The Algorithm
Notes on building AI systems that actually work.
What I've learned building AI systems across healthcare, finance, ecommerce, and government — the architecture decisions, design tradeoffs, and organizational dynamics that determine whether an AI project succeeds or stalls.
Latest
The Execution Layer Has Moved
If the next knowledge work role you fill is built around execution capacity — someone to do the research, draft the documents, produce the analysis — you're building for a version of the work that no longer describes how the work actually happens.
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The Cheaper Half of Oversight
Reviewing outputs answers whether the work is good. Reviewing plans answers whether it was the right work — and only one of those questions can be answered before the scope locks.
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Stop Trying to Automate the Whole Workflow
The organizations getting the most from AI right now are mostly not building sophisticated autonomous systems. They found one expensive step in a process that already works and made that step better.
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Series
Foundations
The analytical and structural thinking that everything else builds on.
8 postsIn Production
What it takes to make AI work in production — the failure modes, misdiagnoses, and the questions worth asking.
5 postsCompound
How an AI operating system of specialized agents is designed, structured, and improved over time.
3 postsGround Truth
Plain observations on what AI is actually doing to knowledge work, organizations, and the people who lead them.
2 postsSubscribe to The Algorithm
Notes on building AI systems that actually work.