Still, if Bengio, Hinton, and Sutskever had been sidelined by capital, the points they made remained valid. They had seen better than anyone the potential of what AI technology could be, and they had the academic credentials to prove it. If they were worried now, I felt it was worth listening to. “Right now there are ninety-nine very smart people trying to make AI better and one very smart person trying to figure out how to stop it taking over,” Hinton said.

There is a different way forward. Artificial intelligence doesn’t have to be what it is today. We don’t need to accept the logic of unprecedented scale and consumption to achieve advancement and progress. So much of what our society actually needs—better health care and education, clean air and clean water, a faster transition away from fossil fuels—can be assisted and advanced with, and sometimes even necessitates, significantly smaller AI models and a diversity of other approaches. AI alone won’t be enough, either: We’ll also need more social cohesion and global cooperation, some of the very things being challenged by the existing vision of AI development.

That tradition of anthropomorphizing continues to this day, aided by Hollywood tales combining the idea of “AI” with age-old depictions of human-made creations suddenly waking up. AI developers speak often about how their software “learns,” “reads,” or “creates” just like humans. Not only has this fed into a sense that current AI technologies are far more capable than they are, it has become a rhetorical tool for companies to avoid legal responsibility. Several artists and writers have sued AI developers for violating copyright laws by using their creative work—without their consent and without compensating them—to train AI systems. Developers have argued that doing so falls under fair use because it is no different from a human being “inspired” by others’ work. The omnipresent AI-to-human analogies have also fueled the sense that such software could become so capable that it surpasses us and comes to threaten our very existence. The fear of superintelligence is predicated on the idea that AI could somehow rise above us in the special quality that has made humans the planet’s superior species for tens of thousands of years.

In total, it presented four key warnings: First, large language models were growing so vast that they were generating an enormous environmental footprint, as found in Strubell’s paper.

Second, the demand for data was growing so vast that companies were scraping whatever they could find on the internet, inadvertently capturing more toxic and abusive language as well as subtler racist and sexist references.

Third, because such vast datasets were difficult to audit and scrutinize, it was extremely challenging to verify what was actually in them, making it harder to eradicate toxicity or more broadly ensure that they reflected evolving social norms and values. Finally, the model outputs were getting so good that people could easily mistake its statistically calculated outputs as language with real meaning and intent. This would make people prone not only to believing the text to be factual information but also to consider the model a competent adviser, a trustworthy confidant, and perhaps even something sentient.

The critiques that I lay out in this book of OpenAI’s and Silicon Valley’s broader vision are not by any means meant to dismiss AI in its entirety. What I reject is the dangerous notion that broad benefit from AI can only be derived from—indeed, will ever emerge from—a vision for the technology that requires the complete capitulation of our privacy, our agency, and our worth, including the value of our labor and art, toward an ultimately imperial centralization project.

Te Hiku shows us another way. It imagines how AI and its development could be exactly the opposite. Models can be small and task specific, their training data contained and knowable, ridding the incentives for widespread exploitative and psychologically harmful labor practices and the all-consuming extractivism of producing and running massive supercomputers. The creation of AI can be community driven, consensual, respectful of local context and history; its application can uplift and strengthen marginalized communities; its governance can be inclusive and democratic.