Should the AI Race Be About Bigger Models, or the Search for Meaning?

Top executives at American artificial intelligence giants OpenAI, Microsoft, and Advanced Micro Devices said at a US Senate hearing recently that while the US is ahead in the race, Washington needs to boost infrastructure and champion AI chip exports to stay ahead of Beijing.

The fight for AI supremacy relies on the scaling hypothesis that says that by making current models larger, training them on ever-increasing amounts of data, and utilizing greater compute— the hardware resources required — we are sure to achieve Artificial General Intelligence (AGI)

If the scaling hypothesis proves to be true, it will indeed be an exciting development for humanity, as the upside is tremendous.

But what if the scaling hypothesis turns out to be false? Would that mean we have lost the AI race? What would be our Plan B?

There is much to be gained by reframing the AI race to be not about better hardware, more data, and bigger models, but about computational understanding of meaning.

Simply put, the ultimate race for AI is not between nations, but between humanity and nature: how do we humans acquire meaning from the world around us, and how might we program something similar into an AI system? This reframing naturally leads us to a Plan B if the scaling hypothesis turns out to be false.