Eswatini
GDP per capita to 2060, by scenario
Constant-rate scenario path; the 2060 endpoint levels are illustrative, not forecasts.
Factor profile
Firms moved factory and office work here for its labor cost: assembly lines, garments, call centers and IT services sold abroad. AI and robots can increasingly do that work.
How this economy meets AI
Today’s production, workforce and automation baseline — context for the scenarios, outside the AGI Growth Index.
Where output comes from (% of GDP)
Where people work (% of employment)
Informal employment
Current automation exposure
Production is relatively manufacturing-intensive. Near-term effects depend on machinery, robotics, supply chains, energy and firms’ capacity to reorganize production, as well as on language models.
This country is not covered by the Global Automation Atlas’s public country file; its task-level exposure work for other countries is at automationatlas.org.
Sources: World Bank WDI; ILOSTAT; Global Automation Atlas (Garg, Crosta & Baier, 2026). The latest available year is shown for each measure; vintages differ, so shares may not sum to 100.
Where Eswatini starts
Eswatini sits in the stalled middle: middling income, low scores, slow growth, and a widening gap to every faster group. It is a small landlocked kingdom of 1.26 million people, tied to South Africa by customs union and a pegged currency. GDP is about $5 billion, or $4,108 per person — lower-middle-income. Ten-year trend growth is 2.3% a year. Its one relative strength is catch-up headroom, scored 3.4: room to grow simply by adopting technology already common elsewhere. The constraints are physical. Compute and energy access scores 1, the floor on this map, and physical build speed 1.1. The economy leans on sugar, textiles and the Coca-Cola concentrate plant at Matsapha, with a large share of fiscal receipts from the Southern African Customs Union. AI diffusion capacity, at 2, is thin.
The three paths
These are simulations, not forecasts. The Modest path is the baseline, and the two AGI scenarios fork from it. Under the Modest scenario, AI stays a normal technology. Eswatini would hold its trend at 2.3% growth a year, ranking 103rd of 157. Under Gradual takeoff, growth would still sit at 2.3% a year even after AGI matures around 2045, at the end of a two-decade ramp. Its rank would fall to 154th, because other economies speed up while Eswatini does not. Under Fast takeoff, with frontier rates arriving from the early 2030s, growth would reach just 2.4% a year, and the rank would be 152nd. The striking result is that AGI barely changes Eswatini's growth in any scenario. What changes is its place in the line, as faster economies pull away. Regulation and politics could still slow or redirect any path, depending on how Swazis and their government respond to transformative AI. The model holds that response constant.
How you would know which world you are in
The scenarios look almost identical for Eswatini through the 2020s and beyond. Watch leading indicators, and watch them live rather than trust one snapshot. Three global signals matter most. First, whether the METR task horizon curve keeps doubling every four to five months through 2027–28; METR publishes it at metr.org. Second, whether frontier lab revenue and capital spending keep compounding; Epoch AI tracks both at epoch.ai. Third, whether growth in the ten to twenty most-ready economies — the United States and China above all — breaks above trend in the early 2030s. Double-digit growth there within a couple of years of 2030 would signal a fast world; a point or two above trend by the mid-2030s a gradual one; a fade back to trend a modest one. Eswatini's own tells would arrive earlier than GDP: electricity capacity added by the Eswatini Electricity Company, any hosting commitment at the Royal Science and Technology Park, and digital-service adoption in government and banking.
If growth goes explosive
For Eswatini, it does not. Its rates stay modest in every scenario: 2.3% under Gradual, 2.4% under Fast. The danger here is not a bad path but a slow one while others race. The frontier could compound near 39% growth a year under Fast, and even the regional median could reach about 4.9%. Eswatini would sit below both. That is relative decline: the economy would keep inching forward, but the gap to every faster group would widen year by year. The shape of the economy would change little, with sugar, textiles and concentrate still dominant. Because wages at home would not rise quickly, the pull toward jobs in South Africa would persist, and Eswatini's most skilled workers are the likeliest to leave. Any computing power the country uses would be rented and controlled from abroad, so it could be repriced by decisions made elsewhere. The hedge is to keep returns at home: build digital skills and domestic services rather than depend on borrowed capacity. Even a slow path would test the state — the ministries, the Eswatini Electricity Company, the communications regulator, and a budget that leans heavily on customs-union transfers.
What Eswatini can do now
Three levers follow from the scores, and one pays off whether or not AGI arrives. Start with AI diffusion, scored 2. Digital skills, deployment-permissive rules and digitized government services raise productivity even in the skeptic's world, so this is the no-regret move a cautious government can make today at no loss. It also feeds the catch-up headroom that is Eswatini's one relative strength. The second lever is physical build speed, at 1.1: statutory time limits on permits, one-stop approvals, and land and transmission readied ahead of demand. The third is compute and energy access, the floor score of 1: adding generation and grid links, and packaging power and land to attract any hosting investment. These last two are AGI-contingent bets. They barely move growth if AI stays ordinary, and even under Fast they lift Eswatini only slightly, because it starts so far back. Eswatini can adapt a peer's model: Cabo Verde, another small African state, built the Praia Technology Park with development-bank finance to anchor digital services. To see how moving these scores changes Eswatini's path under each scenario, open the atlas's policy editor.
Use the policy editor to test how different factor scores change Eswatini’s modeled path. The roadmap builder below can then incorporate those targets into a 1,000-day planning prompt.
Drafted with Claude, Anthropic’s AI model, from the atlas’s data and an editorial template set by the author.
Create a 1000-day AGI and growth roadmap prompt
This prompt can be used with Claude, ChatGPT or an open-weight model hosted on your institution’s systems. It includes the atlas material for Eswatini: the country brief, economic structure, region, archetype and global scenarios. It instructs the model to ask for local information, use existing strategies and the political calendar, and preserve the atlas’s interpretation rules. The prompt is assembled in your browser; the information entered here is not sent elsewhere.
1 · Read the brief → 2 · Set targets in the policy editor → 3 · Generate and copy the prompt → 4 · Run it with an approved model, with high reasoning effort and web access where permitted
Instructions are generated in the selected language; the data annex remains in English. Translations are machine-drafted, and corrections from native speakers are welcome through the contact links on the start page.