge in valuation from competitors who are merely trying to bolt AI onto legacy systems. https://www.antmurphy.me/newsletter/fix-delivery-first

    Double-Speed Baseball and the new SaaS Multiple

  7. 11 Jun

    The 'Maintenance Window' Red Flag & The AI Death Spiral

    If a software company still uses "maintenance windows" to release updates, it is a glaring operational warning sign. In this explainer episode, Dave and Paul break down why maintenance windows indicate a broken software delivery culture that relies on subjective feelings rather than automated data. For private equity operating partners evaluating a new acquisition or monitoring a portfolio company, Dave explains why legacy practices like Change Advisory Boards (CABs) actually decrease stability. More importantly, the hosts reveal why trying to force AI tools into an organization that deploys slowly will create a margin-crushing "death spiral" of dual costs. Key Takeaways: - The Legacy Tech Tax: Why maintenance windows signal that a company lacks automated testing and relies on subjective measures rather than objective facts. - The AI Death Spiral: If you use AI to generate 10x more code, but only release during scheduled windows, you are paying for AI tokens and paying engineers to perform massive amounts of rework when those giant batches fail. - The CAB Illusion: Why Change Advisory Boards (CABs), often used for compliance in highly regulated industries, are actually inversely correlated with software stability. - Killing Product-Led Growth (PLG): You cannot execute a PLG strategy without running continuous, daily experiments to see what customers want. Maintenance windows actively choke off this growth engine.

    The 'Maintenance Window' Red Flag & The AI Death Spiral

  8. 4 Jun

    The AI Amplifier Effect & Why Rework is Killing Your EBITDA

    In our first dedicated research episode, Dave and Paul are joined by Nathen Harvey, who leads the DORA (DevOps Research and Assessment) program at Google Cloud. For over a decade, DORA has proven that elite software delivery performance is a leading predictor of organizational performance, including profitability and market share. In this episode, we unpack DORA's latest findings on Artificial Intelligence to show operating partners exactly why "rubbing AI" on a portfolio company won't work without the right foundational capabilities. Nathen breaks down the dangerous reality of AI as an amplifier: if you speed up code generation but ignore your existing bottlenecks, you will actually reduce your overall output.

    The AI Amplifier Effect & Why Rework is Killing Your EBITDA
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About

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.

Information

  • Creator
    Paul Karner and Dave Mangot
  • Years Active
    2026
  • Episodes
    13
  • Rating
    Clean
  • Copyright
    © 2026 Mangoteque LLC and Horizon Data Partners
  • Show Website
    Engineering Alpha in Private Equity