Notes on AI operating models in PE-backed software companies.
Private Equity · AI Strategy · Value Creation
First Principles
A working set of notes on how AI is changing software operations.
These essays focus on execution: engineering leverage, product economics, staffing, and operating discipline inside PE-backed technology businesses. The aim is practical. Describe the operating changes AI is introducing, the failure points that recur in diligence, and the decisions management teams have to make.
The AI Readiness Ladder
A practical sequence for AI adoption
A five-step framework for judging whether a company is ready to ship AI into production. In practice, boards ask about customer-facing AI before engineering teams have standard tooling, release discipline, or usable data. That mismatch produces delay, rework, and weak product outcomes.
Read Framework →Tools to Outcomes
How AI changes the economics of software
If your software only provides a tool, a competitor will provide the outcome—and take your customers. This piece examines what it looks like when software starts automating part of the customer's job, with a diagnostic rubric for evaluating where a company sits and what the hold-period implications are.
Read Framework →The Intensification Thesis
Why AI raises the value of high-agency employees
Give two software companies access to the same models and the advantage goes to the one with more high-agency employees across engineering, customer support, customer success, sales, and operations. That points toward fewer employees, higher pay per seat, and a higher internal bar for quality, productivity, and operating discipline.
Read Framework →Work in Progress
This site is early. The frameworks above are first drafts—I am actively revising and expanding them through May 2026.
Blackmere performs technical diligence, re-diligence, and AI-readiness assessments for PE-backed software companies.