While the Chinese developments in AI raise questions about investment and technology leadership by a handful of incumbents, the team’s perspective on the macroeconomic impact of AI is unchanged: The main macroeconomic boost is expected to come from increased productivity as companies incorporate AI-driven automation into their businesses.
“The emergence of a credible competitor to US-based AI leaders could provide an uplift to global adoption and productivity,” Briggs writes. The emergence of a solid non-US competitor could prompt some governments to raise the importance of developing domestic AI capabilities. Increased global competition may spur cross-border cooperation or lower regulatory barriers to encourage AI development and adoption.
At the same time, the potential automation and productivity gains from generative AI are generally similar for economies across the globe, Briggs writes. “While we still expect that the US will adopt AI more quickly than other countries, given its leadership in AI model development, the emergence of non-US based platforms and applications could accelerate the adoption timeline elsewhere.”
How AI will boost productivity
The team’s forecasts assume that US adoption of generative AI technology will start to show up in productivity figures in 2027, with peak impact expected in the early 2030s. Other developed markets and key emerging market countries lag behind the US timeline by a few years in these projections. “The recent DeepSeek reports suggest adoption could happen sooner,” Briggs writes.
Goldman Sachs Research still expects AI adoption will climb in the medium-term, and Briggs points out that the types of work tasks automatable by generative AI would result in several thousands of dollars of cost savings per worker per year. “Given that potential cost savings from generative AI are large and the marginal cost of deployment once applications are developed will likely be very small, we see adoption of generative AI as more of question of ‘when’ rather than ‘if,’” he writes.
There are valid questions about how lower-cost AI models could impact stakeholders in the AI ecosystem, Briggs writes. How any profits are distributed will depend on things like market concentration, intellectual property rights, scalability, and ultimately the competitive landscape. While it’s still too early to know the new models’ effect, if expensive hardware and computing power are going to be less essential to realizing the economic benefits, companies that build the physical infrastructure may garner less of the overall gains.
Briggs points out that questions about the distribution of growth are less relevant, however, to the overall macroeconomic story. The outlook doesn’t depend on who specifically benefits, and the overall implications of the breakthrough in China are most likely net positive.
Source: goldmansachs.com
