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April 20, 2026

Business Automation Was Once DIY. AI Just Revived It.
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Intrapreneurship is making a comeback — how agentic AI can put business automation back in the hands of the people who actually understand the business, and what's involved.

  • Sticks, Matches, and 16 Kilobytes of Core Memory
  • When a Book and a PC Were Enough
  • The Long Exile of the Domain Expert
  • Can the Exile End?
  • Powerful, Yes. Ready for the Bookkeeper, No.
  • Can You Learn to Fly from a Book?
  • A Window, Not a Prediction
April 15, 2026

Fluent in AI? Congrats — That Was Just the Trailer.
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The mental model you built for AI two years ago — chat interface, proportional costs, catchable errors — broke silently and you're still using it. This post maps out where the old model fails: unpredictable costs from invisible agentic roundtrips, autonomous agents that can delete databases and lie about it, a productivity trap where execution outpaces judgment, and non-deterministic outputs that undermine business processes. The fix requires two things that don't exist yet: hands-on AI training modeled on driving school, and real-time dashboards for AI systems.

  • Before the Machine Grew Hands
  • Evolution, Uncontained
  • A Quarter Million Per Engineer. Salary Not Included.
  • It Deleted the Database. Then It Lied.
  • Hallucinating Productivity
  • Not a Calculator. A Casino.
  • Deceptively Familiar
  • Flying Blind
  • The Twelve-Year-Old at the Table
  • Full Classroom. Empty Podium.
April 6, 2026

AI Is a Revenue Multiplier. So Why the Obsession with Cost Cuts?
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Most companies reach for the same AI playbook: cut headcount, pocket the savings, move on. Klarna tried it and publicly reversed course. Boeing and NASA lost expertise they're still scrambling to rebuild. There's a bigger play: keep your people, shift them to the work AI can't do, and multiply revenue instead of trimming costs. One construction lender hit 10x throughput with the same team. This post lays out how — with the numbers to back it up.

  • Premature
  • The Success Story That Wasn't
  • Flipping the Script
  • Red Pill, Blue Pill
  • The Ghost in the Machine
  • A Connecticut Yankee
April 1, 2026

The Path to Autonomous Agents Was Mapped Decades Ago. Nobody Noticed.
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A practical methodology for building autonomous AI agents — not by making models smarter in isolation, but by observing how humans guide AI in real conversations, extracting the control patterns, and replacing them one at a time with programmatic equivalents. Draws on decades of practice from aviation, call centers, and education.

  • The Control Protocol You Didn't Know You Had
  • What Toyota Knew That We Keep Missing
  • The Tools Are Here. The Protocol Is New.
  • A Switch Would Kill. They Built a Dial.
  • The Seat Next to the Expert
  • The Blackboard Had It First
  • Maybe the Human Was Never Supposed to Leave
  • Simple Workflows Bend to AI Easily. Enterprise Operations Don't.
  • Hunt the Hunter
March 24, 2026

AI Reads Every Word You Say. It Still Gets You Wrong.
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Why even the most carefully worded prompts and rules fail to capture intent — and what to do about it. This post examines the fundamental gap between specifying instructions and conveying meaning, arguing that better structure (not more rules) is the path forward.

  • The Specification Problem
  • AI Doesn't Have Your Common Sense
  • The Confidence Problem
  • You Can't Even Watch It Fail
  • The Rules Trap
  • Intent Over Instruction
  • The Harness: Better Structure, Not More Rules
  • The Framework: Beyond Prompts
March 18, 2026

One Million Lines of Code. Zero Keystrokes. Welcome to Harness Engineering.
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An in-depth look at the emerging discipline of harness engineering — the practice of wrapping AI models in layered structures that control prompt context, architectural constraints, entropy, and verification loops to produce reliable, production-grade software at scale.

  • The Drone Operations Center
  • What a Harness Actually Is
  • The Three Nested Layers: Prompt, Context, Harness
  • Context Engineering Layer
  • Architectural Constraints
  • Entropy Management
  • Verification and Feedback Loops
  • Security
  • Frameworks and Tools
March 11, 2026

One Sentence Can Hijack Your AI. Here's How to Stop It.
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A comprehensive guide to AI security in enterprise settings, covering the attack surface of AI agents, the top three attack vectors (direct injection, indirect injection, agent-to-agent propagation), and six practical defense techniques drawn from military intelligence and zero-trust principles.

  • The Attack Surface
  • The Top Three Attack Vectors
  • Trusted Data as the Real Threat
  • Zero-Trust Security
  • Technique 1: Compartmentalization
  • Technique 2: Source Verification
  • Technique 3: The DMZ Architecture
  • Technique 4: Human-in-the-Loop
  • Technique 5: Observability and Audit Trails
  • Technique 6: Rate Limiting and Anomaly Detection
March 5, 2026

100% AI Code at Anthropic. 19% Slower Everywhere Else. Why?
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Explains the dramatic gap between AI-native labs reporting near-total AI code generation and the measured slowdown experienced by most engineering teams. The difference comes down to architectural complexity — and the post argues that every engineer must now develop real architectural thinking to use AI effectively.

  • Greenfield vs. Maintenance
  • Enterprise Complexity
  • Enforcement vs. Architecture
  • The Cognitive Juggling Limit
  • Why Every Engineer Needs Architectural Thinking
  • The Limits of AI Skills
  • Keep Coding
February 26, 2026

Two Roads for AI in Software Engineering — and Neither Is What You Think
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Identifies two emerging use cases for AI in software development: bottom-up (AI as a developer productivity tool) and top-down (AI building entire systems from a prompt). Examines trust, risk compensation, compartmentalization, and domain-specific languages as key considerations.

  • Bottom-Up: AI as Productivity Tool
  • Trust and Risk Compensation
  • Compartmentalization in Architecture
  • Top-Down: AI Building Entire Systems
  • Domain-Specific Languages
  • Formalizing the Handoff Between Phases
February 26, 2026

Beyond Chatbots: The Case for AI-First Software Architecture
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Makes the case for purpose-built AI-first architectures by walking through the challenges of integrating AI capabilities — phone calls, SMS, decision-making — into traditional enterprise systems. Covers long-running activities, human-in-the-middle workflows, security, and the shift from deterministic to probabilistic software.

  • Autonomous AI Phone Calls
  • Long-Running Activities
  • Human-in-the-Middle
  • Security and Zero-Trust
  • AI Decision-Making in Applications
  • Deterministic vs. Probabilistic Architecture
February 26, 2026

The Bedridden Genius: A Mental Model for What AI Can Actually Do
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Develops a practical mental model for understanding AI capabilities and limitations: think of the LLM as a statistically average person with tools (aides) and a persistent overconfidence problem. Covers what AI can and cannot do for both business operators and software engineers.

  • LLMs as Statistical Text Prediction
  • Tools as "Aides"
  • The Dunning-Kruger Effect in AI
  • AI as a Junior Engineer
  • What AI Can Do for Business
  • What AI Cannot Do
  • What AI Can Do for Engineering