2 days ago
Karen Hao's Empire of AI brings nuance and much-needed scepticism to the study of AI
Most conversations that we have around Artificial Intelligence (AI) today share one commonality: the technology's society-altering capacity, its ability to leap us towards the next breakthrough, a better world, a future that we rarely imagined would be possible.
The founding mission of Open AI, the company that made AI a household name through ChatGPT in 2022, is 'to ensure that artificial general intelligence — AI systems that are generally smarter than humans — benefits all of humanity'.
Behind this seemingly optimistic idea, tech reporter Karen Hao argues, is the stench of empires of old — a civilising mission that promises modernity and progress while accumulating power and money through the exploitation of labour and resources.
Hao has spent seven years covering AI — at the MIT Tech Review, The Washington Post and The Atlantic. She was the first to profile OpenAI and extensively document the AI supply chain — taking the conversation beyond the promise of Silicon Valley's innovation through reportage around people behind the black boxes that are AI models. And it is these stories that find centre-stage in 'Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI', her debut book.
It is a company book and, like all good business books, gives an intimate picture of the rise of an idea, the people, strategy and money behind it. But the book stands out as it provides us one way of framing the dizzying AI boom and conversation around us.
In doing so, the book joins the list of non-fiction on AI that brings nuance and much-needed scepticism of the subject while being acutely aware of its potential. In 2024, Arvind Narayanan and Sayash Kapoor from the Computer Science department of Princeton University wrote 'AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference'. The book lays out the basics of AI research, helping distinguish hype from reality. The same year, tech journalist Parmy Olson wrote Supremacy: AI, ChatGPT, and the Race that Will Change the World about the unprecedented monopoly that Open AI and Google's AI research wing Deepmind currently have in the world.
This approach needs a lot of computing capacity. The physical manifestation of it are the massive data centres that are mushrooming everywhere. These data centres, in turn, consume a lot of energy.
Open AI cracked this technique and doubled down on it: more data, more high-functioning and expensive Graphic Processing Units (GPUs) that make the computation happen, and more data centers to house them.
This more-is-more approach, Hao writes, has 'choked' alternative forms to AI research, which has been a subject many have been trying to crack and expand since the 1950s. 'There was research before that explored minimising data for training models while achieving similar gains. Then Large Language Models and ChatGPT entered the picture. Research suddenly stopped. Two things happened: money flowed into transformers (a type of highly-effective neural network) and generative AI, diverting funding from other explorations,' Hao says.
With the 'enormous externalities' of environmental costs, data privacy issues and labour exploitation of AI today, it is important to 'redirect some funds to explore new scientific frontiers that offer the same benefits of advanced AI without extraordinary costs,' Hao argues.
But it might be harder than said. In her book, Hao traces how researchers, who were working outside major AI companies, are now financially affiliated with them. Funding, too, primarily, comes from tech companies or academic labs associated with them.
'There's a misconception among the public and policymakers that AI research remains guided by a pure scientific drive,' Hao says, adding that 'the foundations of AI knowledge have been overtaken by profit motives.'
