In recent months, a handful of Chinese companies, notably DeepSeek, have unveiled advanced generative artificial intelligence (AI) models that boast significantly lower costs than current offeringsThis development could accelerate the dissemination of AI technology while enhancing its influence on global economic growthExperts suggest that lowering the cost of accessing advanced AI technologies may challenge widely held beliefs regarding the high barriers to entry that have thus far limited the scalability of the most sophisticated AI models.
Joseph Briggs, co-head of Goldman Sachs’ global economics team, addressed these developments in a recent reportHe noted that while it remains unclear how Chinese researchers have achieved these breakthroughs and what the overall cost structures entail, reduced expenses could indeed facilitate a more rapid global adaptation and proliferation of AI capabilitiesAccording to Briggs, “If lower costs encourage greater competition in platform and application development, this breakthrough might enhance upward economic potential in the medium term.”
Despite the promising implications of these advancements, Briggs cautioned that in the short term, the direct impact on adoption rates might be limited, since cost is not currently the primary barrier to AI uptakeAccording to data from the U.SCensus Bureau, the major short-term impediments reported by companies include insufficient understanding of AI capabilities and concerns over privacyFor instance, as of now, only six percent of American businesses report using AI for routine operations, a slight increase from four percent at the end of 2023.
The promise of generative AI is not just theoreticalGoldman Sachs economists previously forecasted that widespread adoption of generative AI might enhance U.S. labor productivity by 15% over the next decade, primarily through the automation of tasksThis could potentially unleash about $4.5 trillion in annual GDP (in today’s dollars). In the early stages, the financial benefits are expected to accrue to hardware and infrastructure providers, later extending to platform and application developers, ultimately manifesting in increased productivity and efficiency across diverse industries.
The investment cycle around AI in the U.S. is anticipated to diminish once it reaches two percent of the GDP, as costs associated with training AI models and running AI queries decline
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As user adoption rates rise, investments in AI software are expected to experience steady growth.
While the advancements made by China in AI have stirred discussions questioning the standing of existing players in terms of investments and technological leadership, Goldman Sachs maintains its outlook on the macroeconomic impacts of AIThe largest economic impetus is expected to stem from businesses incorporating AI-driven automation into their operations, leading to productivity gainsBriggs emphasized the potential for credible competition to emerge, challenging U.SAI leaders, which could boost global adoption rates and enhance overall productivity.
He remarked, “The emergence of a robust non-U.S. competitor could spur some governments to increase the importance of developing domestic AI capabilities.” The intensification of global competition might encourage cross-border collaborations or the reduction of regulatory hurdles to foster the advancement and adoption of AI technologies.
Moreover, Briggs pointed out that the potential for automation and productivity enhancements produced by generative AI is similar across various global economiesHe speculated, “While we still expect that, given the U.S.'s leading role in AI model development, adoption might occur at a faster rate there than in other countries, the emergence of non-U.S. platforms and applications could accelerate adoption timelines in other regions.”
In terms of how AI could elevate productivity, the Goldman Sachs team anticipates that the impacts of generative AI technology will become apparent in productivity data by 2027, with peak effects expected in the early 2030sOther developed markets and key emerging markets are projected to lag behind the U.S. by several years in this regardHowever, recent disclosures from DeepSeek suggest that adoption could happen sooner than previously expected.
The research from Goldman Sachs remains bullish on mid-term AI adoption increases, indicating that the types of tasks generative AI can automate could save each employee thousands of dollars annually
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Briggs argued, “Given the explosive potential for cost savings associated with generative AI, and the probability of very low marginal deployment costs once application development is complete, we view the issue of adopting generative AI as more of a ‘when’ rather than an ‘if.’”
Despite the optimistic outlook, significant questions exist regarding the influence of these low-cost AI models on stakeholders within the AI ecosystemThe distribution of profits will hinge on market concentration, intellectual property concerns, scalability, and the ultimate competitive landscapeIt is premature to assess the impact of these new models; however, if expensive hardware and computational capabilities become less crucial to realizing economic benefits, companies involved in building physical infrastructure may see diminished returns.
Nevertheless, Briggs clarified that the distribution of gains is less relevant to the holistic macroeconomic narrativeThe overall impact stemming from China's breakthroughs is likely to be a net positive for the global economy.
A pressing concern remains: will the emergence of more efficient AI models lead to a reduction in AI capital expenditures? Stock analysts generally forecast a substantial rise in AI-related capital outlays, estimating that by Q4 2025, these investments will reach $325 billionYet, this raises the question of whether a decrease in capital spending could slow GDP growth.
Goldman Sachs’ research cautions that if cheaper AI models precipitate reduced capital expenditures, two scenarios could potentially restrain the economic impact in this contextAlthough companies have reported increases in AI-related investments, official GDP data reflects minimal effects thus farAnalysts believe it unlikely that companies will significantly reallocate their capital strategies based solely on the latest developments coming out of China.
At the same time, while low-cost AI models may result in less investment in AI infrastructure than predicted, it is also plausible that advancements will encourage existing AI firms to bolster their investments to maintain competitive advantages
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