The Journal
Essays
Long-form writing on AI, philosophy, psychology, and systems thinking.
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Change Management for Engineering Teams
Studying 5 process change initiatives found the 2 that succeeded addressed engineers' autonomy, tooling, and evidence concerns. Co-designed processes achieved 83% adoption.
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Geospatial Data Engineering Is Underinvested and Overneeded
The geospatial analytics market will reach $150 billion by 2028, yet fewer than 8% of data teams have spatial data skills. Location intelligence is the largest skills deficit in data engineering.
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Designing for Composability: Building Systems From Interchangeable Parts
Systems designed for composability reduced feature development time by 41% and integration effort for new channels by 67% across 9 organizations adopting composable architecture principles.
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AI Ethics in the Supply Chain: Training Data Provenance Problem
Tracing training data lineage for 3 models revealed none could document full provenance. One model included 12 sources with no consent chain. AI has a data supply chain problem.
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AI in Education Raises Questions We Are Not Equipped to Answer
AI tutoring systems serve 120 million students, yet ethical questions about pedagogical authority, surveillance of minors, and cognitive formation remain unanswered by any framework.
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The therapeutic value of building: How engineering projects function as meaning-making
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How AI changes the economics of attention, not just productivity
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Data Modeling Is the Meditation Practice of Data Engineering
Teams that skip formal data modeling accumulate 3.2 redundant entity definitions per domain within 18 months. Modeling is the discipline most needed when it feels least productive.
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Streaming Architectures for Teams That Do Not Need Real-Time
Kafka cost $4,200 per month when 94% of consumers queried at hourly or daily granularity. A micro-batch alternative costing $800 per month delivered identical analytical outcomes.
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The Difference Between Data and Evidence
In a review of 30 data-driven proposals, 22 presented data as evidence without the analytical chain from observation to inference. The gap is analytical rigor.