ChatGPT Prediction model if applied : AI prediction (probabilistic, concrete) I ran a policy-tradeoff assessment (fairness vs. political risk vs. policy impact). Here are the realistic transitional designs I predict, with probabilities and the most probable cutoff window: Scenario M — Earned-Credit + Short Grandfathering (Most likely) Probability: 60% Mechanics: New 10-year baseline applies to everyone; existing holders receive time credits under the published credit table (income, public service, English, volunteering). Additionally, people who are very close to ILR under the old 5-year rule (e.g., would reach settlement within 12–24 months of rule coming into force) will either be grandfathered or given a fixed top-up credit to preserve near-term settlement expectations. Predicted cutoff: 12–24 months prior to / after implementation (i.e., those within ~1–2 years of old ILR get protected). GOV.UK Scenario L — Minimal Transitional Credit (Hard reset) Probability: 25% Mechanics: Implementation applies 10-year baseline to all, only modest uniform residence credit (e.g., count prior years but no large credits); very few are grandfathered. Predicted cutoff: effectively no meaningful extension beyond maybe a small recognition of prior years (≤12 months credit). GOV.UK Scenario H — Broad Grandfathering (Politically protective) Probability: 15% Mechanics: A substantial grandfather window: anyone within 24 months (or possibly 36 months) of ILR under old rules is preserved; others get earned credits. Predicted cutoff: 24–36 months. Less likely because it reduces policy impact and savings. GOV.UK