Virginia Eubanks published *Automating Inequality* in 2018, and the building remembers the year even if the metadata doesn't. Eight years is not a long time in the life of an institution, but it is an eternity in the life of an algorithm. The book's central argument — that automated decision-making systems in welfare, housing, and child protective services function as a digital continuation of the nineteenth-century poorhouse — has aged less like a prediction and more like a diagnosis confirmed by biopsy. The Allegheny Family Screening Tool she examined in detail is still running, expanded now, its logic replicated in jurisdictions that read about it not in Eubanks's book but in procurement brochures. Indiana's hybrid welfare automation, which she documented destroying the Stipes family's Medicaid access over a procedural technicality, was a prototype for a generation of systems that have since proliferated across red and blue states alike, often under the banner of "modernization" or "fraud prevention." The coordinated entry system for Los Angeles homelessness she critiqued has been joined by a constellation of similar tools nationwide, none of which have appreciably reduced homelessness. What Eubanks got right was the direction: more data, more opacity, more punishment dressed as efficiency. What she could not have fully anticipated was the speed at which large language models and generative AI would enter the administrative state — not replacing the rigid, rule-based systems she described but layering on top of them, adding new surfaces of inscrutability. The digital poorhouse didn't just persist. It got a renovation.

What the book assumed, reasonably for 2018, was that the primary vectors of automated harm would remain legible enough to be contested — that the problem was one of transparency, accountability, and political will. Eubanks believed, and argued persuasively, that if people could see how these systems worked, democratic pressure could dismantle them. This was the liberal optimism of the Obama aftermath, when "algorithmic accountability" was a phrase gaining traction in policy circles and the idea of auditing public-sector AI seemed achievable. She could not have foreseen the degree to which the transparency movement would be co-opted: jurisdictions now publish model cards and bias audits that function as liability shields rather than instruments of reform. Nor could she have anticipated the pandemic, which accelerated the automation of benefits administration by years, creating systems built under emergency conditions that became permanent infrastructure. Her call for universal basic income, echoing the Poor People's Campaign, reads differently after the brief, strange experiment of pandemic stimulus checks — payments that temporarily reduced poverty and were then withdrawn with bipartisan indifference. The absence in the book that now feels most conspicuous is any sustained engagement with immigration enforcement, where automated systems were already, in 2018, doing precisely what she described in welfare contexts but with even less recourse for those caught in the machinery.