Copy of GenAI/LLM and productivity
I will present 3 papers which discuss this from economics point of view.
The productivity J-Curve
"THE PRODUCTIVITY J-CURVE: HOW INTANGIBLES COMPLEMENT GENERAL PURPOSE TECHNOLOGIES" (GPT - General Purpose Technologies).
The model they present generates a Productivity J-Curve that can elucidate the productivity slowdowns often accompanying the advent of GPTs, as well as the subsequent increase in productivity. They utilize this model to empirically analyze the historical roles of intangibles associated with R&D, software, and computer hardware. Notably, they observe significant and ongoing Productivity J-Curve effects, particularly for software and to a lesser extent for computer hardware. Their adjusted measure of Total Factor Productivity (TFP) is 11.3% higher than official measures at the end of 2004 and 15.9% higher at the end of 2017. Finally, they assess how AI-related intangible capital may currently be impacting measured productivity and find that the effects are small but growing for you.
Basically, this means that there will a lot of -ve productivity due to new technology. For GenAI there can be multiple reasons
The graph shows the in 1-2 years, GenAI productivity will keep improving and then we will actually see the gain. Related to this, is a slightly older paper about ideas.
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Are ideas hard to find?
In their paper "Are Ideas Getting Harder to Find?" Stanford researchers gave a simple metric for measurement of productivity.
Their hypothesis suggests that the growth of total factor productivity (TFP) in the economy, as a proxy for research output, is directly related to the quantity of researchers. As the number of researchers significantly increased in the post-war era while TFP growth remained relatively constant, the focus shifts to understanding the behavior of the number of researchers. This term represents the (potentially varying over time) level of research productivity. The authors contend that for equilibrium, α must have decreased, suggesting that the discovery of research ideas has become more challenging over time.
Ideas can be found if there is research investment
"Are Ideas Really Getting Harder To Find? R&D Capital and the Idea Production Function" is an update on this above paper taking research investment into account.
Their findings suggest that the challenge lies not just in the increasing difficulty of finding ideas, but rather in the necessity for more sophisticated laboratory equipment to discover or implement them. This scarcity can only be addressed by enhancing the accumulation and development of research and development (R&D) capital, not solely by expanding the R&D workforce. Additionally, as investments in R&D equipment are a variable influenced by policy, their results indicate a diminished argument for the likelihood of future secular stagnation.
For GenAI this is more true, since compute requirements are very high and expensive. Not many organization have them and so innovation is only in certain companies.