Notes on data, AI, IT
and security
No marketing fog. The way I think about real problems with founders and managers.
Experience in IT doesn't appreciate on its own
The market pays not for years in the profession but for the ability to solve current problems. Why expertise quietly loses value, and which part of it barely depreciates at all.
When AI exposes the debt sitting in your codebase
The first numbers from Anthropic's Glasswing project are not a story about a smart model. They are a story about how much old vulnerability lives in code we use every day.
Data contracts: from principle to working tooling
Data contracts were discussed as a concept for several years. In 2026 they are working tooling with real implementation costs and real results.
Robotics after LLM: why the next question is not chat, but action in the world
LLMs changed the interface for interacting with computers. The next shift is physical systems that understand context and act in the real world.
AI does not fix bad data
A short note on why an AI rollout in a company starts not with the model, but with the quality of the data underneath.
What data engineering is, and why business needs it before AI
Why companies have to gather and structure data before talking about models and agents.
The next evolution of Agents SDK: long tasks, sandbox, and a production-ready agent
Tools for building AI agents are maturing. What this means for companies thinking about real deployments rather than demos.
Excel in a company is not a shame, it is a symptom of growth
Why a company being held together by Excel is not embarrassing, but a signal of which processes have outgrown their tools.
How to tell when your vendor is making the project more complicated than it needs to be
Signals that an executive can pick up early, before the project's problems become impossible to ignore.
Shadow AI: how the new shadow IT is becoming a security problem
Employees are using AI tools without IT department oversight. The pattern is familiar - but the risks are different from classic shadow IT.
Data, IT, and security cannot be separated
Why splitting these three areas across different teams turns any technology project into a quiet source of hidden risk.
How I look at a new technology project
The set of questions I run any new request through, from an AI assistant to industrial analytics.