Openwashing by Architecture
The open data movement may have been a movement ahead of its time. It is not the first. The dot-com bust did not prove the internet was useless: it proved that the infrastructure, business models, and user base were not yet ready for what the technology promised. In a similar way, open data portals were built, commitments were signed, rankings were published, and most of us attended self-congratulating conferences. The consequential phase, in which the data actually reconciles with fiscal reality and users can verify what they receive, never arrived.
AI does not restart the cycle. But it does create a new reason to complete the work that was left undone. States are legible to themselves, through internal systems that require credentials and institutional access. They are selectively legible to citizens, through portals designed for human browsing. They are barely legible to machines, through partial extracts published without completeness guarantees. Making states legible to AI requires affirmative institutional choices: publishing complete datasets via APIs, providing metadata on coverage, maintaining data pipelines that reflect the full scope of government financial operations. As ever, these are governance decisions, not technical problems.