President Trump speaks to reporters outside the White House on July 15, 2025, in Washington, as Press Secretary Karoline Leavitt watches in reverence.. Manuel Balce Ceneta/AP This story was originally published by WIRED and is reproduced here as part of the Climate Desk collaboration. AI is "not my thing," President Donald Trump admitted during a speech in Pittsburgh on Tuesday. However, the president said during his remarks at the Energy and Innovation Summit, his advisers had told him just how important energy was to the future of AI. "You need double the electric of what we have right now, and maybe even more than that," Trump said, recalling a conversation with "David"--most likely White House AI czar David Sacks, a panelist at the summit. "I said, what, are you kidding? That's double the electric that we have. Take everything we have and double it."

Artificial Intelligence is only partially visible, just like an iceberg. To understand it fully, we must look beneath the surface. The positive side is that technology is making machines smarter. However, the deeper view explained in this book shows that AI is also making a growing number of people cognitively and psychologically dependent on digital networks. Whether you are a social media fanatic, a diehard AI aficionado, or a paranoid sceptic, it is impossible to escape the ubiquitous impact of AI.

The Industrial Revolution enabled Britain to surpass its European rivals and become the world's largest empire, a position it held for almost two centuries. There are some parallels between Britain's rise to world domination and China's ambitions to achieve the same. Artificial Intelligence is to China's twenty-first-century rise to power what the Industrial Revolution was to Britain's ascendance in the late 1700s. However, unlike Britain, China's recent rise to power is not its first experience of historical greatness. China has a long history as a preeminent nation with an advanced, regionally dominant civilization.

Machine learning systems now play a much bigger role in many of our social institutions, from education to healthcare to criminal justice. But many scholars have shown the way these systems are built on data that result in the reproduction of structural bias and discrimination. In this talk, Professor Crawford opens the substrates of training data to uncover the historical origins, labor practices, infrastructures, and epistemological assumptions that go into the production of artificial intelligence. Rather than a focus on technically correcting biases, she argues for a recentering of justice and the enforcement of limits on centralized power. Kate Crawford, Co-Founder of the AI Now Institute, is a Distinguished Research Professor at NYU and a Principal Researcher at Microsoft Research, and she is a leading scholar of the social implications of data systems, machine learning, and artificial intelligence.