Safe From AI?
Seven questions about how you actually spend your working hours — not your job title. We score the share of your week made up of task types AI systems are already observed performing in real work settings, then hand you a verdict: safe, augmented, or automation frontline.
How this assessment works
What it measures
This tool estimates task-level exposure: the share of what you actually
do in a week that current AI systems can perform or substantially
accelerate. It does not predict whether you personally will lose your
job. Exposure and job loss are different things, and conflating them is
the most common error in coverage of this topic.
What you put in
Seven questions about the composition of your working week. How much
of your time goes to drafting and editing text, analysing structured
data, routine decision-making against known rules, physical or
in-person work, novel problem-solving, relationship and negotiation
work, and supervision of others. No job titles, no company, no salary.
What you get out
A score from 0 to 100 and one of three verdicts:
Safe: your week is dominated by tasks with low current AI
penetration, typically physical, interpersonal, or high-accountability
work.
Augmented: significant parts of your work can be accelerated by AI,
but the tasks sit inside chains where a human remains the bottleneck.
This is where most knowledge workers land.
Automation frontline: a majority of your week consists of tasks
already being performed end-to-end by AI systems in production
settings.
How the score is calculated
[YOUR FORMULA — the weighting applied to each of the seven task
categories, and how the responses combine into the 0–100 score.]
The weights are anchored to two bodies of research. The first is the
task-exposure rubric developed by Eloundou et al., which assessed every
task in the O*NET occupational database for whether a large language
model could reduce the time required by at least half. The second is
observed exposure, a measure introduced by Anthropic economists Maxim
Massenkoff and Peter McCrory in 2026, which weights real usage rather
than theoretical capability and weights automated uses more heavily
than augmentative ones.
We weight toward observed exposure rather than theoretical capability,
because the gap between the two is the single most important finding in
recent labour research. Eloundou et al. found around 94% of tasks in
computing occupations were theoretically exposed. Observed usage in the
same occupations ran at about 33%. Scoring against theoretical
capability alone produces alarming numbers that describe a world that
does not yet exist.
Where the data comes from
- O*NET, the U.S. Department of Labor occupational task taxonomy
- Eloundou et al., “GPTs are GPTs” (2023)
- Anthropic Economic Index, and Massenkoff & McCrory, “Labor market
impacts of AI: A new measure and early evidence” (2026) - Brynjolfsson et al., “Canaries in the Coal Mine?” (2025)
[LINK EACH OF THESE TO THE PRIMARY SOURCE]
Limits and assumptions
This is an estimate based on seven self-reported inputs. It cannot see
your employer, your sector’s regulatory constraints, your seniority, or
the quality of the residual tasks in your role — and recent research
argues that the expertise required by what remains may predict wage
effects better than what gets automated.
Exposure measures technical feasibility and current usage, not
adoption. Integration cost, workflow redesign, and approval processes
sit between capability and deployment, which is why the observed
figures trail the theoretical ones by 50 to 65 percentage points across
every major occupational category.
Deployment moves. This assessment is calibrated against data current to
[DATE] and we revise the weights when new releases of the underlying
indices land.
Privacy
The assessment runs entirely in your browser. Your answers are not
stored, not sent to a server, and not linked to you.
AI JOB QUIZ FAQ
Is this a prediction that I’ll lose my job?
No. It measures how much of your current work AI systems can already
do. Whether that translates into job loss depends on your employer,
your sector, and how quickly organisations restructure — factors this
assessment cannot see.
Why doesn’t it ask for my job title?
Because job titles are a poor proxy for what people actually do. Two
people with identical titles can have entirely different task mixes,
and exposure follows tasks. Every serious study in this field measures
at the task level for this reason.
What does “augmented” actually mean?
That AI can accelerate meaningful parts of your week without being able
to complete the work end-to-end. Most knowledge workers land here. It
usually means the composition of your role changes rather than
disappears — less drafting, more reviewing and deciding.
Should I be worried if I score high?
A high score means your task mix is exposed, not that change is
imminent. The clearest early signal in the research is narrow and
specific: workers aged 22 to 25 entering high-exposure occupations are
finding jobs around 14% less often than peers in low-exposure roles,
with no equivalent effect for workers over 25.
How accurate is it?
It’s an estimate from seven inputs, calibrated against published
research. It’s a useful way to think about the structure of your work,
not a forecast. Treat the verdict as a prompt for a conversation, not a
number to plan around.