Insights on AI, engineering leadership, and technology at scale.
SOC 2 Type I and Type II aren't just for security and compliance teams. Engineering and platform leaders own critical controls-here's how to prepare and sustain them.
How to make SOC 2, ISO 27001, and other frameworks a natural outcome of your platform and engineering practices-not a separate compliance project.
What VP-level technical leaders need to know about building and governing AI/ML platforms that serve thousands of engineers and billions of inferences.
Shifting engineering organizations to embrace AI-assisted development, tooling, and new ways of working - without losing rigor or ownership. This post covers upskilling, tooling strategy, and how to preserve ownership and quality while scaling AI adoption.
Autonomous agents are moving from research to production. Here's how technical leaders should think about architecture, safety, and org impact. Learn how to define boundaries, implement guardrails, and scale agent use cases without creating new risk surfaces.
AI accelerates both feature delivery and the accumulation of debt. How exec-level technical leaders should balance speed, quality, and long-term health. This post covers debt as a strategic variable, how AI affects debt creation, and metrics that matter for the board and peers.
Principal engineers, architects, and tech leads are force multipliers. How to attract, assess, and retain them in a competitive AI-era market. This post covers what senior leaders want, how to assess for level, and retention strategies that go beyond compensation.
Running large language models in production requires new patterns for reliability, cost, compliance, and risk. A VP-level playbook.
The transition from hands-on technical leadership to VP of Platform or Infrastructure: scope, mindset, and where to focus your time.
Vision, audio, and language are converging in single models. How technical and product leaders should position roadmaps and partnerships.
Executive perspective on unit economics, ROI frameworks, and when to double down or pull back on gen AI investment.
How to structure platform, ML, and product engineering for speed, ownership, and alignment. Lessons from high-growth and scaled organizations.
A short introduction and what you can expect from this space. This is a place for thoughts on software engineering, leadership, and technology as we build and scale teams and products.