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AI has generated plenty of hype, but what about its economic performance?
By Gary Marcus  |  Oct 05, 2023
AI has generated plenty of hype, but what about its economic performance?
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In trying to predict which are likeliest to endure, economic and financial indicators are the tell-tale signs, says best-selling AI author, entrepreneur and NYU Prof Gary Marcus.


This article has been adapted from the original version, which can be found on Gary Marcus' Substack.

Is the fever for generative artificial intelligence (GenAI) - the currently most popular form of artificial intelligence (AI) - on track to be the tulip mania of the 2020s? As I recently argued, the whole thing could turn out to be nothing more than a fad, but it is currently still way too early to tell.

AI has undeniably hit it big. Google Search - driven by AI - has made more money than almost any product in history. Google Search has been AI-powered since the outset - when it included zero percent generative AI (GenAI) - and continues to make a ton of money now as bits of GenAI are presumably blended in. GenAI presumably helps the quality of results, though whether this has had a material impact on the bottom line is unclear.

Meta, too, has made an immense amount selling ads, and AI - though not necessarily cutting-edge AI - has always partly allowed it to place those ads with such precision. As in the case of Alphabet and Google Search, to see from the outside whether GenAI has had a material effect on Meta’s profits is hard.

A few years ago, a joke in Silicon Valley - anchored to some degree in reality - held that if one had .ai in the domain name of one’s startup, it meant that one could automatically add a zero to one’s valuation - e.g., USD100 million instead of USD10 million.

Nowadays, it feels like this could be two zeros, especially if one claims to

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There are ways to make a huge ROI with AI. Not with generative AI but with prescriptive AI in general and the hybridization of ML and OR in particular. We are able to optimize complex industrial processes by 20 to 40%. More importantly, you can construct robust solutions that can deal with lots of uncertainties in our ever more rapidly changing world. You also get control, explainability and sensitivity analysis as a byproduct if you want to.