Engineering Alpha in Private Equity

Epizódy

• Engineering Alpha in Private Equity Trailer 01.05.2026 3min

  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)
• What's Engineering Alpha & Why You Can’t Just Rub AI on It 05.05.2026 28min

  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)
• Finding “Free EBITDA” in Cloud Contracts & The AI Optionality Playbook 14.05.2026 17min

  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.
• Uber COO questions tokenmaxxing 26.05.2026 6min

  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
• Measuring the ROI of Tech Transformations 28.05.2026 23min

  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.
• The AI Amplifier Effect & Why Rework is Killing Your EBITDA 04.06.2026 50min

  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.