Tunisia
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
Several parts of the economy could transmit AI’s effects. The pace depends on adoption by firms, connectivity and movement of workers between sectors.
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 Tunisia starts
Tunisia is a stalled middle: middling income, middling scores, slow growth. Ten-year trend growth is just 0.9% a year, and the gap to faster economies widens with time. Its strengths are human and institutional, not physical. Catch-up headroom and geopolitical positioning both score 3.5, cyber resilience 3.2, and AI diffusion capacity 3.1 — the last built on strong engineering schools that supply offshoring work for French and European firms. The constraints are hard. Compute and energy access scores 1, near the bottom, because Tunisia has almost no data center capacity. Physical build speed is 1.8 and climate and water resilience 1.6. At about $4,657 per person, Tunisia is lower-middle-income, and the capital to fix the physical gaps is scarce. Its manufacturing base — wiring harnesses, aerospace parts, textiles — is the main export earner.
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. Tunisia would edge up to 1% growth a year, 143rd of 157, barely above trend and near the bottom of the table. Under Gradual takeoff, growth would build over two decades and reach 5% a year once AGI matures around 2045. That would lift Tunisia to 130th, and an economy growing at 5% doubles in about fourteen years. Under Fast takeoff, growth would reach 5.7% a year from the early 2030s, a doubling roughly every twelve to thirteen years. Tunisia would climb 18 places to 112th, because a fast world rewards even a partial build-out of compute and diffusion. But these are modest rates by world standards in every case. Politics could slow or redirect any of these paths. How Tunisians and their government respond to transformative AI is a variable the model holds constant.
How you would know which world you are in
The three scenarios look almost the same until about 2030. So watch leading indicators, not GDP, and watch them live. 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 would signal a gradual one. A fade back to trend would signal a modest one. Tunisia's own signals would arrive first: financial close on the Elmed power cable to Italy, any hyperscaler hosting commitment on Tunisian soil, the order books of the automotive and aerospace plants, and whether engineering graduates stay or leave.
If growth goes explosive
For Tunisia, explosive is the wrong word. Even under Fast takeoff, 5.7% growth a year is modest. The frontier would compound near 39% and the regional median near 16%, so Tunisia would fall further behind both. This is the stalled-middle risk: the gap to faster economies compounds year by year. Still, the change from today would be real. At 5% growth the economy doubles in about fourteen years, so a child starting school now would finish it in a country roughly twice as rich. Higher wages at home would ease the pressure on engineers and graduates to emigrate for work. The honest downside is dependence. If Tunisia rents its computing power rather than owning it, prices and access are set abroad, and a rule-taker inside another power's chip regime has little say. Brain drain is the sharper threat: talent can leave faster than capacity is built. The hedge is to build owned capacity and keep skilled people at home. Even a modest build would test the state — STEG's ability to add generation, the speed of permitting, and the ministries that must approve projects and count the results.
What Tunisia can do now
Three levers follow from the scores, and two pay off whether or not AGI arrives. The first is AI diffusion capacity, at 3.1 already Tunisia's relative strength. Digital skills, permissive deployment rules and digital government carry weight even in the skeptic's world, so protecting and extending this is a no-regret move. The second is physical build speed, low at 1.8. Statutory permitting deadlines, one-stop approvals and pre-zoned land with transmission built ahead of demand help the economy under every scenario, and they are what any later data center build would need — also no-regret. The third lever is the bet. Compute and energy access, at 1, barely moves growth if AI stays ordinary, but under a fast takeoff it becomes Tunisia's single biggest lever. Closing it means offering power and land packages to anchor investment, which a $4,657-per-person economy cannot fund alone. Tunisia can copy a working model: Thailand drew multibillion-dollar cloud investment from AWS and Google by pairing incentives with power. A cautious government can act on the two no-regret levers today and lose nothing. Open the atlas's policy editor to see how moving these scores reshapes each path.
Use the policy editor to test how different factor scores change Tunisia’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 Tunisia: 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.