Engineering Alpha in Private Equity

Paul Karner and Dave Mangot
  • 5.0 (2)
  • Investing
  • Updated Semiweekly

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. 1d ago

    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 firefighting production outages exploded by 70%. Corroborated by broad industry data from DX showing a precipitous drop in Developer Experience (DevEx) scores, Dave and Paul break down why "token-maxing" without human system ownership creates massive P&L liabilities. This episode outlines why "two-pizza teams" (5 to 8 engineers) remain the optimal operational unit, why replacing developers with agents starves your system of critical operational memory, and how outage firefighting actively destroys EBITDA. Key Takeaways: - The 220% vs. 36% Gap: Meta's internal experiment proved that generating code volume with AI agents does not translate to revenue-generating features. Commits skyrocketed 220%, but actual production shipping only grew 36%. - The 70% Firefighting Tax: Replacing developer oversight with autonomous agents caused technical incidents to spike 40% and developer firefighting time to surge 70%. Every hour spent firefighting outages in production is paid labor lost to reactive crisis management rather than value creation. - System Ownership Cannot Be Outsourced: You cannot outsource the operational responsibility of running a company to AI models trained on generic public code. When complex production systems break, human engineers who understand the architecture must be available to fix them. - The Return of the Two-Pizza Team: Meta’s research concluded that the ideal team size remains 5 to 8 engineers. Shrinking teams below this threshold creates severe on-call burnout, rotation failures, and institutional risk. https://leaddev.com/ai/meta-tried-to-shrink-engineering-teams-around-ai

    Meta's Experiment: Why You Can’t Outsource System Ownership to AI

  2. 6d ago

    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 invite gaming, and why automated productivity tools completely ignore essential "glue work"—the cross-team coordination and architectural alignment that keeps complex software systems standing. Drawing on Dr. Nicole Forsgren and Abbi Noda’s research in Frictionless as well as W. Edwards Deming’s classic management principles, Paul and Dave reveal why Operating Partners should stop chasing developer productivity and start optimizing Developer Experience (DevEx). By eliminating Lean waste — such as paying $180k engineers to sit around waiting 3 days for manual testing — operating teams can unlock massive financial leverage and drive sustainable EBITDA expansion. Key Takeaways: - Goldratt’s Law in Tech: "Tell me how you'll measure me, and I'll tell you how I will behave." Measuring developers by lines of code or story points only incentivizes teams to build bloated, unnecessary features faster. - The "Glue Work" Blindspot: As highlighted in Tanya Reilly’s seminal essay and talk Being Glue (https://www.noidea.dog/glue), the most valuable engineers on a team are often doing "glue work"—coordinating API contracts, establishing architecture standards, and aligning cross-functional teams. Vanity productivity tools rate these engineers poorly because they write fewer lines of code, creating toxic promotion incentives. - The Cost of Waiting (Lean Waste): Paying a $150k–$180k software engineer to wait 3 days for manual QA or deployment approvals is pure operational waste. Eliminating 3 days of waiting per developer across a portfolio company directly impacts the bottom line. - Deming’s Point #5 for PE: "If we improve the system, we reduce costs." Developer productivity shouldn't be measured in isolation; it must exist in service of building better products that customers eagerly pay for, with business metrics anchored right alongside engineering KPIs on the boardroom dashboard.

    The Developer Productivity Viper Pit: Why DevEx is Your Real EBITDA Driver

  3. Sep 1

    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 the story of how TZP Group — a lower middle market firm with over $2 billion in AUM—completely "burned their ships," ditched an expensive six-figure CRM, and built their own custom CRM in just two weeks using Claude. The conversation moves beyond the usual hype to dive into the hard operational mechanics of internal AI adoption. Jarrad explains how they built an interactive QSR rollup map that bypassed months of traditional sourcing to win a founder's trust in a live meeting, and shares why saving 30 hours on LBO modeling is a massive P&L win — not because you cut headcount, but because you unlock your team's analytical leverage. Key Takeaways: - The R and M Framework: How TZP Group maps every single AI initiative to a clear financial driver. It must either drive top-line Revenue (R, such as accelerating speed-to-lead from 3 minutes to 30 seconds to boost conversions) or optimize Cost and Margin (M, such as container-load optimization). - The "Burn the Ships" CRM Strategy: TZP Group was paying over six figures for a hosted cloud database that acted as a flat CRM. With a hard 30-day contract renewal deadline, they exported everything to Excel and built a custom "TZP CRM" in two weeks. The result? Annual costs plummeted from six figures to just $125 a month ($1,500/year run rate), while delivering a tool 4x to 5x more effective and custom-built for their deal workflows. - Sourcing Sourcing Sourcing (The QSR Map): Discover how TZP's deal team scraped franchise databases to build an interactive HTML target map. By identifying target coordinates and LLC ownerships, they presented a seller with 5 priority add-on locations, only to discover the operator was already secretly in contract on 3 of them—building instant, undeniable deal-table credibility. - The "30-Hour Analyst" Dividend: Shifting LBO modeling from Excel to a custom React/HTML template with slider toggles reduced a 3-day process to a few hours. Instead of "mission accomplished," this efficiency allows analysts to spend those 30 saved hours stress-testing unobvious structural variables—like interest rate spikes and tariff events—bringing massive analytical leverage and credibility to the investment committee.

    Engineering Alpha Internally: Burning the Ships and the '30-Hour Analyst' Dividend

  4. Aug 17

    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 a 5x increase in coding speed only yields a 15% overall efficiency gain, why Agile has created "bad habits" that are actively hurting AI deployments, and why the true P&L winners of the next 18 months will be the firms that "skate to where the puck is going" by preparing for distributed, edge-based AI. Key Takeaways: The 15x Fallacy: Coding only accounts for 10% to 20% of the software development lifecycle. Michael breaks down why focusing solely on developer speed misses the broader delivery bottleneck—and why systemic redesign of testing, deployment, and architecture is the only way to capture true bottom-line ROI. Agile's Bad Habits: The "iterate-as-you-go" Agile culture has made engineering teams sloppy. Michael explains why AI actually demands a "go slow to move fast" approach, requiring more holistic, upfront architectural design to prevent runaway token costs and unstable systems. The 1,000-Engineer Hackathon Playbook: How Michael got an entire enterprise organization engaged in AI, generating 200 projects and 24 viable product features in a single week by shifting the responsibility of "making time" directly onto his directors. The 18-Month Horizon: Why standard LLM wrapping is a shortsighted strategy. Michael outlines how the commoditization of software delivery will shift value to small language models, localized reasoning models, and proprietary data moats.

    The 15x Fallacy: Why Coding Speed Isn't Your AI Bottleneck

  5. Aug 5

    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

  6. Jul 28

    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

  7. Jul 9

    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

  8. Jun 29

    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
See All (17)

Ratings & Reviews

5
out of 5
2 Ratings

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
    17
  • Rating
    Clean
  • Copyright
    © 2026 Mangoteque LLC and Horizon Data Partners
  • Show Website
    Engineering Alpha in Private Equity