How do you actually compare to the machines, and to other humans?

Frey and Osborne's Oxford paper is still the clearest starting point, scoring 702 occupations on automation risk and putting roughly 47 percent of US jobs in the high-risk band. The OECD later redid the work at task level rather than occupation level and came out far lower, at around 14 percent high risk with another 32 percent facing substantial change. Both agree on the shape even where they disagree on the size. Routine cognitive work sits at the top of the exposure curve, covering data entry, basic accounting, paralegal research, telemarketing and insurance underwriting, while physical dexterity in messy environments, complex social judgement and genuine creative origination sit at the bottom. McKinsey's 2023 generative AI report then moved the goalposts by showing that writing, coding and customer service, all previously counted as safe, are now exposed in ways the 2013 models never anticipated. Run the will-AI-take-my-job tool for your own occupation's band.

No clinical definition exists yet, though research on cognitive offloading has produced some usable proxies. The warning signs cluster around four behaviours, which are reaching for a tool before you have tried the task yourself, feeling stuck or anxious when it is unavailable, accepting output unverified on topics where you actually hold expertise, and noticing that your own writing fluency, mental arithmetic or problem-solving stamina has gone soft. A 2025 survey of 319 knowledge workers by Microsoft Research and Carnegie Mellon found that the more people trusted the AI, the less critical thinking they reported putting into the task, while people confident in their own ability did more of it. Productive use and dependency are different things, and the test between them is whether you can still think the thought without the tool. The AI dependency quiz scores emotional reliance, cognitive offloading, social substitution and skill atrophy, with population context from current usage surveys.

AI literacy means understanding what a large language model can and cannot do, spotting its failure modes, judging its output honestly, and using it without overtrusting it. The EU AI Act, adopted in 2024, now requires organisations that use AI to make sure their staff have enough AI literacy, and the quiz here splits it into four parts covering understanding of how the technology works, application in practice, evaluation of output quality and bias, and ethics around privacy, attribution and societal impact. A literate user can explain why an LLM hallucinates, knows that a confident tone carries no information about accuracy, and checks factual claims before leaning on them. They also know the difference between a base model and a fine-tuned chat model, why phrasing changes output quality, and what a training data cutoff means in practice. Most people score lower than they expect on application and evaluation, particularly on retrieval-augmented generation, the limits of in-context learning, and how to catch a confident claim with nothing underneath it. The AI literacy score quiz tests all four pillars and returns a percentile against current population data.

Safety is the wrong frame for the question. Almost every knowledge job gets reshaped over the next five to ten years, and reshaping sits a long way from replacement. The OECD's 2023 employment outlook found that even the highest-exposure jobs mostly survive in some form, with the mix of tasks shifting rather than the role vanishing. Roles that genuinely disappear share three traits, being fully digital, having well-structured inputs and outputs, and carrying a cost of error low enough that human checking adds little. Tier-one customer service, basic copywriting and template legal drafting all fit that pattern. Roles that grow tend to pair AI fluency with physical presence in unstructured environments, or accountability for high-stakes decisions, or the relational trust that therapy, leadership and complex sales run on. McKinsey's 2023 report on generative AI and the future of work in America put up to 30 percent of US hours worked as automatable by 2030, spread very unevenly across roles. The will-AI-take-my-job tool gives your exposure band, and the AI relationship quiz scores how ready you personally are to work alongside these things.

It depends entirely on the task, and the spread between tasks is enormous. GPT-4 scored in the 89th percentile on SAT maths and the 93rd on reading and writing, the 88th on the LSAT, 4 or 5 on most AP exams, and was reported to pass the Uniform Bar Exam at the 90th percentile. Those figures come from OpenAI's own GPT-4 technical report, and a 2024 re-analysis in Artificial Intelligence and Law put the bar result nearer the 62nd percentile against first-time test takers. GRE quantitative came in at the 80th percentile and verbal at the 99th. Give the same model physical common sense, real-time reasoning under uncertainty, sustained multi-step planning or genuine creative origination and it falls well behind a competent human professional. The am-I-smarter-than-ChatGPT tool takes your own standardised test scores and shows where you sit against those published figures, on the honest framing that the model beats the human median on narrow benchmarks and loses on integrated, accountable work.

About 34 percent of US adults told Pew Research in early 2025 that they had ever used ChatGPT, roughly double the 2023 share, rising to 58 percent among adults under 30. Adoption skews young, educated, urban and heavily concentrated in technical, marketing and professional services work. The fastest-growing route in is workplace integration through Microsoft Copilot and Google Gemini, which reaches people who never open ChatGPT at all and pushes real penetration well above the chat-app numbers. OpenAI said ChatGPT had around 200 million weekly users by August 2024 and 300 million that December. Set against historical adoption curves the pace is hard to compare with anything, since the telephone took 75 years to reach 100 million users, the internet 7 years, smartphones around 16 years to reach a billion, and ChatGPT 2 months. The AI purity test is a lighter self-audit of how far into your week these tools have got.

Partially, and not in the places people expect. Two years of evidence puts generative AI at competent on first drafts, format conversion, stylistic mimicry and idea generation, and weak on sustained originality, taste, editorial judgement and anything genuinely unexpected. Harvard Business School, working with Boston Consulting Group consultants, found a 40 percent quality lift on tasks inside the model's competence frontier and a measurable drop outside it, where consultants trusted answers that were plausible and wrong. So far the work most exposed sits at the formulaic end, stock copy, formulaic social posts, generic illustration and template drafting, while a distinctive voice and original framing have held their value. The technophobia test and AI relationship quiz probe where you sit on that.

A small set of primary sources carries the whole silo. Occupation-level risk comes from the Frey and Osborne Oxford paper with its original 702-occupation automation index, alongside the OECD task-level employment outlook that corrects some of Frey and Osborne's overestimates. Productivity and economic impact draw on the McKinsey Global Institute generative AI reports, particularly the 2023 economic potential study, plus the Goldman Sachs 2023 analysis. US adoption rates come from Pew Research annual technology surveys, with Sensor Tower supplying app-level usage analytics for ChatGPT and its competitors. Human against model performance comes from OpenAI's published GPT-4 benchmark scores, which are the company's own figures. Every tool cites its own source on the page and updates as new survey waves and benchmark releases land. That matters more here than anywhere else on the site, because a number that was right six months ago can already be out of date, so the footer of every AI tool carries a last verified date.