Build vs. buy
Capability fit, lifecycle cost, switching risk, integration, internal talent, vendor concentration, and the value of control.
Hands-on AI · October 4, 2026
Building with AI
I’m a founder-operator and CFO with 25+ years of experience building businesses and improving how they work. I’m also a tech-minded nontraditional coder, using AI to build websites and apps, explore business automation and develop a multi-agent working environment.
What interests me is the ability to turn an operational problem into something I can test myself. I bring the business context, define what a useful result looks like and work with AI to build toward it.
I’ve recently added OpenAI Dots to my setup and named my dot TRON 😏. I’ve positioned TRON as an always-on AI Chief of Staff above my DorkOS multi-agent platform, helping coordinate work while local agents execute defined tasks within available access and approval boundaries.
The setup is still evolving. I’m documenting the practical work, including what succeeds, what needs testing and where human judgment remains essential, in BlackBoxx.ai Lab Notes.
Technology-finance depth
Architecture and economics belong in the same room.
Karl evaluates technology through business requirements, control design, total cost of ownership, adoption, and strategic flexibility. Build-vs-buy is rarely a binary choice; the real decision is which capabilities must remain differentiated, which can be standardized, and where integration risk moves.
Technology ROI
Benefits tied to cycle time, quality, capacity, risk, decision speed, and cash—not only licensing cost or claimed hours saved.
Cost allocation
Cost-center, shared-service, and internal-recharge designs that clarify consumption without creating counterproductive behavior.
Security and customization
Authority, data exposure, maintenance, auditability, and exception handling considered with user value and implementation speed.
Executive education · MIT Sloan
Strategy before automation.
Karl completed the MIT Sloan School of Management Executive Education program Artificial Intelligence: Implications for Business Strategy in March 2024.
His final project applied the program’s frameworks to an AI transformation roadmap for Preserve, connecting the current state, proposed initiative, implementation scope, stakeholders, technical considerations, action plan, and measurable outcomes.
Applied AI
Governed automation, not automation theater.
Through BlackBoxx.ai, Karl architects and pressure-tests practical AI systems: specialized agents, approval paths, automation, model and application integration, validation controls, and human-review checkpoints.
As an Apple Developer, he has multiple iOS applications in development and uses AI-assisted coding, agentic development tools, and automation to turn business requirements into testable products. The work is hands-on, but the standard remains operational: working software, clear authority, observable behavior, and evidence that the system does what it claims.
His interest in edge and distributed intelligence follows the same logic. Moving inference closer to devices changes latency, privacy, bandwidth, lifecycle management, and governance; it does not eliminate infrastructure.
Construction finance automation
Automate the repetition. Keep the financial judgment.
Construction finance contains recurring, document-heavy workflows across invoices, commitments, change orders, job costing, WIP schedules, billing support, retainage, forecasting, and management reporting. Those workflows can benefit from bounded automation that prepares work, identifies missing information, applies documented rules, and routes exceptions.
The control standard remains explicit: consequential accounting, cash, contractual, and stakeholder decisions stay with an accountable person. KSIG Advisors' operating analysis explains where AI-enabled workflows can improve speed and visibility, where human judgment remains essential, and which controls should precede scale.