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Toi Staff
Jeffrey Bernstein Ph.d
Tarik Cyril Amar
Gideon Levy
Belen Fernandez
Penny S. Tee
Muhannad Ayyash
John Nosta
Andrew Silow-Carroll
Sharona Margolin Halickman
A Potential Path to Safer AI Development
Imagine you’re in a car with your loved ones, following an unfamiliar road up a spectacular mountain range. The problem? The way ahead is shrouded in fog, newly built, and lacking both signposts and guardrails. The farther you go, the more it’s clear you might be the first ones to ever drive this route. To either side, you catch glimpses of precipitous slopes. Given the thickness of the fog, taking a curve too fast could send you tumbling in a ditch, or—worst case scenario—cause you to plunge down a cliffside. The current trajectory of AI development feels much the same—an exhilarating but unnerving journey into an unknown where we could easily lose control.
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Since the 1980s, I’ve been actively imagining what this technology has in store for humanity’s future and contributed many of the advances which form the basis of the state-of-the-art AI applications we use today. I’ve always seen AI as a tool for helping us find solutions to our most pressing problems, including climate change, chronic diseases, and pandemics. Until recently, I believed the road to where machines would be as smart as humans, what we refer to as Artificial General Intelligence (AGI), would be slow-rising and long, and take us decades to navigate.
My perspective completely changed in January 2023, shortly after OpenAI released ChatGPT to the public. It wasn’t the capabilities of this particular AI that worried me, but rather how far private labs had already progressed toward AGI and beyond.
Since then, even more progress has been made, as private companies race to significantly increase their models’ capacity to take autonomous action. Now, it is a common stated goal among leading AI developers to build AI agents able to surpass and replace humans. In late 2024, OpenAI’s o3 model indicated significantly stronger performance than any previous model on a number of the field’s most challenging tests of programming, abstract reasoning, and scientific reasoning. In some of these tests, o3 outperforms many human experts.
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