Nest-building-and-destroying weaver birds may seem unusually profligate with energy. But they are by no means the only species besides us inclined to spend energy on apparently pointless work. The avian kingdom alone is blessed with thousands of similar examples of expensive elaboration, from the grandiose plumage of birds of paradise to the over-elaborate nests of bowerbirds.

In the 1950s and ’60s, as developmental economics took root in Chile and Uruguay, favoring strong government regulation and an inward focus on industrialization as the path to maturing a developing economy, American multinationals who had made billions from their holdings in Chilean mines began to chafe against the growing state taxes and restrictions. The US government subsequently embarked on a quest to refashion Chile’s economic policies to be more favorable to American business interests, launching a program in 1956 called “the Chile Project” to educate a hundred Chilean students at the University of Chicago under the intellectual tutelage of American economist Milton Friedman.

Under Pinochet’s rule, Chile privatized nearly everything—education, health care, the pension system, even water. The strategy produced economic growth; it also fueled stunning inequality. Chile is among the most unequal countries in the world today, with nearly a quarter of the country’s income concentrated among a few powerful families in the 1 percent. Having never meaningfully industrialized, it also remains tethered to the extraction economy that makes it relevant to higher geopolitical powers.

And so, as the AI boom arrived, Chile would become ground zero for a new scale of extractivism, as the supplier of the industry’s insatiable appetite for raw resources, not just its copper and lithium in the north but also its land, water, and energy resources for a growing crop of data centers in the Santiago metropolitan region. In May 2024, the government proudly announced that the country would welcome twenty-eight new data centers, on top of its existing twenty-two, over the coming years, bringing in $2.6 billion of foreign investment.

In Brazil, a 2023 art exhibition coproduced by a Chilean university showed the vast chasm between the reality of Latin America’s rich Indigenous cultures and the woefully bereft depictions of them spit out by Midjourney and Stable Diffusion as primitive, technologically inept peoples.

In recent years, the Atacameños have mounted more and more resistance. They fly black flags on their houses to denounce the exploitation of their lands and their community. They’ve organized protests to physically block the roads that company buses and trucks must take to get to the mines. They’ve contracted lawyers to assert their legal rights as Indigenous peoples under international law, which protects their cultural and territorial sovereignty. As companies and the Chilean government have been forced to invite them to negotiations, central to Indigenous demands are the need for the government to conduct research into the health of the Atacama Desert’s ecosystems and to quantify the water loss and any irreparable damage.

This is where Te Hiku’s story diverges completely from OpenAI’s and Silicon Valley’s model of AI development. Intimately familiar with the devastating effects of colonial dispossession, Jones and Mahelona were determined to carry out the project only if they could guarantee three things—consent, reciprocity, and the Māori people’s sovereignty—at every stage of development. This meant that even before embarking on the project, they would get permission from the Māori community and their elders, asking them if the endeavor was even something they wanted; to collect the training data, they would seek contributions only from people who fully understood what the data would be used for and were willing to participate; to maximize the model’s benefit, they would listen to the community for what kinds of language-learning resources would be most helpful; and once they had the resources, they would also buy their own on-site Nvidia GPUs and servers to train their models without a dependency on any tech giant’s cloud.