Engineering Alpha in Private Equity

When major tech firms announce massive workforce reductions while simultaneously pouring billions into AI infrastructure, PE board members and Operating Partners are left asking: Are these layoffs driven by true AI productivity gains, or are companies simply reallocating budget to pay for AI CapEx? In Episode 21 Paul and Dave apply an economic lens to AI-era labor market disruptions. They deconstruct the mechanics of corporate layoffs—contrasting direct labor substitution against budget-shifting ("machete-style" cuts to fund AI infrastructure) and Jevons' Paradox (where demand for specialized talent rises while traditional roles are displaced). Most importantly for Private Equity deal teams, Paul outlines how to spot "AI-washing" during M&A due diligence. He explains why treating generic headcount reduction as an "AI efficiency story" is a dangerous trap: one-off fat cutting does not create a durable, recurring flow of EBITDA expansion, and overpaying by applying an expanded AI valuation multiple to one-time cost cuts destroys deal returns. Key Takeaways: - Substitution Effect vs. Budget Reallocation: Not all AI-era layoffs stem from automated productivity. Megacap and growth firms often make broad cuts across non-AI divisions simply to free up cash flow for massive AI infrastructure and token OPEX budgets. - Jevons' Paradox; Organizational Complementarity: Labor market disruption and hiring expansion coexist. Indiscriminate headcount cuts risk destroying organizational complementarity—slicing out the "glue work" and domain knowledge required to run portfolio companies effectively. - Discerning "AI-Washing" in Deal Underwriting: During M&A due diligence, deal teams must probe whether projected headcount savings stem from a proven, repeatable AI workflow or basic "fat cutting." - The Valuation Multiple Trap: Basic fat cutting has always been a standard PE value creation lever. Slapping an "AI story" onto generic cost reduction does not turn a one-off savings into a durable EBITDA engine, and boards should be extremely wary of applying higher valuation multiples under the guise of AI.

How are elite Private Equity firms restructuring their internal Value Creation and Portfolio Operations teams to capture AI returns? In Episode 20 Paul and Dave welcome Dante Sacco, Head of Network at OneGuide, to deconstruct the findings from OneGuide’s State of Portfolio Ops Report 2026 (analyzing 60+ top PE/growth equity firms and 30+ value creation leaders). Dante reveals a dramatic shift in PE org charts: the percentage of PE firms with dedicated AI ops titles jumped from 5% to 24% year-over-year. More importantly, 83% of these hires are deeply technical—with firms deploying "Forward Deployed Engineers" (technical ICs) directly into PortCos rather than relying on generalist strategy consultants. Dante, Paul, and Dave discuss why 51% of PE firms see major functional efficiency gains in a majority of PortCos, yet only 6% are capturing 10%+ EBITDA lift—highlighting the critical gap between license adoption and true system/SDLC redesign. Key Takeaways: - The Rise of the AI Ops Title (5% to 24%): Dedicated AI roles inside PE value creation teams expanded 5x year-over-year. Tech-titled ops roles are now the single most common functional specialist position in PortCo ops teams—outnumbering both Go-To-Market and Finance specialists. - The Shift to Forward Deployed Engineers (FDEs): 83% of new AI ops hires have technical backgrounds (36% technical leaders, 45% technical individual contributors). PE firms are deploying hands-on engineers into PortCos to execute systems integration, not just write high-level strategy decks. - The EBITDA Capture Gap (51% vs. 6%): 51% of PE firms report that over half their PortCos see functional efficiency gains from AI (led by software engineering). However, less than 6% of firms have achieved a 10%+ EBITDA or revenue lift across the majority of their portfolio—proving that handing out LLM licenses without fixing underlying processes caps financial ROI. - Data Readiness is the #1 Blocker: Surveyed PE value creation leaders ranked data readiness and data quality as the #1 obstacle to AI value creation, followed by finding verified technical talent. - Standardized Playbooks & Diagnostic Screeners: Dante Sacco outlines how leading PE ops teams are scaling impact across PortCos using standardized AI modernization screeners, tech-stack rubrics, and shared vendor frameworks. Referenced Report: OneGuide - State of Portfolio Ops Report 2026: https://www.askoneguide.com/report/ OneGuide newsletter: https://www.askoneguide.com/resources/

When generative AI accelerates code creation by 5x or 10x, writing code ceases to be the organizational bottleneck. Every other stage of the Software Development Life Cycle (SDLC) gets severely strained. In Episode 19, Paul and Dave break down OpenAI’s internal "Agentic Software Factory" model, as reported by Gergely Orosz in [The Pragmatic Engineer](https://newsletter.pragmaticengineer.com/p/openai-software-factory). Dave and Paul contrast OpenAI’s practical, systems-engineered framework against Anthropic’s aspirational guidelines. They reveal how OpenAI deploys specialized, domain-specific review agents (security, cloud infrastructure, data) to unblock code review queues. Furthermore, they analyze the financial and operational mechanics of production monitoring: while performance-tuning agents can optimize code to run workloads on 50 servers instead of 500, slashing cloud OPEX directly into EBITDA expansion, live production outage management strictly enforces human-in-the-loop oversight to prevent catastrophic revenue loss. Key Takeaways: Unblocking the Post-Code Bottleneck: Accelerating code generation without scaling code review creates massive feature-branch inventory. OpenAI solves this by orchestrating specialized review agents (Data, Security, Infrastructure) supervised by a master agent to deliver instant architectural feedback without adding headcount. Direct EBITDA Expansion via Automated FinOps: OpenAI’s performance optimization bots analyze live workloads and propose code efficiency fixes. Running the same software on 50 servers instead of 500 slashes cloud infrastructure OPEX, directly expanding portfolio company EBITDA margins. Human-in-the-Loop Production Safety (Hadfield's Law): While pre-production workflows are heavily automated, live production outage bots do *not* autonomously push fixes to live environments. Adhering to Hadfield's Law ("there's no problem so bad that you can't make it worse"), outage bots act as investigative assistants for human engineers, protecting top-line revenue and preventing customer churn. SDLC Automation in the Large: Scaling software development with AI requires treating delivery pipelines like automated server patching: moving from manual, low-volume reviews ("in the small") to continuous, agent-driven orchestration ("in the large").

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 the crucial difference between rigid tactical goals and overarching strategic aims. Paul and Dave explore how elite software companies use AI not just to write bulk code, but to dramatically accelerate product discovery experiments in partnership with product management. Drawing on frameworks from Marty Cagan (Silicon Valley Product Group), Teresa Torres, Ant Murphy, and Marc Benioff’s Salesforce V2MOM model, this episode provides listeners with a blueprint for aligning engineering output directly with hold-period EBITDA expansion. Key Takeaways: Deming’s Warning on Aims vs. Goals: "We translate aims to goals and then we do stupid things in the name of the goal." When management incentivizes arbitrary outputs (e.g., commit counts or strict deadlines), teams optimize for the metric rather than the business aim, resulting in costly fire drills, unmonetized code inventory, and burned-out staff. AI as an Experimentation Engine: The highest ROI on AI code generation comes from enabling rapid product discovery experiments. Instead of wasting months of developer capacity on unvalidated features, AI allows teams to test market hypotheses in days—provided continuous delivery (CD) pipelines are in place. Learning vs. Shipping (Ant Murphy’s Dilemma): If an engineering organization cannot ship software continuously, market learning stalls. Unblocking delivery bottlenecks shifts the competitive constraint from engineering capacity to market learning velocity. Cascading Alignment via Salesforce V2MOM: How Marc Benioff’s V2MOM framework ensures that every individual contributor—from senior architects to junior devs—understands how their daily work directly supports the CEO's top-line vision and the board's financial targets. Dynamic Incentives Across the Hold Period: Value creation aims evolve over the investment lifecycle—from foundational platform cleanup early in the hold period to margin optimization pre-exit. Management must dynamically adjust system incentives rather than enforcing static KPIs. Referenced Resources: - Ant Murphy’s Essay about Would you rather be able to learn faster than you can ship, or ship faster than you can learn? (https://www.antmurphy.me/newsletter/fix-delivery-first) - Marty Cagan / Silicon Valley Product Group (SVPG) - Teresa Torres: Continuous Discovery Habits (https://a.co/d/033dZekt)

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 Jeff Bezos’s concept of "undifferentiated heavy lifting," Dave and Paul outline why code generation is only a fraction of the true cost of software ownership. They analyze real-world case studies—including how one portfolio company built a custom database engine that slashed COGS by 70%, while TZP Group built a custom CRM for $125/month, against the hidden operational taxes of 24/7 on-call rotations, uptime SLAs, and testing for data correctness. Key Takeaways: * The "Undifferentiated Heavy Lifting" Test: AI makes writing code fast, but it doesn't eliminate operational overhead. If a software tool doesn't create a defensible competitive moat, building it internally distracts valuable engineering capacity from core revenue-generating features. * The Hidden On-Call Reliability Tax: Building software means owning software. Maintaining 24/7 uptime for critical applications requires at least a 3-person on-call rotation to prevent developer burnout and avoid catastrophic production downtime (where 10 minutes of downtime can burn $100,000 in revenue). * Proving System Correctness: AI agents generate code quickly, but "hope is not a strategy". Systems processing financial data or high-volume transactions must prove 100% data correctness over time to prevent silent, compounding revenue leakage. * High-Leverage Custom Builds (The 70% COGS Slash): Building custom software *is* worth the investment when it directly transforms unit economics—such as replacing an off-the-shelf database to cut infrastructure COGS by 70% or replacing overpriced, flat SaaS databases with tailored internal workflow tools. * The Future of Enterprise SaaS: Dave presents his hot take on why SaaS vendors will soon offer deep, AI-driven customization to prevent clients from building homegrown alternatives.