Long-form writing from startup and enterprise execution, organized with a theorem-first lens and grounded in practical implementation.
Each post aims to answer four questions:
The goal is practical strategy: fewer slogans, clearer tradeoffs, and decisions teams can execute.
By early March 2026, the AI startup market looks less like a gold rush and more like a durable industry with clear pressure points. This post lays out where leverage sits, what buyers reward, and what durable execution looks like now.
As of late February 2026, AI security is defined by adaptive attacks and layered, operational defenses.
As of mid-February 2026, AI team structures have stabilized into a few workable patterns. This guide explains the models, tradeoffs, and roles that hold up in practice.
A pragmatic look at AI cost trends in early February 2026, plus what to do about them.
Regulation isn't a future problem anymore. It's showing up in procurement, security reviews, and internal sign-off. The teams that treat compliance as engineering will ship faster than the ones scrambling to bolt it on.
As of late January 2026, AI-native architecture is a stable discipline with repeatable patterns for delivery, safety, and change management.
Reliable agents aren't prompted into existence. They're engineered -- with bounded tools, validation at every step, explicit recovery paths, and the same discipline you'd apply to any production system. Here's how I build them in Go.
Video AI is practical for scoped workflows. This post covers what works, how to design for reliability, and where human review still matters.
Less hype, more plumbing. Agents get real but stay bounded. Routing beats monolithic models. Governance lands on the critical path. And the teams that win will be the ones that treat AI like software, not magic.
A year-end look at what actually happened in AI -- not the hype, but the operational shift. The novelty phase is over. The infrastructure phase has begun.
The most important thing that happened to AI in 2025 wasn't a model release. It was the shift from 'what can it do' to 'how do we run it.' That's progress.
The technology works. The pilots work. What doesn't work is going from five demos to fifty production features without an operating model. That's not an AI problem -- it's a management problem.
Your AI system can return 200 OK and still be wrong, unsafe, or confidently hallucinating. Here's how to detect, contain, and learn from AI incidents -- drawing from the same IR principles that work for traditional systems.
AI debt doesn't look like normal tech debt. It hides in prompts nobody owns, evals nobody runs, and data pipelines nobody watches. By the time you notice, every change feels dangerous.
Individual AI speedups are a distraction. The real gains come from treating AI as team infrastructure -- embedded in docs, decisions, and onboarding.