Investigation

For many years now, multinationals have been collecting an increasing amount of genetic, proteic sequences and epigenetic informations. They are reducing living organisms to data compiled in digital databases. Using “artificial intelligence” algorithms, they claim to have the tools to determine which genetic modifications will produce a given new characteristic. In a society where genetic modification techniques and patents are intimately linked, these algorithms will above all accelerate the claim to own living organisms.

Inf’OGM interviewed Stéphane Galais, a farmer in Ille-et-Vilaine (Brittany, France) with a 25-hectare farm producing milk and cheese. He is also national secretary of the Confédération paysanne. In this interview, he explains and analyses what agritech means to him.

In 2025, the European Union is expected to adopt a “biotech act” aimed, among other things, at modernising agriculture through new technologies. At the same time, public policies, in particular the Common Agricultural Policy (CAP), are guiding farmers towards ever more expensive and sophisticated mechanisation. As a result, a “high-tech” and soil-less agricultural model is taking shape in Europe, with the risk of increasing farmers’ indebtedness and marginalising small-scale farming.

“Artificial intelligence” (AI) is used to extract and restructure information from raw or unstructured data. Companies are using it to identify phenotypic traits associated with genetic sequences. Referring to a recent patent application by the company Inari, the German NGO Testbiotech points out the risks associated with the combination of new genomic modification techniques (NGT) and AI. It condemns the possible abuses of patentability and the need for robust GMO regulations. However, Inari has already filed other similar applications, which raises questions about the legal impact of such rights and their adaptation to AI-driven technologies.

The digitization of living organisms is the subject of a growing number of projects. Computer data, generated and stored in ever-larger “data centers”, are used by “artificial intelligence” matrices. These data are of all kinds: genetic sequences, proteins, etc. In these fields, which require increasing natural resources, investments are multiplying.