About Me

I write about applied AI in the real world: what works, what breaks, what is overhyped, and what is genuinely changing how we build products, companies, and decision systems.

My focus is practical, not theoretical. I’m interested in AI as an engineering and product discipline: how language models, agents, synthetic data, tabular transformers, decision intelligence platforms, and modern data systems can be used to solve actual problems. That means going beyond demos and into architecture, tradeoffs, failure modes, evaluation, deployment, governance, and code.

This blog is where I explore the good, the bad, and the ugly of applied AI.

The good: AI systems that compress months of work into days, make complex data usable, automate painful workflows, and open up new product categories.

The bad: brittle agents, hallucinated confidence, poor evaluation, messy organizational incentives, unclear ownership, and AI projects that look impressive but fail to create durable value.

The ugly: production systems that only work in demos, dashboards no one uses, “AI strategy” without implementation, and the uncomfortable gap between what executives think AI can do and what current systems can actually deliver.

I’ll write about real use cases, real architectures, and real code. Expect posts on agentic systems, synthetic populations, decision intelligence, natural language analytics, data products, AI-native software design, and the changing relationship between humans, machines, and organizations.

My bias is simple: applied AI only matters when it helps people make better decisions, build better systems, or do something valuable that was previously too slow, too expensive, or too complex.

This blog is my attempt to document that frontier as honestly as possible.