According to the Zhitong Finance APP, Goldman Sachs published a research report stating that recently, a few Chinese companies, such as DeepSeek, have developed advanced generative artificial intelligence (AI) models at a lower cost than existing products. This development could accelerate the adoption of AI and enhance the technology's contribution to global economic growth.
This breakthrough challenges the notion that high investment costs are the biggest barrier to entry for the largest and most powerful AI models. Joseph Briggs, co-head of Goldman Sachs' global economics team, wrote in his team's report that while it is not yet fully determined how Chinese researchers are developing their AI technology and its total costs, a lower cost structure could help AI develop and spread more rapidly worldwide.
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Briggs wrote, &#34;If lower costs help increase competition in platform and application development, this breakthrough could enhance the macroeconomic upside in the medium term. Limited adoption remains the main bottleneck to unlocking AI-related productivity gains, and competition will accelerate the construction of AI platforms and applications, thereby promoting adoption.&#34;
&#34;Nevertheless, the impact on adoption in the short term may be limited, as cost itself is currently not the main barrier to adoption,&#34; he added.
According to data from the U.S. Census Bureau, the largest short-term barrier to adoption reported by companies so far is a lack of understanding of AI capabilities and privacy concerns. Only 6% of U.S. companies report using AI for routine production, up from 4% at the end of 2023.
How will AI boost GDP?
Previously, Goldman Sachs' economic team had a baseline expectation that widespread adoption of generative AI could increase U.S. labor productivity by 15% over approximately 10 years, primarily through the automation of work tasks. This would release about $4.5 trillion in U.S. annual GDP (in current dollars). The economic benefits are expected to initially benefit hardware and infrastructure providers, then expand to platform and application developers, and ultimately manifest as productivity and efficiency improvements across a broader range of industries.
The team also predicts that the AI investment cycle in the U.S. will gradually weaken after reaching 2% of GDP, as the computational costs of training AI models and running AI queries decline. As end-user adoption rates increase, investments in AI software are expected to grow steadily.
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Although China's progress in the AI field has raised questions about the investment and technological leadership of a few existing companies, the team maintains its view on the macroeconomic impact of AI: the main macroeconomic driver is expected to come from the productivity gains as companies incorporate AI-driven automation into their businesses.
Briggs wrote, &#34;The emergence of a credible competitor challenging U.S. AI leaders could drive global adoption rates and productivity improvements.&#34; The rise of a strong non-U.S. competitor may prompt some governments to emphasize the importance of developing domestic AI capabilities
The intensification of global competition may promote cross-border cooperation or reduce regulatory barriers to encourage the development and adoption of AI.
At the same time, Briggs wrote that the potential automation and productivity gains brought by generative AI are roughly similar across global economies. &#34;While we still expect that, given the U.S.'s leading position in AI model development, the U.S. will adopt AI faster than other countries, the emergence of non-U.S. platforms and applications may accelerate the adoption timeline in other regions.&#34;
How will AI enhance productivity?
The team's forecast assumes that the adoption of generative AI technology in the U.S. will begin to reflect in productivity data by 2027, with peak impact expected in the early 2030s. In these forecasts, other developed markets and key emerging market countries lag behind the U.S. by several years. Briggs wrote, &#34;Recent reports from DeepSeek suggest that adoption may occur earlier.&#34;
Goldman Sachs Research still expects AI adoption to rise in the medium term, with Briggs noting that the types of work tasks that generative AI can automate could save each employee thousands of dollars annually. He wrote, &#34;Given the potential cost savings from generative AI are substantial, and once application development is complete, the marginal cost of deployment may be very low, we believe that the adoption of generative AI is more a question of 'when' rather than 'if.'&#34;
Briggs wrote that there are reasonable questions about how low-cost AI models will affect stakeholders in the AI ecosystem. The distribution of any profits will depend on market concentration, intellectual property, scalability, and the ultimate competitive landscape. While it is still too early to understand the impact of new models, if expensive hardware and computing power become less critical for achieving economic benefits, companies building physical infrastructure may see less overall gain.
However, Briggs pointed out that questions about growth distribution are less relevant to the overall macroeconomic story. The outlook does not depend on who specifically benefits; the overall impact of breakthroughs in China is likely to be net positive.
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Will China's AI development reduce investment?
An important question is whether more efficient AI models will lead to a reduction in AI capital expenditures—stock analysts predict based on general estimates that AI-related capital expenditures will rise to $325 billion by the fourth quarter of 2025—and whether this will lead to a slowdown in GDP growth.
Goldman Sachs Research points out that if cheaper models lead to a reduction in AI capital expenditures, there are two scenarios that may limit the economic impact in this scenario. Although companies report that they are increasing their AI-related investments, the impact on official GDP data has been limited so far. Goldman Sachs Research's stock analysts believe that companies are unlikely to significantly adjust their capital allocation solely because of the latest news from China
At the same time, although low-cost AI models may lead to less-than-expected construction of AI infrastructure, it is also possible that these advancements will encourage existing AI companies to increase investment to maintain their leading position. Fundamentally, if these new developments stimulate competition and reduce costs, they could catalyze faster construction of AI platforms and applications

DPSK

Goldman Sachs research report points out that low-cost generative AI models developed by Chinese companies like DeepSeek may accelerate the popularization of AI and drive global economic growth. Although short-term adoption is limited by understanding of AI capabilities and privacy issues, in the long run, the widespread application of AI is expected to increase U.S. labor productivity by 15% within 10 years, releasing approximately $4.5 trillion in GDP

- Goldman Sachs reports that Chinese companies like DeepSeek have developed advanced generative AI models at lower costs, potentially accelerating AI adoption and enhancing its contribution to global economic growth.  
- The report suggests that while the exact development costs are unclear, reduced costs could foster competition in AI platform and application development, ultimately boosting macroeconomic prospects.  
- However, short-term adoption may remain limited due to existing barriers such as insufficient understanding of AI capabilities and privacy concerns among companies.

Zhitong

DeepSeek

Goldman Sachs

Goldman Sachs: DeepSeek's technological breakthrough is expected to accelerate the popularization of AI, potentially leading to a significant boost in global economic growth