Your AI Governance Policy Is Not Governance Your AI Governance Policy Is Not Governance I sat in a meeting last month where a CTO walked me through their AI governance framework. Forty-two slides. Acceptable use language, data classification tiers, a numbered list of prohibited applications. Legal had reviewed it. HR had signed off. The CISO looked
Two Failure Modes in LLM-Generated Pull Requests: Capability-Gap and Scope-Discipline Haircuts in a Controlled 216-Run Evaluation Two Failure Modes in LLM-Generated Pull Requests: Capability-Gap and Scope-Discipline Haircuts in a Controlled 216-Run Evaluation Abstract LLM-generated pull requests can fail mergeability review for two structurally distinct reasons. The first is a capability-gap haircut: the model cannot produce output that meets maintainer standards,
Cost-Adjusted Capacity: When Tokens Stop Converting to Mergeable PRs Cost-Adjusted Capacity: When Tokens Stop Converting to Mergeable PRs Most AI coding budgets optimize the wrong variable. They track token spend and model tier as if buying a bigger model and feeding it more tokens reliably produces more shipped software. The evidence says otherwise, and the gap between what
prompt-engineering Using Evaluation Data to Match Model Capability to Task Requirements Evaluation data from your prompt testing reveals which models actually work for your task. Using that data to choose models is more reliable than benchmark scores and more cost-effective than defaulting to the largest available model.
Finding the Causal Root: Measuring Value When No Single Model Owns the Outcome Accuracy doesn't equal value. Most AI projects fail due to misframed problems and missing measurement—focus on causal impact, not just model performance.
POST-AI Judgment Was Built by Proxy. AI Automated the Proxy. Activity was never a guarantee — people still hit plateaus. But it gave you the raw material. AI automated the proxy before we designed the replacement.
fulfillment Fulfillment Is Not Enjoyment Enjoyment fades the moment you chase it. Fulfillment requires the opposite — it lives on the other side of difficulty. The research is clear, and so is what to do about it.
When Small Mistakes Compound: Building Systems That Survive Imperfect AI Small AI mistakes can snowball into big problems when they ripple through automated processes. Learn how to build systems that catch errors before they compound and cost your business.
efficiency The Compounding Tax: Why Small Model Errors Become Large System Failures Small model errors compound rapidly in sequential tasks, turning seemingly reliable systems into failure-prone ones. Don't rely on per-step accuracy—trajectory reliability is what matters, and it degrades faster than you think.
The Machine Is Learning to Build Itself. You Still Have to Decide What to Build. AI is rapidly writing code – Anthropic's engineers now ship 8x more per quarter. But don’t panic; it highlights the crucial need to strategically manage AI adoption, following a proven maturity model.
Proving Competency at Every Stage: The Eight Stages of AI Maturity Feeling advanced and being advanced are different things. This is the measurement guide for each of the eight stages: what proof looks like, what gaps look like, and the signals that tell you whether you are where you think you are.
Before You Architect Anything, Agree on What the AI Actually Does AI projects often fail because teams misunderstand each other's vision. A shared classification system—a taxonomy and ontology—can ensure everyone agrees on what the AI *does* before building it.
labor-compression The Productivity Paradox: When AI Helps Everyone Equally AI boosts productivity most for junior workers, not experts—a surprising twist that reshapes how we think about knowledge work and career progression. Is your role becoming "collapsable" or uniquely valuable?
mentorship Mentorship Is a System, Not an Accident Stop waiting for accidental mentorship! Real career growth happens when mentorship is designed with a specific ask and recurring cadence.
craftsmanship The Standards That Scale: From Code Quality to AI Governance Just as code quality demanded standards in 2013, AI governance requires them now. Organizations willing to say no to deploying autonomous agents without robust infrastructure will build trustworthy AI systems.
AI Writing Code vs. Shipping Code: The Maturity Gap A new MIT/Wharton study tracked 100,000+ developers through three generations of AI coding tools. Commits up 180%. Releases up 30%. Consumer usage: flat. That's not one problem — it's three, at three different layers of the organization. Here's how the maturity model maps to each.
craftsmanship Software Craftsmanship Is Not a Job Title Craftsmanship is an identity built on standards that hold under pressure: a minimum acceptable level of quality, the discipline to automate, and the willingness to say no to people who can fire you.
agile Technical Debt Is a Chronic Condition, Not a Single Decision Technical debt isn't a one-time choice; it’s a *continual* trade-off between quality and speed. Recognizing this as a chronic condition, not an acute problem, changes how we approach it entirely.
DevOps Trust Was Always the Problem: From DevOps to Agentic AI DevOps adoption stalled not due to tooling, but a lack of trust. We're seeing the same pattern with agentic AI – capability isn’t the blocker, it’s building trustworthy infrastructure.
CI-CD From CI/CD to the Intelligence Operating System Strong software delivery practices—like CI/CD—build mental models crucial for building trustworthy AI systems. The path to an "Intelligence Operating System" accelerates when you understand the direct lineage from source control to AI governance.
DevOps Your CI/CD Pipeline Is Already Cognitive Scaffolding CI/CD isn't just about delivery; it’s cognitive scaffolding—a system of tools that distributes and preserves team knowledge, boosting capability over time. Think version control, tests, & pipelines as more than tooling!
machine-learning Classical Statistics vs. Machine Learning: A Practical Distinction Classical statistics needs prior theory; machine learning thrives on sufficient data. Learn when each approach is the right tool for your project!
Building With AI Biological Coordination Patterns Applied to Multi-Agent Software Delivery Coordination overhead grows faster than agent count. The brain solves the same problem with specialization, inhibition, periodic synchronization, and convergence, and each translates into a concrete orchestration primitive for multi-agent delivery.
Building With AI Adversarial Reasoning Architecture: A Multi-Track Approach to AI Agent Reliability AI reasoning fails worst when it is confidently wrong. A structurally separate challenger track that attacks the primary reasoning chain catches flawed assumptions a single model misses.
