Opening — Why this matters now AI infrastructure has entered its spreadsheet era. Not the glamorous spreadsheet, where revenue projections grow diagonally upward and nobody asks where the assumptions came from. The other spreadsheet: the one where compute cost, memory footprint, inference latency, training instability, and model quality all insist on appearing in the same row. ...
The expensive habit of feeding the machine Data teams have a familiar ritual. The model disappoints. Someone asks for more data. Another person asks for cleaner data. A third person, usually with a spreadsheet and a suspiciously calm face, asks whether the extra labeling budget is approved. Then the pipeline expands. More driving clips. More corner cases. More annotated scenes. More storage. More training runs. More dashboards explaining why the latest model is still not quite where it should be. ...
Receipts are not glamorous. That is precisely why they are useful. A receipt-item categoriser is not a benchmark leaderboard, a launch demo, or a dramatic agentic workflow with a glowing dashboard. It is the kind of small, repetitive business decision that quietly determines whether an AI system becomes a product or remains an expensive toy. A bottle of iced coffee needs a category. A supermarket item needs to land in the right expense bucket. The output must be parseable. The cost must be low enough to repeat thousands or millions of times. Nobody wants a philosophical essay from the model. They want a JSON array. ...