Amershi et al. (2019) studied AI development workflows at Microsoft and reported a now-famous number: ~70% of AI development time is spent on data preparation and feature engineering. Not modeling. Not deployment. Not evaluation. Data wrangling.

If you're building anything ML-adjacent in 2026, that number is still mostly true — and it's still mostly avoidable if you treat data infrastructure as a first-class system instead of an afterthought you write inside notebooks.

Where the 70% actually goes

Amershi's breakdown across 551 ML practitioners pointed at four sinks:

Schema reconciliation — same entity, four upstream sources, four shapes

Imputation and outlier handling — null rates that move week-over-week

Feature pipelines that drift silently — train/serve skew nobody owns

Labeling and re-labeling — the cost line nobody budgets for

The single most common antipattern is treating each of these as a notebook exercise instead of a deployable artifact.

What actually cuts the tax

Treat features as code.…