AI is transforming the economy - understanding its impact requires both data and imagination

"Others foresee a revolution that might add between US$17 trillion and $26 trillion to annual global economic output and automate up to half of today's jobs by 2045. But even before the full impacts materialize, beliefs about our AI future affect the economy today - steering young people's career choices, guiding government policy and driving vast investment flows into semiconductors and other components of data centres."

"Given the high stakes, many researchers and policymakers are increasingly attempting to precisely quantify the causal impact of AI through natural experiments and randomized controlled trials. In such studies, one group gains access to an AI tool while another continues under normal conditions; other factors are held fixed. Researchers can then analyse outcomes such as productivity, satisfaction and learning. Yet, when applied to AI, this type of evidence faces two challenges."

"First, by the time they are published, causal estimates of AI's effects can be outdated. For instance, one study found that call-centre workers handled queries 15% faster when using 2020 AI tools. Another showed that software developers with access to coding assistants in 2022-23 completed 26% more tasks than did those without such tools. But AI capabilities are advancing at an astounding pace."

AI's projected economic impact ranges from a modest 0.9% GDP boost over ten years to a transformation adding US$17–26 trillion annually and automating up to half of jobs by 2045. Expectations about AI already influence career choices, government policy and massive investments in semiconductors and data-centre infrastructure. Researchers use natural experiments and randomized controlled trials to quantify AI's causal effects by comparing groups with and without AI tools and measuring productivity, satisfaction and learning. Two key challenges arise: published causal estimates can be quickly outdated as AI rapidly improves, and controlled studies often fail to capture wider organizational and market ripple effects.

Read at Nature