The arithmetic of hope

The world is counting on AI for abundance. Generative AI is supposed to increase the productivity levels to a point where we don’t have to worry about employment, inflation or wages, because the algos will take care of it. But there are a few important nuances to consider in this scenario: (1) the energy needed to power and train AI is massive and we don’t have the installed capacity to do it, and more importantly, an adequate grid to transport it and store it. (2) like every new technology in history, it takes time and a few misses to get it right. In the chart below, you can see the cliff between what’s being researched in AI (where the dollars go) and the actual implementation of those solutions with some practical application (5%). These numbers will improve with time, but at this point, the solution is not living up to the hype, and the ROI on this projects is very low. Which leads us to issue number (3): timing. The debt, inflation, deficits and productivity problems are very impatient ones. It’s not clear we will have a solution against current economic excesses in time for the downturn. And in the meantime, valuations of the companies working on these solutions are getting really frothy. To put it in simple words, hope is getting very expensive and will arrive late. Can we stretch the current situation until we get the solution we need?

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