New AI job-disruption countdown tool launches on roneehulk.com

By AI, Created 10:15 AM UTC, May 20, 2026, /AGP/ – Edinburgh author Ronee Hulk has launched a tool that gives workers a personalized estimate of when AI will materially disrupt their current job, with plain-English explanations and citations on both sides. The site is designed to narrow the gap between public anxiety about AI and the evidence on where disruption is most likely.

Why it matters: - The tool tries to turn broad fear about AI into a specific, evidence-based estimate for a named occupation. - Workers can see not just a countdown in days, but also the reasoning behind it and competing evidence. - The product aims to clarify where AI is most likely to reshape work first, and where change may take longer.

What happened: - Ronee Hulk launched “How Long Until AI Takes Your Job?” on the homepage of roneehulk.com. - A user enters a job title in plain English, such as primary school teacher, barrister or lorry driver. - The tool returns a countdown in days, a short structured explanation, and two supporting and two contrarian pieces of evidence, each with a clickable citation. - The tool is presented as personalized, citation-backed and designed to estimate when a job, as currently performed, will be materially disrupted by AI.

The details: - The system uses three layers of grounding: a structured occupation catalogue, a regional adjustment, and a citation layer. - The occupation catalogue contains anchored target dates that only change when the underlying evidence base changes. - Regional adjustments reflect differences in adoption, regulation and labor market structure. - The citation layer forces each response to show named sources on both sides of the question, including sources that disagree with the headline number. - The narrative text is generated after the score, date, region and citations are set. - The tool says it judges jobs as they are currently performed, not as they might be redesigned in the future. - It treats the target date as the point of material disruption, not full elimination. - The tool uses O*NET as the reference for what each occupation involves. - It applies regional context because adoption, regulation, capital intensity and language coverage vary across markets. - The target dates combine occupation-level exposure scoring from Goldman Sachs, OpenAI, the OECD, and academic groups at Oxford, Princeton and Stanford. - The model also uses observed deployment data, including enterprise adoption surveys, model capability benchmarks and labor market indicators from official statistical offices. - The platform applies accelerator and decelerator factors such as regulatory friction, union density, physical embodiment requirements and consumer trust thresholds. - Where evidence is weaker, the tool defaults to longer time horizons rather than shorter ones. - Hulk is the Edinburgh-based author of Dear Future: You Can Keep The Change and the creator of roneehulk.com and LikelyStance.com.

Between the lines: - The product is built around transparency, not a single black-box prediction. - By showing both supporting and opposing evidence, the tool signals that AI job disruption is uneven and contested. - The emphasis on material disruption suggests the countdown is meant to measure meaningful change in work, not job disappearance.

What’s next: - The tool will change only as the evidence base changes, which suggests the countdown is meant to be updated over time. - Its usefulness will likely depend on how well the underlying evidence tracks real-world AI adoption across occupations and regions. - Ronee Hulk is also positioning roneehulk.com as a broader platform, alongside the political inference site LikelyStance.com.

The bottom line: - The new tool gives workers a personalized AI disruption estimate built on named evidence, regional context and explicit uncertainty instead of a generic prediction.