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Economist
Forecasts the economy, builds and runs models on economic data, advises governments, central banks and companies on policy, and writes research. Central banks already use machine learning in forecasting and modelling, but say it is not always better than traditional methods and that it complements rather than replaces expert judgement; about a quarter of their AI use cases have reached full production. Language models make telling errors with economic data, mixing first releases with revisions, and in a randomised study teams that handed reproduction work to AI succeeded far less often than human teams. The US Federal Reserve says AI is not used to set policy. The US projects 5% growth for economists.
Take one forecast you produce and write down which data vintage each input comes from.
This is not a probability of losing your job. It combines how much of the role's task load is exposed to automation with how far adoption has actually gone — useful for comparing occupations on one consistent basis, and for nothing else.
Written for economists in central banks, government, international organisations, consultancies and research; statisticians, data scientists and financial analysts have their own pages. The evidence is a US labour projection, a survey of central banks' AI use, the head of Germany's central bank on its machine-learning models, a Federal Reserve study of language models' errors with economic data, a randomised study of reproducing economics research with AI, and a Federal Reserve governor on what AI is not used for; most of it comes from central banks, and it establishes how institutions use and limit AI, not how economists' numbers or hours have changed.
What is actually changing#
The unit of analysis is the task, not the job title. A role is not replaced — its task mix shifts.
Each tile is one task. Its size is how much of the job it is; its colour is where the task is heading. Click a tile to see what the judgement does not establish.
Central banks' own accounts and one staff study; they do not measure forecast accuracy in practice or economists' time.
One experiment with 2024 models and one institution's statement; newer tools may do better.
One central bank's statement; it does not show how briefings are drafted elsewhere.
Inferred from institutions' descriptions; no record measures research output or quality.
Is this your job? Say so and this page narrows to your share of it.
A job title is a bundle of tasks bought together, and no two people hold the same bundle. Nothing is sent anywhere — it stays in this browser.
Read all 4 tasks in full — direction, reasoning and limits →
Recent changes#
United States. The statistics bureau projects employment of economists to grow 5 percent from 2025 to 2035, from 18,600 to 19,500, with about 1,100 openings a year. It says demand will come from organisations applying big-data analysis to pricing, advertising and other areas and from a more complex global economy, but that employment will depend partly on government budgets. It does not mention AI.
A named person with standing publicly predicted something, on a date, in an attributable statement. It is recorded so that who said what, and when, stays checkable — and it never moves a task's assessment, because a prediction is not an observation. Its value arrives later: the record sits on the same page as the evidence about that occupation, so anyone reading the forecast reads the record of what happened next beside it. That is the reckoning; this site publishes no verdict on whether a forecast came true.
Germany. The central bank's president says machine-learning applications are used to model inflation, interest rates and fiscal expenditures as well as loan and trade volumes, that experience shows these approaches are not always superior to traditional ones, and that AI complements staff as an additional tool and does not replace expert knowledge; the bank has also released a platform for staff to configure text-based assistants. The head of the institution describing its own work.
An employer has put it into production. Can move the baseline — weighted by scale and how similar the setting is.
United States. The governor says the Federal Open Market Committee is not using AI in developing or setting policy but rather to aid staff in other tasks such as writing, coding and research, and that research at the Fed is proceeding deliberately and cautiously because many AI tools are not yet ready to be put into production. A policymaker's statement about one institution.
Failure, rollback, regulation or cost is suppressing adoption. Can lower an assessment or widen its uncertainty.
United States, Federal Reserve Board staff. Testing language models' recall of macroeconomic data, the authors find accurate recall overall but two important errors: mixing first-print data with later revisions and mixing data for past and future periods; on any given day the model is likely to believe it has data in hand that has not been released, which limits its use for historical analysis or to mimic real-time forecasters.
A demo, benchmark or paper shows the task can be done. Updates what the technology can do — not what employers will do.
Central banks worldwide, surveyed from September to November 2024. The report says AI applications have advanced most in statistics and economic research, in particular nowcasting, sentiment analysis and outlier detection, but that just about a quarter of AI use cases make it to full-scale implementation, so central banks are still mostly experimenting rather than using AI in day-to-day operations. It counts use cases, not staff time.
Small-scale trial in a real setting. Tells us the deployment conditions are being tested, not that they hold — so one pilot is never enough on its own; two independent ones are.
An experiment with 288 researchers randomly assigned to 103 teams that computationally reproduced published social-science results: human-only teams, AI-assisted teams and teams that minimally guided an AI. Human teams matched AI-assisted teams' success rates and achieved 57 percentage points higher success than AI-led teams, and found significantly more major errors than both. A discussion paper using 2024 models.
A demo, benchmark or paper shows the task can be done. Updates what the technology can do — not what employers will do.
What this means for you#
If you are starting out, expect machine-learning models and AI coding help to be part of the job. Build what institutions say they still need people for: knowing the data and its revisions, judging when a model is wrong, and turning analysis into advice someone can act on.
Expect faster coding and more model output to check. Judgement about what the numbers mean for a decision stays with you.
Your options#
Four directions, each with its real constraints and one thing you can test this week. Continuing as you are is a legitimate choice — it just has to be a chosen one.
Become the person who checks the models
Central banks use machine learning alongside traditional models and say it is not always better, and language models mix up data vintages.
Model validation work sits mostly in larger institutions.
Take one forecast you produce and write down which data vintage each input comes from.
Stay in policy analysis and advice
Central banks say AI is not used to set policy, and the US projects growth for economists.
Policy roles are few and often depend on government budgets.
List which parts of your last briefing were judgement and which were data work a tool could have done.
Move into data engineering for economic statistics
Central banks' AI work concentrates in statistics and nowcasting, which needs people who understand both the data and the methods.
It needs strong programming skills, and statistician and data scientist roles compete for it.
Find out which datasets in your organisation are built by hand and who maintains them.
Common questions#
Not on present evidence. Central banks use machine learning in forecasting and modelling but say it complements rather than replaces expert knowledge, the Federal Reserve does not use AI to set policy, and the US projects 5% growth for economists.
We do not answer that with a number of years. Watch whether central banks move more AI from experiments into production, and whether models stop confusing data vintages. Those tell you more than any date.
Not reliably on present evidence. Germany's central bank says machine learning is not always superior to traditional approaches, and Federal Reserve staff found language models mix first releases with revisions and assume data that has not yet been published.
Not well in the evidence so far. In a randomised study of reproducing published results, teams that minimally guided an AI succeeded 57 percentage points less often than human teams, while AI-assisted teams did as well as humans but no better.
What these judgements rest on#
3 of 4 task judgements on this page are backed by a verified event and 1 are platform inference, each labelled where it appears. Behind them sit 1 technology dimensions, a reconstructed trajectory since language models reached the public, and 6 verified events.
See which technologies, how it got here, and the method →
Where it sits in the official classification: skills, knowledge, related jobs →
Other roles in the same function#
A company divides its work into functions before it divides it into jobs. These sit in Technology & data alongside this one — a fact about org charts, not a judgement that they are similar or that they are changing in the same direction.
Junior software developer · Experienced software engineer · Frontend developer · Backend developer · Data engineer · Machine learning engineer · AI researcher · Software tester / QA engineer · Data analyst · Data scientist · Statistician · Business analyst · Business systems owner · IT support specialist / helpdesk · Security analyst (SOC) · DevOps engineer / platform engineer / SRE · Network engineer · Technical writer / documentation engineer · Grant writer / grants officer