Will Venice be inhabitable in 2100? What kinds of policies can we develop to navigate the best scenarios for this floating city? In 2012, the École Polytechnique Fédérale de Lausanne (EPFL) and the University Ca’Foscari launched a programme called the Venice Time Machine to create a large-scale digitisation project transforming Venice’s heritage into ‘big data’. Thanks to the support of the Lombard Odier Foundation, millions of pages and photographs have been scanned at the state archive in Venice and at the Fondazione Giorgio Cini. While commercial robotic scanners were used at the archives, a new typology of robotised circular table was developed by Adam Lowe and his team at Factum Arte to process the million photographs of Fondazione Giorgio Cini. The documents were analysed using deep-learning artificial-intelligence methods to extract their textual and iconographic content and to make the data accessible via a search engine. Also during this time, thousands of primary and secondary sources were compiled to create the first 4D model (3D + time) of the city, showing the evolution of its urban fabric. This model and the other data compiled by the Venice Time Machine were part of an exhibition at the Venice Pavilion of the Biennale of Architecture in 2018, shown side-by-side with potential projects for Venice’s future. Having reached an important milestone in convincing not only the Venetian stakeholders but also a growing number of partners around the world that care about Venice’s future, the Venice Time Machine is now raising funds for the most ambitious simulation of the city that has ever been developed. Its planned activities include a high-resolution digitisation campaign of the entire city at centimetre scale, a crucial step on which to base a future simulation of the city’s evolution, while also creating a digital model that can be used for preservation regardless of what occurs in the coming decades. On the island of San Giorgio Maggiore, a digitisation centre called ARCHiVe (Analysis and Recording of Cultural Heritage in Venice) opened in 2018 to process a large variety of Venetian artefacts. This is a joint effort of Factum Foundation, the École Polytechnique Fédérale de Lausanne and the Fondazione Giorgio Cini, along with philanthropic support from the Helen Hamlyn Trust. The centre aims to become a training centre for future cultural heritage professionals who would like to learn how they can use artificial intelligence and robotics to preserve documents, objects and sites. These operations will work together to create a multiscale digital model of Venice, combining the most precise 4D information on the evolution of the city and its population with all the available documentation of its past. The project aims to demonstrate how this ‘digital double’ can be achieved by using robotic technology to scan the city and its archives on a massive scale, using artificial intelligence techniques to process documents and collecting the efforts of thousands of enthusiastic Venetians. In a project called ‘Venice 2100’, the Venice Time Machine team’s ambition is to show how a collectively built information system can be used to build realistic future scenarios, blending ecological and social data into large-scale simulations. The Venice Time Machine’s ‘hypermodel’ will also create economic opportunities. If its hypotheses are valid, Venice could host the first incubators for start-ups using big data of the past to develop services for smart cities, creative industries, education, academic scholarship and policy making. This could be the beginning of a renewal of Venice’s economic life, encouraging younger generations to pursue activities in the historic city, at the heart of what may become one of the first AI-monitored cities of the world. Venice can reinvent itself as the city that put the most advanced information technology and cultural heritage at the core of its survival and its strategy for development. Artificial intelligence can not only save Venice, but Venice can be the place to invent a new form of artificial intelligence.