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46 articles in AI · page 2 of 2
Your Data Pipeline Breaks at the Seams
Most ML pipelines fail between stages, not inside them. Here are the three seams to instrument and the contract that keeps each one honest.
Structured and Unstructured Is the Wrong Split
The useful question is not which format your data is in. It is whether a schema is enforced, who owns the record, and what the unit of record is.
Agentic AI in Finance Breaks the Two-Person Control
Every real financial control assumes two people. An agent with a login and a payment permission is one actor with none of the accountability.
AI Is Not a Utility Until It Has an Owner
The electricity analogy breaks on three things: model behavior changes on upgrade, output is not fungible, and the meter says nothing about correctness.
AI Will Not Fix Your ERP Forecast
Forecast accuracy is bounded by the chart of accounts and the close calendar, not by the model. Add AI to a broken model and you get faster wrong numbers.
AI-First IT Has to Change the Unit of Work
Buying agents does not change IT. Changing what counts as a unit of work does, and that decision belongs to whoever owns change control.
TOPS Is Not a Workload Number
Edge AI runs out of memory and memory bandwidth before it runs out of compute. Most hardware requirements measure the thing that is not binding.
AI Projects Fail on Decision Rights
The model is rarely why a pilot dies. The reasons are who owns the number, who can say no, and what happens when the output is wrong.
Multimodal Models Do Not Weigh Evidence
A multimodal system can read the scan, the note, and the lab value. Ask what it does when they disagree, and the demo ends.
The Case for Small Models Is Where the Data Lives
Small language models are not winning on benchmark scores. They win when the data cannot leave the building and the task is narrow enough to be checkable.
Your Data Pipeline Fails at the Seam, Not the Stage
Most model incidents are pipeline incidents. The stages are fine. What breaks is the contract between training and serving, and the untested backfill.
Pick the Model After You Know the Cost of Being Wrong
Algorithm choice is the last decision in a model project. The label supply, the cost of a false positive and the retraining path decide most of it first.
Pick ML by Label Budget, Not by Algorithm
Supervised, unsupervised, reinforcement: the choice is mostly about the evidence you have and what you can afford to label.
If You Cannot Name the Decision, You Have a Topic
Most AI use case frameworks start with a problem and a KPI. Start one step earlier, with the decision, the person who makes it, and the record it leaves.
The AI Solutions Manager Owns the Boundary
The role is not model selection. It is owning the line between the model and the workflow, and the evidence that the line holds.
The Data Janitor Is the Domain Expert
Cleaning data is not janitorial work. Every fix encodes a judgment about what a row means, and that judgment belongs to the people who own the process.
A Million Tokens Did Not Kill Retrieval
Long context moved the boundary between retrieving and reading. It did not fix the middle of the window, and it did not create an audit trail.
The Benchmark Number Is a Claim About the Harness
Claude 3.7's TAU-bench score came with a footnote about scaffolding. That footnote is the most useful thing in the announcement.
Colossus Was a Construction Project, Not a Model Story
xAI put 100,000 GPUs online in 122 days by reusing a closed factory and buying temporary power. The constraint was never the chip.
Gemini 2.0's Real Bet Is Model Tiering
Google shipped a flagship and a cheap tier in the same release. The useful question is which task deserves which model, not which model wins.
Reasoning Models Change the Unit of Cost
A reasoning model bills you for thinking and hands back a summary. That changes how you budget, evaluate, and audit an AI workflow.
DeepSeek's Real Number Was Never $6 Million
The training run was cheap and the weights are open. What changed is the price of a credible second option, not the price of intelligence.
End of the archive. 46 essays.