{"id":35554,"date":"2026-10-16T09:41:34","date_gmt":"2026-10-16T13:41:34","guid":{"rendered":"https:\/\/www.rbcwealthmanagement.com\/en-ca\/?p=35554"},"modified":"2026-10-16T09:41:36","modified_gmt":"2026-10-16T13:41:36","slug":"the-china-us-ai-race-who-is-leading","status":"publish","type":"post","link":"https:\/\/www.rbcwealthmanagement.com\/en-ca\/insights\/the-china-us-ai-race-who-is-leading","title":{"rendered":"The China-U.S. AI race: Who is leading?"},"content":{"rendered":"\n

By Jasmine Duan<\/strong><\/p>\n\n\n\n

\n Investors may still remember the \u201cDeepSeek Moment\u201d in Jan. 2025, when a\n small Chinese AI startup launched an open-source reasoning model that\n matched the performance of leading U.S. AI models. Since then, China has\n rapidly narrowed the gap in AI development with the U.S., intensifying\n debate about who will lead this technology race.\n <\/p>\n \n

Chinese AI models reach tier-1 performance<\/h2>\n

\n Despite facing intensifying hardware constraints, Chinese AI models are\n demonstrating rapid improvements in coding and reasoning capabilities. A\n recent example is Chinese startup Moonshot\u2019s Kimi K3 model, whose overall\n performance nearly matches that of advanced U.S. models at a\n fraction of the cost.\n <\/p>\n

\n Chinese companies now account for the largest share of generative model\n downloads on Hugging Face, the primary hosting platform for AI models.\n Rather than isolated breakthroughs, Chinese developers are releasing\n high-performing models at a weekly cadence. New releases consistently rank\n among the platform\u2019s most downloaded each week, which is a sign of both\n quality and developer productivity, in our view.\n <\/p>\n \n

Not all Chinese AI models adopt a low-price strategy<\/h2>\n

\n Chinese models have gained popularity partly because they are open source,\n allowing developers to customize and train them on proprietary data, and\n partly because they are significantly less expensive than closed-source\n alternatives. This has led many investors to assume Chinese firms could\n only compete on price. However, we argue that this assumption is being\n tested.\n <\/p>\n

\n Chinese AI companies are now pursuing differentiated strategies.\n Moonshot\u2019s Kimi focuses on offering higher-intelligence models at premium\n prices. General language models from Qwen and Z.AI adopt two-tier\n approaches, pairing advanced, expensive models with efficient,\n cost-effective options. DeepSeek, meanwhile, sticks to a low-cost\n strategy.\n <\/p>\n

\n At the same time, U.S. providers are responding with their own price cuts,\n particularly for lower and mid-tier models. We view this as a healthy\n market strategy for AI companies: lower-cost models expand accessibility,\n while premium options serve enterprises\u2019 most complex needs.\n <\/p>\n \n

China\u2019s biggest constraint: semiconductors<\/h2>\n

\n As AI demand surges globally, Chinese companies face mounting supply\n constraints on semiconductors. While previously reliant on stockpiles,\n legacy chips and gray-market purchases to train models, these workarounds\n are no longer sufficient as compute needs have accelerated.\n <\/p>\n

\n We believe the divergence in investment capacity reveals the severity of\n the constraint. According to Bloomberg, the eight largest U.S.\n hyperscalers are projected to increase capital expenditure by 76 percent\n in 2026, reaching over US$780 billion. By contrast, China\u2019s three major\n internet companies and three telecom carriers combined plan only roughly\n $85 billion in data centre capex this year, which is roughly one-tenth\n of the U.S. total. Even this amount represents a stretch, as memory chip\n prices have quintupled over the past half-year, further eroding purchasing\n power.\n <\/p>\n \n

AI spending in China is a fraction of the U.S. total<\/h3>\n
\n
\n
\n \"Chinese\n
    \n
  • \n
    \n
    <\/div>\n <\/div>\n U.S.\n <\/li>\n
  • \n
    \n
    <\/div>\n <\/div>\n China\n <\/li>\n <\/ul>\n

    Note: 2026 figures represent estimates.<\/p>\n

    Source – RBC Wealth Management, Bloomberg<\/p>\n

    \n The line chart compares annual capital expenditures by U.S. AI\n hyperscalers and Chinese AI firms from 2023 through 2026 (estimated)\n in U.S. dollars. Chinese spending totaled $59 billion in 2023 and\n 2024, $67 billion in 2025, and is projected to reach $85 billion in\n 2026. U.S. hyperscalers spent $168 billion in 2023, $256 billion in\n 2024, $444 billion in 2025, and are projected to spend $781 billion\n in 2026.\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n

    \n More concerning for Chinese AI firms, in our view, is that U.S. export\n controls prevent China from accessing advanced manufacturing equipment,\n particularly extreme ultraviolet (EUV) lithography machines needed for\n cutting-edge chip production. This creates a potential structural\n disadvantage. While reports periodically surface of Chinese breakthroughs\n in developing EUV lithography, we doubt these will have meaningful\n near-term impact given their prototype status and the challenge of\n reaching mass production.\n <\/p>\n

    \n Chinese chipmakers are responding by stacking multiple chips in smaller\n spaces, improving interconnects and pursuing system-level optimization to\n narrow the performance gap. We believe these efforts may not fully offset\n supply constraints, but they represent the most viable path forward in the\n near term.\n <\/p>\n \n

    Where will AI competition go next?<\/h2>\n

    \n For many people, what matters most is how AI will affect their\n livelihoods, not which country will win or lead the AI competition. Public\n perception of AI diverges sharply between China and the United States.\n According to Stanford University\u2019s AI Index Report in 2025, 84 percent of\n Chinese respondents reported excitement about AI, compared to just 38\n percent of Americans. Meanwhile, American concerns centre on job\n displacement, while Chinese concerns are more varied.\n <\/p>\n

    \n We think this divergence partly reflects structural economic differences.\n The U.S. Bureau of Labor Statistics reports that more than 70 percent of\n American workers are in service sectors\u2014which are more vulnerable to AI\n disruption\u2014compared with approximately 50 percent in China, according to\n Chinese government data sources. AI\u2019s immediate workforce impact therefore\n seems smaller in China, explaining why Chinese people view it as a\n practical tool to increase productivity rather than an existential threat.\n Furthermore, a recent Chinese court ruling protecting a worker in an\n AI-related labour dispute signalled the country\u2019s regulatory commitment to\n labour-rights protections.\n <\/p>\n

    \n Beyond cultural attitudes, the divergence also reflects differing levels\n of trust in government policymakers and in big technology companies, and\n belief in personal benefit from the technology\u2014with Chinese attitudes\n ranking higher than Americans\u2019 views. We believe these perceptual\n differences could help shape each country\u2019s AI development trajectory.\n <\/p>\n

    \n The China-U.S. AI competition\u2019s outcome remains uncertain, with the\n competitive landscape shifting rapidly as new models emerge. We think a\n few factors will be critical differentiators for China\u2019s long-term AI\n development relative to the U.S., including abundant engineering talent,\n reliable and affordable energy supplies and capacity for large-scale\n industry applications.\n <\/p>\n

    \n With the contribution of Leo Shao. <\/em>\n <\/p>\n","protected":false},"excerpt":{"rendered":"

    China\u2019s AI models now rival leading U.S. alternatives yet face mounting semiconductor constraints. We examine the latest developments in Chinese AI and explore what these advances mean for the competitive landscape ahead.<\/p>\n","protected":false},"author":22,"featured_media":35555,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"rbcwm_post_date":"2026-10-15T15:31:24","editor_notices":[],"rbc_url_alias":"","rbcwm_featured_desktop_image_position":"","rbcwm_featured_mobile_image_position":"","_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[73],"tags":[238,1175,1176],"rbcwm_content_owner":[607],"rbcwm_need":[],"rbcwm_segment":[],"rbcwm_solution":[],"rbcwm_topic":[74],"rbcwm_channel":[],"rbcwm_format":[],"class_list":["post-35554","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analysis","tag-ai","tag-china-ai-2","tag-chinese-ai","rbcwm_content_owner-pag","rbcwm_topic-global-insights"],"acf":{"rbcwm_subtitle":"China\u2019s AI models now rival leading U.S. alternatives yet face mounting semiconductor constraints. 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