This podcast examines AI systems through a concrete architectural teardown.

Each episode selects one AI system or product and dissects it end to end — from user interaction through data flow, model invocation, infrastructure, and operational constraints.

A typical episode walks through:

• the system boundary and assumptions
• the actual architecture (not the marketing description)
• where latency, cost, and throughput enter
• early bottlenecks and scaling limits
• failure modes that appear only in production
• decisions that shift risk between humans and machines

The goal is to make system behavior legible, not to evaluate tools or trends.

New material will appear here as the next season begins.