Photo - Dylan Gillis/Unsplash
A new tool on roneehulk website aims to give any worker a personalised, citation backed estimate of how many days remain before their job, as it is currently performed, is materially disrupted by AI.
The tool, How Long Until AI Takes Your Job?, sits on the homepage of roneehulk website. It is the work of Edinburgh based author Ronee Hulk, writer of Dear Future: You Can Keep The Change. A user enters a job title in plain English, such as primary school teacher, barrister or lorry driver; and within seconds receives a specific countdown in days, a short structured explanation, and two pieces of supporting evidence alongside two pieces of contrarian evidence, each with a clickable citation.
The tool works because every estimate rests on three layers of grounding. The first is a structured catalogue of occupations whose anchored target dates only move when the underlying evidence base moves. The second is a regional adjustment based on observable differences in adoption, regulation and labour market structure. The third is a citation layer that forces every response to display real, named sources on both sides of the question, including ones that disagree with the headline number. The narrative layer writes the prose, but the score, the date, the region and the citations are all set before it ever runs.
The tool is transparent about its assumptions. It judges each job as currently performed, not against a hypothetical future redesign. It treats the target date as the point of material disruption rather than full elimination, recognising that most occupations will continue to exist in some form well beyond it. It uses O*NET as the canonical reference for what each occupation actually involves, and it applies regional context because adoption, regulation, capital intensity and language coverage vary materially between markets.
The target dates themselves are arrived at by combining three inputs. The first is occupation level exposure scoring published by Goldman Sachs, OpenAI, the OECD; and academic groups at Oxford, Princeton and Stanford. The second is observed deployment data, including enterprise adoption surveys, model capability benchmarks and labour market indicators from official statistical offices. The third is a small set of accelerator and decelerator factors that the platform applies consistently, such as regulatory friction, union density, physical embodiment requirements and consumer trust thresholds. Where the underlying evidence is weaker, the tool defaults to longer time horizons rather than shorter ones.
A quick test by us for "Academia" gave a timeline of more than 20 years with a caveat that human innovation and interpretation are extremely important in Academia. Hence, this career will never be completely taken by AI.
"AI will disrupt journalism's processes, but trust and accountability will keep human reporters essential.
"Journalism sits at a crossroads, with AI poised to disrupt its processes and outputs in the next decade. While AI tools like ChatGPT and Bard are already generating articles and summaries, the question is not whether AI will write the news but whether it will erode the trust and value of human reporting.
"The case for disruption is strong. According to the Financial Times, AI powered content platforms are producing a meaningful share of digital articles at near zero cost, threatening traditional newsrooms. Automated tools can analyse vast datasets, draft reports; and even mimic editorial styles, creating a sharp economic advantage for AI driven publishers. This efficiency could lead to job losses and a decline in investigative journalism, which requires time and resources that AI cannot yet replicate.
"The counter case is rooted in trust and accountability. As the BBC has reported, public trust in journalism remains tied to human oversight and ethical standards. Live reporting, investigative depth, and the ability to ask unscripted questions are structural advantages that AI cannot replace. Regulatory bodies like Ofcom also ensure that journalistic integrity is upheld, making it harder for AI generated content to dominate in regulated markets like the UK.
"Readers should demand transparency and human accountability in the news they consume. Journalism thrives on trust; and that trust is earned through human effort, not algorithms.
"The stories that shape us are not just told; they are lived."
As AI evolves, this calculation will also evolve simultaneously.
