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Goldman Sachs report questions generative AI's productivity benefits, power demands, and industry hype. Economist Daron Acemoglu doubts AI's transformative potential, highlighting limitations in real-world applications and escalating training costs.
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In a recent report by Goldman Sachs titled "Gen AI: Too Much Spend, Too Little Benefit?", concerns about generative AI's productivity benefits, returns, and power demands were highlighted. The report questions the industry's hype around AI's transformative potential, emphasizing the limited impact on productivity growth and the challenges in improving complex tasks. Economist Daron Acemoglu of MIT expressed skepticism about generative AI's ability to revolutionize industries, citing the lack of real-world interaction and intelligence in AI processes. The report also discussed the escalating costs of training AI models and the potential limitations in achieving superintelligence. Criticisms were raised regarding the overestimation of AI's capabilities and the disconnect between AI developers and the practical implementation of AI systems, as seen in fast food ordering systems requiring human intervention despite AI integration. The report underscores the need for a realistic assessment of AI's capabilities and the importance of preserving human expertise in creative fields that AI struggles to replicate effectively.
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1 commentsBasically, it works fairly OK 90% of the time, but the cost is ~$1 per thing we need it to do. That's not a sustainable price when someone might be hitting this a lot. If costs don't rapidly come down then it's not worth introducing it to customers (currently only being used for internal tests).
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