Engineering Alpha in Private Equity
Engineering Alpha in Private Equity is a podcast that explores how software engineering and data science excellence create operational alpha in private equity. Hosted by Dave Mangot, author of DevOps Patterns for Private Equity, and co-hosted by Paul Karner, PhD, an economist with experience in PE-backed companies. Each episode examines the intersection of technology decisions and investment outcomes.
Episodes
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 sho
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 ou
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 expl
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
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
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 Int
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 comp
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 t
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
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 mea
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".
Thi
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 engineerin
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 qual
The 15x Fallacy: Why Coding Speed Isn't Your AI Bottleneck
In Episode 13, Paul Karner and Dave Mangot sit down with tech veteran Michael Frendo, the current CTO of New Relic (a Francisco Partners and TPG company) and former executive at Proofpoint (a Thoma Bravo company).
Michael strips away the hype of "vibe coding" and shares the hard operational realities of leading a thousand-person engineering organization through the AI revolution.
He reveals why
Engineering Alpha Internally: Burning the Ships and the '30-Hour Analyst' Dividend
How many private equity firms tell their portfolio companies to "innovate with AI" while their own deal teams are still drowning in manual Excel sheets and copy-pasting CRM data? In Episode 14 of Engineering Alpha in Private Equity, Jarrad Berman, Partner in TZP Group’s Portfolio Growth Group, joins Paul Karner and Dave Mangot to turn the operational excellence playbook on itself.
Jarrad shares t
The Developer Productivity Viper Pit: Why DevEx is Your Real EBITDA Driver
When Private Equity boards want to optimize engineering costs, they often fall into a dangerous trap: using software to measure "developer productivity" using lines of code, velocity points, or pull request wait times. In Episode 15 Paul and Dave navigate the "viper pit" of software measurement.
They break down why comparing velocity across teams is statistically meaningless, why vanity metrics
Meta's Experiment: Why You Can’t Outsource System Ownership to AI
When Private Equity boards attempt to capture AI returns, a common line item targeted is engineering headcount. In Episode 16, Paul and Dave analyze a recent LeadDev report detailing Meta’s attempt to shrink engineering teams around AI agents. While Meta saw a staggering 220% increase in code commits, only 36% made it into production, while technical incidents jumped 40% and time spent firefightin
The New Calculus of Buy vs. Build: Claude Code, Undifferentiated Heavy Lifting, and Slashing COGS
With generative AI tools making code generation faster and cheaper than ever, Private Equity deal teams and portfolio company executives are re-examining off-the-shelf SaaS software. Why pay six-figure SaaS subscriptions when an engineer can spin up a custom tool in days? In Episode 17, Paul and Dave re-evaluate the classic "Buy vs. Build" decision through an operational and P&L lens.
Revisiting
Rethinking Incentives: (AI)ms vs. Goals and the Economics of Experimentation
When PE backed companies attempt to drive AI R&D performance, they often set local targets—like story point velocity or code commit volume that inadvertently trigger perverse incentives and operational chaos. In Episode 18 we examine organizational incentives in the age of AI through an economic lens. Quoting W. Edwards Deming’s famous warning: "I achieved my goal, but not my aim", we break down t
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