Over the following year, Donald Trump’s spectacular rise and win in the 2016 US presidential election would shock the left-leaning workforce of the tech industry into self-reflection. As upheaval ripped through companies like Meta and Google and techlash sentiment gripped the public, AI researchers, too, began to question whether the field had moved too quickly to yoke its technologies to corporate bottom lines. An accounting of the societal impacts of commercializing AI research returned an unsettling scorecard: Automated software being sold to the police, mortgage brokers, and credit lenders were entrenching racial, gender, and class discrimination. Algorithms running Facebook’s News Feed and YouTube’s recommendation systems had likely polarized the public, fueled misinformation and extremism, enabled election interference, and, most horrifying in the case of Facebook, precipitated ethnic cleansing in Myanmar.

As ChatGPT swept the world by storm in early 2023, a Chinese AI researcher would share with me a clear-eyed analysis that unraveled OpenAI’s inevitability argument. What OpenAI did never could have happened anywhere but Silicon Valley, he said. In China, which rivals the US in AI talent, no team of researchers and engineers, no matter how impressive, would get $1 billion, let alone ten times more, to develop a massively expensive technology without an articulated vision of exactly what it would look like and what it would be good for.

Through the course of my reporting, I would come to conclude something even more startling. Not even in Silicon Valley did other companies and investors move until after ChatGPT to funnel unqualified sums into scaling. That included Google and DeepMind, OpenAI’s original rival. It was specifically OpenAI, with its billionaire origins, unique ideological bent, and Altman’s singular drive, network, and fundraising talent, that created a ripe combination for its particular vision to emerge and take over. “I get the sense that Sam is the most ambitious person on the planet,” a former employee says. In other words, everything OpenAI did was the opposite of inevitable; the explosive global costs of its massive deep learning models, and the perilous race it sparked across the industry to scale such models to planetary limits, could only have ever arisen from the one place it actually did.

Within weeks of the white paper, OpenAI and Microsoft formed a strategic alliance with Google and Anthropic to launch the Frontier Model Forum, a group for advancing relevant research and influencing the policy agenda on AI safety risks. It was a rare issue in which the interests of Doomers, Boomers, and profit-motivated corporates aligned: Keeping frontier models front and center in government regulatory discussions was ideologically imperative to Doomers and convenient to Boomers and corporates for shifting attention away from regulating existing AI models and their problems. Everyone also advanced their causes by arguing against opening up the weights of cutting-edge models. In the Forum’s first year, Meta was conspicuously absent. (It would join a year later to gain a seat at the table.)

Redistributing knowledge will also need policies that require companies to relinquish key details about the training data and technical specifications of their models and supercomputers. Only then could independent corporate model evaluators do their work. UC Berkeley researcher Deborah Raji, who has continued to engage with global policymakers after the Schumer forums, says this is also a bare minimum for guaranteeing the real-world safety of corporate systems. That is, not the theoretical rogue AI harms of Doomerism, but the existing real-world harms, from discrimination to misinformation to job automation, that consumers and communities can already face if widely deployed models aren’t properly tested. “We have the CFPB that monitors consumer finance products. We have the FDA that monitors medical devices. But for some reason when it comes to AI products, there’s just no oversight,” Raji says. AI models should in fact require more transparency than the average product. “These are data-defined systems. They’re not deterministic. So we need to know more about these systems to understand what they’re doing.” Such transparency is additionally crucial for measuring the impact of AI on the environment. In this regard other products once again already submit to evaluations that AI products do not. “If you’re using a car, if you are buying an appliance, you have an Energy Star rating,” says Sasha Luccioni at Hugging Face. “But AI is so integrated into our society, so widely used in products, and we don’t have any information about the sustainability of these systems.”