• Validating at the end of a milestone proves the system matches the specification. It cannot prove the specification was right, and that is precisely the check large-batch work skips.
• Building is itself a source of information. Constraints, edge cases and real integration behavior only surface during implementation, so a large batch guarantees that learning arrives after you've built around the wrong assumption.
• AI weakens the case for horizontal specialist tickets. When one person can cover interface, backend, integrations, tests and docs, the organizational reason to slice work by layer instead of by user outcome mostly disappears.
• Kniberg's skateboard framing is sharper than 'ship an MVP': the first release is the earliest testable version of the whole user outcome, which later becomes usable and eventually lovable — never a component shipped alone.

If you're using AI to ship faster and finding yourself with large codebases that don't quite fit the problem, this article gives you a decision rule: match your implementation batch size to your uncertainty level. It's a practical mental model drawn from real applied-AI startup experience, not abstract advice.

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