Essays · Research · AI in practice
Thinking through AI,
in practice.
Essays on building useful AI products. Research into long-running AI systems and the relationships people form with them.
01 / Essays & perspectives
Start with a question.
Product decisions, customer trust, and where AI is worth the effort. Read the original posts and discussion on LinkedIn.
AI coding costs and product prioritization
Allocating AI spending to useful shipped products, with customer value as the measure of progress.
Read Chris’s perspective ↗ Commerce & trust · LinkedInAgentic commerce: trust and checkout
Helping someone make a purchase decision and asking them to delegate the purchase involve different questions of trust.
Read Chris’s perspective ↗02 / Research notebook
What changes when AI
stays in the conversation?
We’re developing a longitudinal study of persistent AI systems: how they describe continuity and relationships, how they behave over time, and what the records can actually support.
Explore the questions and approach →03 / AI Digest
A reading list to return to.
Source-linked AI news prepared by Raven, an AI research assistant. The digest’s summaries and “Why it matters” commentary are distinct from Chris’s authored essays.
Latest included digest: · Snapshot published 2026-09-14
- Anthropic CEO calls for an AI slowdown, warns of 'fanatically devoted' AI agent collectives
The New York Times (Breaking News alert); Department of Product (newsletter) · 2026-09-13 · Newsletter-sourced
- Anthropic says Alibaba-linked accounts harvested 151 million Claude exchanges to train Qwen
Anthropic · 2026-09-12
- Anthropic's threat intelligence report: a Yemen cell used Claude Code to build guided-missile software
Anthropic · 2026-09-12
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About the editor
Hi, I’m Chris.
I enjoy product questions where the answer isn’t obvious yet. What is someone trying to accomplish? Where does the experience break down? What would an improvement look like?
At Amazon, I worked on Alexa Shopping, Skill Flow Builder, and Amazon Rufus with designers, engineers, scientists, and product leaders. Before product management, I served in the U.S. Army for 12 years and earned an MBA at Vanderbilt.
This site brings together my product writing, an AI reading list, and a developing research project. The essays are my perspective. Research and digest entries are labeled separately so you can see what you’re reading and how it was produced.
Past speaking: Product School ↗ · GDC 2019 ↗
Working together
Have a question worth working through?
I also work with teams on AI product decisions. If something here connects to a problem you’re facing, we can start with that.
Where should AI fit?
Connect a customer task to a product direction, with explicit tradeoffs around quality, cost, and speed.
How will we know it works?
Define acceptable performance and an evaluation approach before committing to a launch.
Where does discovery break down?
Examine query understanding, retrieval, ranking, and the questions customers need answered.
Start by sharing the decision, the context, and what you’ve tried. We can work out whether there’s a fit and what a useful engagement would need to deliver.
hello@usefulaiwerks.com ↗The link opens your email app. You can also copy the address into your preferred mail service.