Think of the foundation model as the CPU; your product is the computer you build around it.
In the tooling era, the consumer was human: better interfaces, better workflows, better collaboration for people. Now the consumer is often the model itself. Tokens, verifiers, labs, simulators, context managers, memory systems; these aren’t just more AI. They’re the cognition infrastructure built to augment what models can do, paper over what they can’t, and govern what they shouldn’t.
Products are being built around what sits next to the model. A new class of model-adjacent products.
The Series
This 6-part series covers what that computer looks like, and how to build it safely.
Pre-Read: The Autonomy Ladder
Before you build: the mental models for human-AI collaboration. Why L1 copilots need different infrastructure than L4 autonomous agents.
Part 1: Architecture
The physics of production AI: latency engineering that keeps humans in the loop. Token economics that don’t bankrupt you.
Part 2: Context & Tools
Memory and hands for the model: retrieval that doesn’t hallucinate. Tools that don’t break production.
Part 3: Quality Gates
Model outputs are hypotheses that need verification pipelines to catch errors before users do.
Part 4: Governance & Practice
Alignment as a runtime surface, policy enforcement without retraining. Team practices that ship.
Part 5: The Implementation Path
The build manifest: 90 days from foundation to production.
Who This Is For
Engineering leaders and product managers building AI-first products. You’ve moved past “which model?” to “what do I build around it?”
Key Influences
This series synthesizes research from Karpathy (verifiability, ghosts vs animals), Knight Institute (autonomy levels), OWASP (agentic security), and production patterns from 2025-2026.
Start with The Autonomy Ladder to understand the framework, or jump to Part 1: Architecture if you’re ready to build.