Insights
Leadership Insights
Practical insights on the decisions AI has made harder, not easier. The chapters of Skin in the Runtime grew out of these; Contract Over Code is the operating model that followed; Cognitive Debt is the audit. The argument keeps moving here first.
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The insights behind the book, in chapter order
- 1 The Review Bottleneck: When AI Writes Faster Than Humans Can Own the Contract 15 min read
- 2 Cognitive Debt: What Teams Must Still Understand When AI Writes the Middle 12 min read
- 3 Measuring AI-Assisted Engineering: The Metrics That Matter (and the Ones That Lie) 16 min read
- 4 The Leadership-Grade Homelab: Why Tech Leaders Need Skin in the Runtime 11 min read
- 5 Agent Sprawl Is the New Shadow IT: Why AI Adoption Needs Platform Engineering 14 min read
- 6 Own Your Destiny: Why AI Makes Vendor Default Obsolete 11 min read
- 7 Leading Platform Migrations at Scale: $14M in Savings with Zero Downtime 10 min read
- 8 Leadership Lessons from Law Enforcement: Crisis Management for Technology Executives 17 min read
- 9 Designing Small End-to-End Teams that Ship the Whole Experience 7 min read
All insights
25 insights, newest first. Filter by the decision you are facing.
Can your team still own the contract when AI writes the middle?
Generation got cheap. Review and contract ownership did not. A 30-minute Monday scorecard for whoever owns delivery: eight claims, 1–5, two lowest rows, one owner and one gate this week.
Spinning Up a Local AI Lab: OpenCode, llama.cpp, and LoRA
A howto for a working local AI lab: one llama.cpp router serving specialist models, OpenCode as the control plane, and LoRA when an adapter earns a slot. Practice for owning the harness—not a chatbot toy.
The Token Bill Is the New Cloud Bill: When Nobody Owns Consumption
AI seats were a project. Agent loops are an operating cost. An executive model for token fat tails, hard-cap rationing, cost per completed owned outcome, and why platform teams—not a monthly invoice—must own the meter.
The Review Bottleneck: When AI Writes Faster Than Humans Can Own the Contract
AI did not remove the human constraint—it moved it from typing to review, and it thinned product ownership of the ask and the outcome. An executive model for review capacity, contract review versus style review, shipping too fast, and when to add gates instead of more senior reviewers.
The Leadership-Grade Homelab: Why Tech Leaders Need Skin in the Runtime
A production-shaped lab is not a hobby rack—it is deliberate practice that keeps technical judgment calibrated when enterprise stakes are high. Why tech leaders need skin in the runtime.
Own Your Destiny: Why AI Makes Vendor Default Obsolete
There is no better time to tackle tech debt and vendor decisions. AI and open platforms are making rented SaaS and contracted code optional again—if leadership still has skin in the runtime.
Cognitive Debt: What Teams Must Still Understand When AI Writes the Middle
Teams do not need to know every implementation detail when AI is a competent middle-layer partner. Cognitive debt is losing the durable high-level model—intent, boundaries, invariants, and failure modes—required to direct that partner and intervene when it is wrong.
Falling Behind Is a Choice: AI, Modern Frameworks, and the New Access Reality
Modern AI, cloud-native infrastructure, and production-grade frameworks are more accessible than ever. Leaders who are falling behind are usually facing an adoption problem, not an access problem.
Measuring AI-Assisted Engineering: The Metrics That Matter (and the Ones That Lie)
License counts and anecdotal speedups do not prove AI adoption is working. An executive framework for baselines, outcome metrics, review burden, platform-fit signals, and governance—so leaders know whether AI is changing economics or just accelerating the wrong architecture.
Technical Due Diligence for Acquirers and Boards: What a Real Technology Review Looks Like
Most technology due diligence is a tool checklist that misses integration risk. An executive framework for what actually predicts post-acquisition failure—cloud debt, delivery collapse, AI governance gaps, and platform maturity—and how to run a two-week review that informs the deal.
How Do Cloud Savings Disappear After Migration — and How to Keep Them
Cloud savings usually vanish within 18 months after migration, when the project team disbands. Ownership, FinOps, and platform guardrails keep the dividend.
The Fractional CTO Playbook: When and How to Deploy Executive Leadership Without Full-Time Overhead
When fractional technical leadership makes sense, how to scope engagements for measurable impact, and frameworks for onboarding, leverage, and lasting value—drawn from 25+ years of software, product, and executive leadership.
Agent Sprawl Is the New Shadow IT: Why AI Adoption Needs Platform Engineering
Agentic AI is moving from pilots into production workflows, creating a new form of shadow IT. Technical leaders need platform engineering discipline to manage AI agents with governance, context standards, validation, observability, and cost control.
LLMs Are Becoming a Commodity: Durable Advantage Comes from Workflow, Not Vendor
Leadership teams are over-focusing on branded AI tools and agent races. The real advantage comes from repeatable workflows, task-specific clients, operational leverage, and internal tooling shaped around your domain.
From Concept to Cloud: Building Enterprise Software at AI Speed
How AI-augmented development with expert leadership enabled building a cloud-native Git platform with enterprise features in record time.
The AI Workflow Revolution: Why Cursor and Claude Are Changing Everything
Five hard-won insights on integrating AI tools into development workflows. Why adoption is binary, why fundamentals still matter, and why nobody will be competitive without AI.
Designing Small End-to-End Teams that Ship the Whole Experience
A leadership-level perspective on building small, high-performing teams that fuse HCI, application, and infrastructure with local Docker workflows and Kubernetes validation.
Training Models with Models: Why Quality Labeled Data Beats Algorithm Sophistication
Using AI to train AI isn't just possible—it's becoming essential. But the real competitive advantage lies in purpose-built models and exceptional labeled data, not the latest architecture. Strategic insights on building AI that works.
Leveraging AI as a Strategic Advantage: From Workflow to Product
How technical leaders and engineers can integrate AI into both development workflows and products to maintain competitive advantage. Real insights on AI, ML, and agentic systems beyond the hype.
Embedded Firmware Development: Why Simplicity Wins in Critical Systems
Lessons learned from developing critical embedded firmware on ARM Cortex-M microcontrollers. Why bare-metal approaches often outperform RTOS and Linux-based solutions in reliability, real-time performance, and maintainability.
Leading Platform Migrations at Scale: $14M in Savings with Zero Downtime
How I achieved $14M in annual cost savings migrating from multi-cloud to AWS with zero business impact. Strategic planning, team building, and risk mitigation for large-scale platform migrations.
The Business Case for Platform Engineering: ROI Beyond Cost Savings
How to articulate the business value of platform engineering investments to executives. Cost optimization, developer productivity multipliers, and ROI measurement frameworks.
Building High-Performing Platform Engineering Teams
Practical insights on hiring, structuring, and leading platform engineering teams that deliver self-service infrastructure, developer productivity, and business value.
The Hard Path vs. The Easy Path: Why Native Mobile Development Wins Long-Term
Why cross-platform frameworks like React Native and Flutter look appealing but rarely deliver on their promises. Strategic insights on choosing native development and the iOS-first, Android-copy pattern that saves time and money.
Leadership Lessons from Law Enforcement: Crisis Management for Technology Executives
What 16 years as an auxiliary police officer taught me about leadership, crisis management, and decision-making under pressure. How law enforcement principles translate to technology executive leadership.