I’ve been thinking about the increasing evidence that generative AI, specifically large language models (LLMs), could significantly boost labor productivity and drive up GDP.
Many countries have been in a productivity funk for years. Even emerging economies have experienced a slide in productivity growth since the Global Financial Crisis.
This development is likely to motivate governments to act. For governments, if AI (or any technology or policy) offers a way out of the slump, they will want to take it.1
They can’t look a gift horse in the mouth.
When I spoke with economists, Eric Brynjolfsson and Lindsey Raymond, a few days ago, we discussed their new paper demonstrating that labor productivity improved by about 14% in certain jobs.
A Goldman Sachs paper suggested that generative AI could improve productivity in the US by 1.5 to 2.9% over a 10-year period. Globally, this could translate to a 7% growth in GDP. In the context of the UK and US economies, this means an additional $210 billion and $1.6 trillion, respectively.
These staggering numbers should prompt governments to ensure their economies are well-positioned to capitalize on these benefits. Public-private partnerships will likely play a crucial role in this process, with the market driving investment and governments setting frameworks to further optimise the results.
The UK government, for instance, has allocated £900 million to build a supercomputer and another £100 million to train a foundation model. If we scale these numbers by GDP for the US, that would amount to about $9.9 billion. However, the US allocation for a national AI research task force supercomputer is only $2.6 billion over five years, indicating that the UK is making a more substantial commitment on a per GDP basis.
But is this enough, and is it the right approach?
