Experts say artificial general intelligence (AGI) will take around forty years to develop and won’t fully automate us for almost a century. At the same time, advancements like ChatGPT and DALLE-2 seem to send a different message. Silicon Valley and leading AI initiatives are anticipating this technology to spark radical change over the next decade – enough to redefine society from the ground up.

So, why do AI researchers continue to predict AGI into the latter half of the century? And should we even take them seriously?

1. Experts don’t want to admit that AGI could be a decade away because it would make them irrelevant.

AI experts' work is spread across the fields of machine learning and computational neuroscience. There are a plethora of research directions being explored. These include reinforcement learning, convolutional neural networks, training image classifiers, neurosymbolic modules, hybrid systems wanting to directly imprint inspirations from the brain… This is still just barely scratching the AI approach surface.

Yet, today's premier AI systems make little use of other AI researchers' work. The most powerful models in 2023 are still based on Google’s “Transformer” architecture first showcased in 2017. And with this current paradigm, progress mainly stems from solving the engineering challenges of transformers. These include but are not limited to: hardware scaling, data curation, algorithmic tweaks, careful fine-tuning, and adding more compute. Advancements are, by and large, anything that serves to scale these models up bigger – personifying the mantra of "Scale is all you need."

In contrast, academia values publishing research that is “novel” and “innovative”. Researchers must craft a unique AI portfolio to stand out from the crowd and capture shares from the S&P H-INDEX – hopefully bolstering their odds to secure future grants. Academics must be constantly innovating or to die trying.

Transformers though just want more server racks, more engineers, and more Reddit memes to train on. This doesn’t leave much room for the intellectual freedom and innovation academia demands or for the other AI approaches to substantially contribute.

Instead, we’re now in the middle of an industry speedrun to see if this actually scales all the way to AGI.

If scaling systems like transformers beats every other approach and single-handedly leads to AGI, most resear...

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