Thinking / field notes2025—26

Ideas sharpened by the work.

A deliberately small collection about the decisions between model capability and product reliability. Written to clarify a point of view, not fill a feed.

01Essay2026 · 5 min

AI products fail on power, not models

The biggest risk in AI product development is often not model quality. It is who gains agency, who absorbs uncertainty, and who is accountable when the system is wrong.

A model changes the distribution of decisions inside an organisation. If the product does not make that redistribution explicit, resistance and shadow workflows will beat technical quality.

Working notes

Map whose judgment the system augments, replaces, or makes visible.
Design escalation around accountability rather than hierarchy alone.
Measure adoption through changed decisions, not prompt volume.

02Field note2026 · 4 min

Evals before code

Why a small, opinionated evaluation set is the most useful first artifact for an AI product team.

Writing an eval set forces the team to turn taste into an operational boundary. It exposes disagreement early, before a polished prototype makes weak assumptions feel inevitable.

Working notes

Start with real failure cases, not an abstract benchmark.
Separate model quality from workflow quality and interface quality.
Keep the evaluation set alive as users and data change.

03Field note2026 · 4 min

Confidence is a product surface

Thresholds, fallbacks, and human review are not backend implementation details. They are the experience of reliability.

Every probabilistic system needs a legible boundary between action, clarification, and escalation. Product teams should own that boundary explicitly.

Working notes

Use the cost of a wrong action to set the confidence policy.
Preserve context across a human handoff.
Make low confidence observable instead of smoothing it away.

04Analysis2025 · 38 min

Designing for LLM-native discovery

How AI-mediated search changes product discoverability, content structure, and the relationship between authority and retrieval.

As discovery moves from ranked links to synthesised answers, products need information that machines can interpret without sacrificing the clarity humans need to trust it.

Working notes

Structure content around explicit questions and attributable answers.
Treat entity consistency and structured metadata as product infrastructure.
Build authority through evidence that remains useful outside the original page.

Read the published analysis