LOThe world is changing. You have more than one option.
Understand how AI, robotics and automation are changing your work and your studies — and find a next step that actually fits your situation.
Where are you standing right now?
The same change reads differently depending on whether you hold the job, are studying for it, employ it, or are building for it.
One task, as it actually reads
Taken straight from the site — one of 339 task judgements across 67 occupations. Every one of them carries what it does not establish, and says whether it rests on a verified record or on our inference.
Generated tests derive from the specification or the code, so they share its blind spots. Finding the failure nobody imagined requires a model of how real users and real systems misbehave, built from experience with this product and this domain. Tools widen the search; the hypothesis about where to look is still human, and it is where the expensive bugs are.
There is no single curve
Same period, same method, four occupations. One nearly sextupled, one barely moved, and one came back down. A single number for 'AI and jobs' has to average these together, which is how it stops meaning anything.
- Copywriter12 → 76 · rose the most
- Machine learning engineer34 → 47 · the only one that came back down
- Loan officer / credit officer40 → 61 · the middle of the set
- Care worker / nursing assistant12 → 17 · barely moved
Which four are shown is computed, not chosen — steepest, the one that fell, the median, flattest. How the curve is reconstructed →
Start with an occupation
All occupations →Each one is broken into its actual tasks — which are automating, which still need a person, and which are new because of automation.
Moves meaning between languages — and decides what to do when the meaning does not move cleanly.
Handles the transactions and the people at a branch counter — and is the bank's face for customers who need a human.
Writes words meant to make a specific person do a specific thing — and is judged on whether they did.
Answers customers who have a problem — and decides, case by case, what the company will do about it.
Keeps an office or an executive running: calendars, travel, expenses, documents, and every small thing that would otherwise stop.
Finds out how the software fails before users do — and is the person who says whether it is ready to ship.
Makes visual decisions on someone else's behalf — and defends them.
Does the reading, drafting and organising that lets a lawyer sign off — and is accountable for nothing being missed.
Takes payment, answers questions, restocks shelves and keeps a shop floor working — often all in the same hour.
Turns hours of footage into the minutes someone will actually watch — and decides what the story is in the process.
Records, reconciles and reports on an organisation's money — and increasingly, explains what the numbers mean to people who make decisions.
Gets a specific group of people to notice, want and buy something — across channels that change every year.
Questions worth a long answer
All notes →Every section of these cites a verified record or a task judgement, and prints what it rests on directly underneath. A section with no citation fails our build.
How many jobs has AI actually replaced so far?
There is no single number, and the honest reason is not that the data is missing — it is that almost every published figure counts something other than a job. Here is each one we could check, and what it actually counts.
Which parts of my job will AI actually take over, and how can I tell in advance?
Not how hard the task is. Not how well-paid. The property that predicts whether a task moved is whether something other than a human being can say 'that is wrong, try again' — and say it a thousand times overnight without getting tired.
How many years do I have before AI takes my job?
We refuse to answer this with a number, and that refusal is enforced by a test that fails the build. This is the argument behind it: every ingredient a year-estimate would need is either unmeasured, reversible, or decided by someone who has not decided yet.
How does AI regulation actually change my job, in practice?
Most writing about AI and work describes technology arriving and jobs changing afterwards. In the verified record the commonest sequence runs the other way: a rule creates a duty, the duty lands on a named role, and somebody's job description grows a paragraph before anything is deployed.
Which parts of a business does AI actually get deployed into first?
Adoption is narrower than the coverage suggests and lands in the same three places. The interesting question is not which function buys the tool — it is which role inherits the work the tool creates, because in the verified record that role is almost never the one that chose it.
Still studying?
All majors →A major is not one job. Each page breaks down the competencies it actually trains, the several directions they lead, and one thing you can test this term.
