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Why promising AI pilots never become real products
A pilot proves something is possible. A product has to keep working. The gap between the two is predictable, and it can be closed before the money is spent.
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Stories about technology, products, people, and the decisions shaping what comes next.
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What does it really take to bring AI into production?
Make four decisions about a hypothetical AI project (scope, data, oversight, rollout) and see what you’d be trading away. A simulation, clearly labelled.
The personalization paradox
When does tailoring an experience to someone make it better, and when does it become intrusive? An interactive look at four rungs of personalization.
The cost of not building
Budgets record what projects cost. They rarely record what it costs to leave an idea untested. A practical way to make “not now” a real decision.
What shipping products teaches you
Five practical observations about dates, first versions, measurement, release size and memory, written as field notes rather than rules.
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10 stories, sorted editorial.
Why promising AI pilots never become real products
A pilot proves something is possible. A product has to keep working. The gap between the two is predictable, and it can be closed before the money is spent.
What does it really take to bring AI into production?
Make four decisions about a hypothetical AI project (scope, data, oversight, rollout) and see what you’d be trading away. A simulation, clearly labelled.
The personalization paradox
When does tailoring an experience to someone make it better, and when does it become intrusive? An interactive look at four rungs of personalization.
The cost of not building
Budgets record what projects cost. They rarely record what it costs to leave an idea untested. A practical way to make “not now” a real decision.
A confidence score is not a probability
Many models emit a number between 0 and 1 that looks like a probability. Unless it has been calibrated, it isn’t one.
What shipping products teaches you
Five practical observations about dates, first versions, measurement, release size and memory, written as field notes rather than rules.
Designing experiences for millions of people
How product teams think about scale, usability and personalization when the rare case stops being rare.
What happens before a good product decision
Framing, evidence, options, trade-offs and a pre-mortem: the unglamorous work that makes a product decision look obvious afterwards. Illustrated with a clearly hypothetical team.
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What does it really take to bring AI into production?
Make four decisions about a hypothetical AI project (scope, data, oversight, rollout) and see what you’d be trading away. A simulation, clearly labelled.
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