We know surprisingly little about how automation will unfold outside rich countries. So we built the Global Automation Atlas: 18,000 tasks, 124 countries, and 2.3 million task-country comparisons.

Two highlights: #1: automation exposure rises with income. But countries at low- and middle-income levels still differ substantially.

#2: when a task is exposed to automation, is technology substituting core worker input, or mainly augmenting workers to do the task? In most countries, substitution-oriented exposure is larger, especially in lower-income settings.

Explore the Atlas for more results: This includes our paper, open data+code and interactive visuals. We couldn't do it all, so we released data and code, and linked our measures to common units of analysis to enable future research: occupation, industry, and skills.

This work wouldn't be possible without my amazing co-authors @TommasoCrosta and @jasmin_baier. We have a lot more planned, and would love your feedback!

Nice! Been waiting for people to do this - looks awesome!

Thanks!! Hopefully it moves us forward on this literature!

@ATabarrok @tylercowen

why the heck is this not viral af this is so cool, incredible work!!

hah, thank u!!

very cool. i'm curious which exact "automations" are we talking about here? and lets hope you do not get cancelled for using wrong India map 😭😭

it's a very good question, and we consider all the major categories of automation. This is often overlooked, so we decided to explicitly measure the channel of automation for each task, in each country

very interesting!

What a cool work Prashant ! And a needed one ... Bravo to the team ! 👏👏

Well done! Thank you.

With just a glimpse, fantastic work!

“A glimpse into the world proves horror is nothing other than reality” Hitchcock

That's what those altases are for 🤫

Wonder what the equilibrium strategy for knowledge workers will be ? I am personally inclined towards becoming a chef.

There’s dancer, firefighter, caretaker and massage therapist.

Great to see the global south included in in more literature! I wonder if this might lead to some degree of convergence (even if slight) in wages, due to increases in capital elasticity compared to labor elasticity for the richer countries.

🧐

@KhoaVuUmn Another great project @Prashant_Garg_

@KhoaVuUmn 🦄

Important work. The richest-country lens makes automation look like productivity; elsewhere it may reshape bargaining power, informality, and state capacity first. 'The benefits are real. But so is the trajectory.' - The Universe Without Noise, R A Hewitt

how do you define exposure? And how do you measure it changing over time?

Clearest version is here, from paper. Tried to follow the literature in designing the definition in prompt. Unfortunately, it is a forward looking metric as of March 2026, so it is cross-sectional.

tasks is fine but how do you evaluate model capabilities with AI systems - how good it actually is? and... what if the workflow changes?

we did lots of validation checks (see bottom bit in page, details in paper). One method is to compare various measures we create to closest available external dataset. Others are to see consistency across models and to check internal reasoning provided by the model systematically

Very interesting!

Thank you!!

Automation data at global scale

Cool! Awesome.great work.

Thanks!!

@sanjeevsanyal @NITIAayog @TVMohandasPai @PMOIndia

@Prashant_Garg_ this is great!

To what extent does this depend on how dependent the nation’s economy is on the services sector?