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.

Выпуски

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

    -9 ч

    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/

    29 мин.
  2. The AI "Hoax," Economic Accounting, and Nobel-Winning ROI

    29 июн.

    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.

    13 мин.
  3. Token Economics, Data Moats, and the Future of Tech Due Diligence

    23 июн.

    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 Langanau 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.

    49 мин.
  4. Double-Speed Baseball and the new SaaS Multiple

    18 июн.

    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

    14 мин.
  5. The 'Maintenance Window' Red Flag & The AI Death Spiral

    11 июн.

    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.

    16 мин.
  6. The AI Amplifier Effect & Why Rework is Killing Your EBITDA

    4 июн.

    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.

    50 мин.
  7. Measuring the ROI of Tech Transformations

    28 мая

    Measuring the ROI of Tech Transformations

    How do you actually prove that a massive technology transformation is working? In Episode 4, we flip the script and dive into Paul Karner’s world of data science and causal inference. Using a real-world example of a private equity portfolio company that implemented machine learning to automate manual document review, Paul explains the critical process of tracking tech ROI. Dave and Paul break down why a tech transformation shouldn't just be a cost-saving measure, the nightmare (and necessity) of tying operational tech data directly to your ERP, and how to start with your financial end-goals and work backward. Key Takeaways: - The Cost-Savings Trap: Why undertaking a tech transformation where the only goal is cost savings usually leaves money on the table—or fails entirely. - Connecting the Silos: Why you must marry up operational data with your timekeeping and ERP systems to prove that a new tool is actually saving labor hours. - True EBITDA Leverage: How to avoid the "tokens versus humans" trap. Instead of just replacing headcount, learn how redeploying saved labor hours to scale output sends every additional margin dollar straight to the bottom line. - The 30-Day Playbook: Paul’s actionable advice for leaders currently in a transformation: start with a clear vision of how the project rolls up into the P&L, crack into the financial data, and work backward to find the gaps.

    24 мин.
  8. Uber COO questions tokenmaxxing

    26 мая

    Uber COO questions tokenmaxxing

    In this hot-take episode, Paul Karner and Dave Mangot analyze a massive red flag in the current tech landscape: Uber's COO recently admitted it's getting harder to justify the money spent on AI "token maxing," while the company is slowing hiring to fund these AI investments. Dave and Paul break down why high token consumption often just creates stranded "inventory" rather than revenue-generating features, and why cutting your engineering labor force before AI proves its actual ROI is a massive mistake. Key Takeaways: - The "Token Maxing" Illusion: Why an increase in AI token consumption does not proportionally translate into useful consumer features or revenue. - Inventory vs. Revenue: AI helps developers write more code, but it's getting stuck in feature branches. That code is simply "inventory," and you only make money when inventory hits production. - Protecting the Productivity Engine: Why the decision to slow headcount/labor to offset AI costs is deeply flawed if the AI isn't actually yielding the expected efficiency gains. - The Data-Driven Playbook: Why leadership must look at actual production metrics and token ROI before disrupting the underlying labor force. https://www.businessinsider.com/uber-coo-andrew-macdonald-ai-token-spending-harder-justify-2026-5

    6 мин.
  9. Finding “Free EBITDA” in Cloud Contracts & The AI Optionality Playbook

    14 мая

    Finding “Free EBITDA” in Cloud Contracts & The AI Optionality Playbook

    In this news review episode, we break down the recent wave of partnerships between major cloud vendors and private equity firms, starting with the Thoma Bravo and Google Cloud announcement. These partnerships highlight an immediate lever for value creation: enterprise cloud agreements that can drastically reduce operating expenses and instantly boost P&L. Beyond the immediate cost savings, we explore the strategic necessity of maintaining “optionality” in a highly uncertain AI landscape. We also issue a warning to CTOs: stop isolating your solutions architects in “innovation labs” and expecting new technology to fix broken systemic problems. Key Takeaways: - The “Free EBITDA” Play: Why your portfolio companies are leaving money on the table if they aren’t negotiating enterprise agreements with AWS, GCP, or Azure. Dave shares a real-world example of securing a 50% discount on internal bandwidth costs. - Why AI Optionality is King: In a highly volatile AI market, getting locked into a single LLM vendor is a massive risk. We explain why the best operational playbook involves using cloud platforms to access multiple models (like Anthropic, DeepSeek, and Gemini) to build a custom “race car”. - The Solutions Architect Trap: Why bringing in solutions architects to build a segregated “skunkworks” or innovation lab is a recipe for failure. - Tech Can’t Fix a Broken Org Chart: If your development team and your SREs report to different executives with misaligned incentives, no amount of AI or cloud architecture will help you hit your exit targets.

    18 мин.

  10. 5 мая

    What's Engineering Alpha & Why You Can’t Just Rub AI on It

    Welcome to the first official episode of the Engineering Alpha and Private Equity podcast! Hosts Paul Karner (economist and data scientist) and Dave Mangot (DevOps expert) break down what "Engineering Alpha" actually means for middle-market PE firms. Moving past the old world of financial engineering, Paul and Dave explore how true operational excellence within engineering organizations drives outsized returns and high EBITDA. They also tackle the elephant in the room: AI. They explain why it's a tool, not a magic product, and why failing to build the right foundations will amplify your problems rather than your profits. Key Takeaways: - Defining Engineering Alpha: Why optimization and efficiency inside the engineering organization are the true drivers of operational leverage and higher ROI. - The Deming Philosophy: How W. Edwards Deming’s statement that 94% of problems are systemic (and thus management's responsibility) applies directly to PE investors and C-suite executives. - AI Reality Check: Why AI is an amplifier for both good and bad processes, and why you shouldn't just mandate an "AI story" from the board without establishing the foundations in the DORA research. - The CircleCI Report: Exploring recent data showing that while AI helps developers write more code, it's often trapped in feature branches, has longer outages, higher customer churn, and negative ROI. - The New Valuation Metric: Why "agentic proficiency is the new SaaS multiple" and how building scalable foundations improves unit economics and drives higher valuations. Links & Resources Mentioned: - [DORA](https://dora.dev/) (DevOps Research and Assessment) State of DevOps Reports and the Accelerate book - [CircleCI Report](https://circleci.com/resources/2026-state-of-software-delivery/) on continuous integration tests - Nick Lichtenberg's [Fortune interview](https://fortune.com/2026/04/28/tech-layoffs-ai-disruption-corporate-america-doesnt-one-silicon-valley-ceo-knows-why/) with the CEO of Box - [Agentic Proficiency - The New Premium SaaS Valuation](https://blog.mangoteque.com/blog/2026/04/15/agentic-proficiency-the-new-premium-private-equity-saas-valuation/) Podcast theme music by [J-KIND](https://soundcloud.com/jkind). Connect with [Paul](https://www.linkedin.com/in/pkarner/) and [Dave](https://www.linkedin.com/in/dmangot/) on LinkedIn to join the conversation. [Learn more](https://engineeringalpha.fm)

    28 мин.

  11. 1 мая

    Engineering Alpha in Private Equity Trailer

    Welcome to Engineering Alpha in Private Equity, the podcast about how software engineering and data science excellence create operational alpha in private equity. In this trailer, Dave and Paul discuss the founding thesis of the show and what listeners can expect. ## Topics covered - What "operational alpha" actually means - Who are Paul Karner and Dave Mangot - What you can expect from the show - The best ways to engage [Learn more](https://engineeringalpha.fm)

    4 мин.
  • Выпусков: 11

Об этом подкасте

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.

Информация

  • Автор
    Paul Karner and Dave Mangot
  • Годы выхода
    2 тыс.
  • Выпуски
    11
  • Ограничения
    Без ненормативной лексики
  • Авторские права
    © 2026 Mangoteque LLC and Horizon Data Partners
  • Сайт подкаста
    Engineering Alpha in Private Equity

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