Землетрясения в Венесуэле: жители на ИИ собирают приложения для поиска пропавших

After a series of earthquakes in western Venezuela that began on November 24, 2025, local developers and ordinary residents used AI to build websites and apps within hours to search for missing people and coordinate aid — against the backdrop of a slow response from state services. This is reported by Rest of World.

How AI replaced slow aid

Venezuelan residents used AI code generators to quickly deploy the digital rescue infrastructure that authorities failed to launch in time. Instead of waiting for official channels, programmers and volunteers described the task to a neural network in words and received ready-made code: lists of missing people, maps of destruction, collection points for humanitarian aid. Part of the work was carried out by Venezuelans abroad — the diaspora coordinated aid remotely, which is also reflected in the headline of the 2026 Rest of World piece.

Venezuela's state services have worked for years under economic crisis and resource shortages, so in the first days after the tremors, official coordination simply failed to reach many affected areas — and residents themselves began to fill this vacuum.

*Location — western Venezuela, the earthquake zone (tremors since November 24, 2025, with a magnitude of up to 6.2)

*Tools — AI code generators: a website or app is built without a large team

*Tasks — searching for missing people, maps of destruction, coordinating volunteers and supplies

*Who — local developers, ordinary residents, and the diaspora abroad

*Reason — slow response from official services

Developers and citizens used AI to build websites and apps to search for missing people and coordinate aid, — the

Rest of World piece states.

What residents built with AI

The main products were simple web apps for specific disaster tasks: databases of missing people, interactive maps of affected areas, and services that bring together those who need help and those offering it. The key difference from past disasters is speed: what used to require a team of developers and weeks of work is now put together by one person in a few hours. Generative AI takes on the coding, while the author only needs to formulate what exactly should work.

Technically, behind most of these projects are mass-market AI code-generation tools and no-code platforms: a user describes the needed function in plain language, and the model produces a ready-made page or form. This is exactly what, in 2026, is called "vibe-coding" — assembling software through dialogue with a neural network rather than manual programming.

At the same time, such services carry risks. Data on missing people is often unverified by anyone, entries get duplicated, and people's personal information ends up in open forms without protection. According to Rest of World, the initiative was bottom-up — it was built by the affected people and volunteers themselves, not the state, so there is no unified standard of quality and privacy for such tools.

Why this matters for civic technology

The Venezuela case shows that generative AI has lowered the barrier to entry for building crisis services almost to zero. In 2026, a person without a team or budget can launch a working missing-persons search website in a single evening — where until recently programmers and weeks were needed. This turns ordinary residents into "citizen developers" and helps close the gap left by slow bureaucracy in the first, most critical hours for rescue after a disaster.

Similar grassroots digital command centers have emerged in past disasters too, but they used to be built by professional volunteer programmers. AI has, for the first time, made this kind of work accessible to almost anyone with a laptop and internet access.

What this means

Generative AI is becoming a tool of civic self-organization in disasters: when state aid is slow to arrive, residents are increasingly assembling their own digital services within hours rather than weeks. Venezuela in late 2025 – early 2026 became one of the first notable examples of such "grassroots" AI-driven disaster response.