The Embedded Engineer: Why Every Business Will Run an AI Harness
As AI changes the economics of custom software, one engineer with a capable harness can turn company-specific workflows into maintained systems instead of SaaS workarounds.
Blog
Insights on AI harnesses, agentic systems, and the future of autonomous work.
What CTOs Should Ask Before Letting Teams Use Public AI Tools
The right policy is not “yes” or “no” to public AI. It is whether the company has clear enough workload rules, controls, and ownership to use those tools without drifting into avoidable exposure.
AI Compliance Is Becoming a Product Requirement
Compliance stops being a late-stage legal review once AI systems start touching real operations, real customers, and real internal controls.
How To Decide Which AI Workloads Must Stay Inside Your Environment
The right privacy boundary is not set by intuition alone. Companies need a repeatable way to separate low-risk AI use from workloads that demand tighter control.
A Buyer’s Checklist for Evaluating Private AI Platforms
Private AI sounds appealing in theory, but buyers still need a practical way to distinguish serious platform design from vague claims about security and control.
AI Slop: The Digital Casserole Nobody Ordered
AI slop is what happens when someone uses AI without enough subject mastery or tool skill to know whether the output is useful, correct, or coherent.
When Should a Company Run a Local LLM?
Local deployment is not a badge of technical seriousness. It is a workload decision that makes sense only when privacy, control, or economics justify the added operational burden.
What the EU’s Google Ruling Signals for Enterprise AI Access and Governance
When regulators force dominant platforms to open data and distribution channels, enterprise buyers should pay attention. AI competition is increasingly becoming a control and access question, not just a model quality question.