Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI

by Karen Hao

For the first time, OpenAI also spelled out its AGI definition: “highly autonomous systems that outperform humans at most economically valuable work.”

Now, scientists were re-creating intelligence—an idea that would define the field’s measures of progress and would decades later birth OpenAI’s own ambitions. But the central problem is that there is no scientifically agreed-upon definition of intelligence.

What’s left unsaid is that in a vacuum of agreed-upon meaning, “artificial intelligence” or “artificial general intelligence” can be whatever OpenAI wants.

The Costs of Connection, published just that year, argued that Silicon Valley’s pervasive datafication of everything was leading to a return of disturbing historical patterns of conquest and extractivism.[*] The following year, a paper called “Decolonial AI” from Shakir Mohamed and William Isaac at DeepMind and Marie-Therese Png at the University of Oxford reinforced a suspicion I had begun to develop: The AI industry, in equal parts fueled by and fueling this datafication, was in turn accelerating that new colonialism further.

In fact, deep learning models are inherently prone to having discriminatory impacts because they pick up and amplify even the tiniest imbalances present in huge volumes of training data.

The term extractivism comes from the Spanish word extractivismo and the Portuguese word extrativismo, coined decades ago by Latin American scholars seeking to describe a global economic order that was dispossessing them of their natural resources for little local or regional benefit,

Extraction is the not inherently damaging removal of matter from nature and its transformation into things useful to humans. Extractivism, a term born of anti-colonial struggle and thought in the Americas, is a mode of accumulation based on hyper-extraction with lopsided benefits and costs: concentrated mass-scale removal of resources primarily for export, with benefits largely accumulating far from the sites of extraction.”

But the consistency of workers’ experiences across space and time shows that the labor exploitation underpinning the AI industry is systemic. Labor rights scholars and advocates say that that exploitation begins with the AI companies at the top. They take advantage of the outsourcing model in part precisely to keep their dirtiest work out of their own sight and out of sight of customers, and to distance themselves from responsibility while incentivizing the middlemen to outbid one another for contracts by skimping on paying livable wages.

“We now have machines that can mindlessly generate words, but we haven’t learned how to stop imagining a mind behind them,” said Bender.

Most crucially, Te Hiku would create a process by which the data it collected would continue to be a resource for future benefit but never be co-opted for projects that the community didn’t consent to, that could exploit and harm them, or otherwise infringe on their rights. Based on the Māori principle of kaitiakitanga, or guardianship, the data would stay under Te Hiku’s stewardship rather than be posted freely online; Te Hiku would then license it only to organizations that respected Māori values and intended to use it for projects that the community agreed to and found helpful.

another lesson to be drawn from Te Hiku’s experience that the three hundred ten hours still proved sufficient for developing the very first te reo speech-recognition model with 86 percent accuracy. Where OpenAI seeks to develop singular massive AI models that will do anything, a quest that necessarily hoovers up as much data as possible, Te Hiku simply sought to create a small, specialized model that excels at one thing. In addition, Te Hiku benefited from the cross-border, open-source AI community: As its starting point, it used a free speech-recognition model from the Mozilla Foundation called DeepSpeech, which itself is an artifact of a different vision of AI development. Like Te Hiku, Mozilla trained the model only on data donated with full consent and built it using a neural network architecture developed by the Bay Area–based research lab of the Chinese company Baidu. In all, Te Hiku used only two GPUs.