How the global South may pay the cost of AI development

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FIG Fellows: Francesco Tasin

Authors: Julian Jacobs, Francesco Tasin

Abstract:

The environmental costs of artificial intelligence are commonly regarded as a key risk that may emerge from the widespread diffusion and general advancement of the technology. There has been an exponential increase in capabilities and resources allocated to frontier AI models in recent years – a trend that is likely to continue in the coming decades.

Of the key inputs in AI development, compute and data storage are the most resource-intensive. Evidence has suggested this poses material risks related to the unequal global distribution of the harms and benefits of AI. The global South – particularly developing countries – may miss out on many economic benefits due to significant capital costs, while simultaneously enduring the hazards of AI growth in the form of possible environmental degradation and labour disruption.

As a general rule, more compute entails better predictions and more robust machine learning systems. While algorithmic efficiency is clearly another crucial component of AI development, simply increasing compute has proven a remarkably reliable strategy for the upscaling of AI models, allowing them to leverage larger training datasets, and helping them become ‘smarter’.