This article by Ramani and Wang, entitled “Why transformative AI is really, really hard to achieve,” is probably the best critical economic analysis of the current AI debates I’ve come across. It assesses what would be required for AI technologies to live up to the current hype cycles about how these technologies will massively benefit economic productivity. Based on the nature of AI technologies being developed, combined with the history of economic productivity enhancements over time, the authors conclude that the present day hype is unlikely to be met.
Key to the arguments is that AI technologies do not, as of yet, sufficiently automate a vast set of tasks which are comparatively easy for humans to accomplish, nor are they able to benefit from the latent knowledge and intelligence that guides humans in their daily lives. The authors argue that AI technologies must broadly automate tasks, instead of discretely automating them, in order to achieve cross-industry improvements to productivity. Doing otherwise will merely accelerate aspects of processes which will remain gridlocked in the aggregate by more traditional or less automated processes.
The authors are not dismissing the potential utility of AI technologies, however, but instead just arguing that they are not as likely to achieve the transformative economic miracles that many are suggesting are just around the corner. However, even if AI systems are ‘only’ as significant for productivity as the combustion engine (which discretely as opposed to comprehensively enhanced productivity) this would be a significant accomplishment.
Excited Pixels 