In late 2025, a notable shift began taking shape in global capital markets: foreign investors started showing markedly warmer interest in China’s artificial intelligence sector. The catalyst was not a sudden breakthrough in Chinese model capabilities—though those have been advancing—but rather a straightforward valuation gap. Chinese AI companies, by the numbers available to investors, were trading at significantly cheaper valuations than their U.S. counterparts. At the same time, a growing chorus of market voices, including prominent investors who had previously called major market dislocations, began warning that U.S. AI stocks might be in bubble territory.
The contrast is stark. In the United States, the AI trade has been the dominant driver of equity market gains. The rally has been powered by a handful of hyperscalers and chipmakers, with valuations reaching levels that have historically preceded sharp corrections. Michael Burry, the investor who gained fame for predicting the 2008 U.S. housing crash, was among the latest to publicly flag bubble risks in U.S. AI names. His warning came on a Monday in late November 2025, adding to a growing list of cautious voices.
Meanwhile, the U.S. AI trade itself has been rotating. In the same week, attention shifted to Alphabet following positive reviews of its newest AI model. This came just weeks after Warren Buffett’s Berkshire Hathaway disclosed a rare technology position in the stock. The churn within the U.S. AI complex—investors moving from one large-cap name to another based on model releases—illustrates both the enthusiasm and the nervousness that characterize the current market environment.
China’s situation is different in kind, not just degree. Vincent Lu, partner and head of private equity at Boman Group, an Australian asset manager based in Melbourne, told CNBC that bubble risks for Chinese AI firms appear far more contained than in the U.S. Boman Group oversees AU$910 million, which converts to approximately $591.26 million, with most of that capital allocated in Australia and North America. Notably, the firm has participated in funding rounds for U.S.-based AI companies—Anthropic earlier in 2025 and OpenAI the previous year. Lu’s perspective is therefore not that of a China bull by default; it is an assessment from an investor with direct exposure to the U.S. AI private markets who nonetheless sees a different risk profile in China.
The interest from foreign investors is not merely anecdotal. It reflects a broader reassessment of where the AI opportunity set truly lies. Chinese AI models, while lagging behind the frontier models from U.S. labs, have emerged as cheaper and highly capable alternatives. For a large swath of real-world AI applications, frontier capability is not required. This has driven adoption of Chinese models not only in the United States—where cost-conscious enterprises are exploring alternatives—but also, reportedly, in developing economies across Africa and other regions.
The geopolitical and economic implications are significant. Daniel Remler, senior fellow in the technology and national security program at the Center for a New American Security (CNAS), a think tank, told CNBC that based on current trends, it seems more likely than not that Chinese AI will become the default for developing countries. That is a statement with far-reaching consequences for the global technology order, for U.S. influence, and for the commercial landscape that European robotics and automation companies operate within.
Rory Green, chief China economist at TS Lombard, went further. He warned that most of the world might be running on a “Chinese tech stack” within five to ten years. Green’s comment, made in the context of China’s rapid catch-up in technology, underscores the speed at which the competitive landscape is shifting. China, he noted, is “moving up the value chain very rapidly,” threatening what has been a U.S. monopoly on AI.
The market context for these developments is a U.S. equity rally that carries identifiable risks. Analysts have pointed to three specific factors that could pop the AI bubble. First, the cheaper Chinese large language models (LLMs) are putting pressure on the pricing power of U.S. AI companies. Second, hyperscaler return on investment (ROI) concerns are mounting. The hyperscalers—the massive cloud and data center operators—are projected to spend approximately $700 billion in capital expenditures in fiscal year 2026, a figure that represents about 2% of U.S. GDP. If that spending delivers negative ROI, the tech sector tailwinds that have driven market gains could reverse sharply. Third, infrastructure constraints are becoming a binding issue. Electricity shortages, rising energy costs, and local opposition are delaying nearly half of the planned AI data centers for 2026.
These three factors are interconnected. Cheaper Chinese models reduce the revenue expectations for U.S. AI services, which in turn undermines the ROI case for massive capital expenditures. If the infrastructure cannot be built on time due to power and permitting issues, the revenue side is further delayed. The result is a fragile setup for U.S. AI valuations, even as the technology itself continues to advance.
Why it matters for European robot service
For European companies that service, maintain, and integrate industrial robots, the shifting dynamics of the global AI market are not an abstract financial story. They are a direct input into procurement decisions, technology roadmaps, and competitive positioning.
The first and most immediate implication is cost. European robot service providers and their customers have been operating in an environment where AI capabilities—whether for vision systems, path planning, predictive maintenance, or quality control—have been priced at a premium, largely set by U.S. frontier labs. The emergence of cheaper Chinese models that are “highly capable” for most use cases changes that pricing dynamic. For the majority of industrial AI applications, a robot service provider does not need a frontier model that can reason across complex multimodal inputs. It needs a model that can reliably classify defects, predict bearing failures, or optimize a pick-and-place sequence. Chinese models are increasingly able to deliver that at a fraction of the cost.
The second implication is around supply chain and technology stack choices. If, as TS Lombard’s Rory Green suggests, most of the world might be running on a “Chinese tech stack” within five to ten years, European robot service companies need to consider which ecosystem they are building their expertise around. A service provider that invests heavily in U.S.-only AI integrations may find itself at a cost disadvantage. Conversely, a provider that builds expertise in Chinese AI platforms may gain access to markets—particularly in developing countries—where those platforms are becoming the default.
The third implication is about the data center infrastructure that underpins cloud-based robot services. The report of delays in nearly half of planned AI data centers for 2026, driven by electricity shortages, rising costs, and local opposition, is directly relevant to European operators. Many robot service offerings rely on cloud processing for compute-intensive tasks. If data center capacity in the U.S. and Europe does not come online as planned, latency and availability could suffer. European robot service providers should be assessing their reliance on cloud AI and considering edge computing alternatives that are less dependent on hyperscaler infrastructure timelines.
The fourth implication is competitive. The U.S. AI trade has been a powerful magnet for capital, and that capital has funded rapid iteration. If a bubble pops, the flow of cheap capital into U.S. AI startups could slow dramatically. That would have a chilling effect on the pace of innovation in the U.S. AI ecosystem, potentially opening space for Chinese competitors and for European companies that can bridge between ecosystems. For robot service providers, this means the competitive landscape could shift faster than anticipated, with new entrants from China or from developing countries that have adopted Chinese AI as their default.
The fifth implication is around standards and interoperability. If Chinese AI becomes the default in developing countries, as CNAS’s Daniel Remler suggests is more likely than not, then the industrial robots deployed in those countries—and the service providers that support them—will increasingly operate on Chinese AI platforms. European robot service companies with global operations or ambitions will need to support a multi-platform environment. This is not a trivial engineering challenge. It affects everything from API compatibility to data governance to cybersecurity certification.
The sixth implication is geopolitical risk. European companies are caught between the U.S. and China in a technology competition that shows no signs of abating. The U.S. has imposed export controls on advanced chips, which affects what Chinese AI models can do and what hardware they run on. China has responded with its own industrial policies. For European robot service providers, this means navigating a complex regulatory environment where the choice of AI platform has implications beyond cost and capability. It affects compliance, export controls, and the ability to serve customers in different jurisdictions.
What buyers and operators should know
For buyers of robot services and operators of robotic systems in Europe, the developments in the AI market have practical implications that warrant attention.
First, pricing pressure is likely to continue. The availability of cheaper Chinese AI models that are capable for most use cases means that AI-enabled robot services should become more affordable over time. Buyers should be skeptical of pricing that assumes a U.S. frontier-model cost structure. Competitive bidding should include options that leverage lower-cost AI models where the application does not require frontier capability.
Second, capability assessment should be use-case specific. The source material notes that Chinese models lag behind the frontier but are “highly capable” for most applications. Buyers should not assume that “lagging behind the frontier” means inadequate. For most industrial robot applications—defect detection, predictive maintenance, path optimization—the gap between Chinese and U.S. models may be irrelevant. Buyers should test models on their specific workloads rather than relying on benchmark comparisons that may not reflect real-world conditions.
Third, infrastructure risk is real. The projection of $700 billion in hyperscaler capex for fiscal year 2026, and the report that nearly half of planned AI data centers for 2026 are delayed due to electricity shortages, rising costs, and local opposition, should inform procurement decisions. If a robot service depends on cloud AI processing, buyers should ask about the provider’s infrastructure contingency plans. What happens if the data center that processes your vision data is delayed? What is the fallback? These are questions that should be asked now, not when a service degradation occurs.
Fourth, the timeline for technology stack decisions is shorter than many assume. The warning that most of the world might be running on a “Chinese tech stack” within five to ten years is a strategic planning input. For robot service contracts that span multiple years, the choice of AI platform made today will shape the service provider’s cost structure and capability set for the duration of the contract. Buyers should consider whether their service provider has a multi-platform strategy or is locked into a single ecosystem.
Fifth, the geopolitical dimension cannot be ignored. The U.S. AI export controls and China’s response create an environment where technology choices have diplomatic and regulatory consequences. European buyers should ensure that their robot service providers can demonstrate compliance with applicable export controls and data governance requirements, particularly if they operate in multiple jurisdictions.
Sixth, the market for AI-enabled robot services is becoming more global. The report that Chinese AI adoption is rising in developing economies in Africa, and the assessment that Chinese AI will likely become the default for developing countries, suggests that the competitive landscape for robot services will include providers from these regions. European buyers may find that global service providers offer cost advantages by leveraging AI platforms that are cheaper and more accessible in developing markets.
Seventh, the risk of a U.S. AI bubble popping has indirect consequences for European buyers. If U.S. AI valuations correct sharply, the funding environment for U.S. AI startups will tighten. Some of those startups may be suppliers to European robot service providers. Buyers should assess the financial health of their critical AI suppliers and consider diversification of AI dependencies.
Eighth, the timeline for AI capability improvements in China is uncertain but appears rapid. The source material notes that China has “rapidly caught up” and is “moving up the value chain very rapidly.” Buyers should expect Chinese AI models to close the remaining gap with U.S. frontier models over time. Procurement decisions that lock in a U.S.-only AI strategy may become increasingly costly relative to a multi-platform approach.
Finally, buyers and operators should recognize what is not disclosed in the source material. The source does not provide specific pricing data for Chinese versus U.S. AI models. It does not provide specific performance benchmarks for Chinese models in industrial robot applications. It does not specify which Chinese companies are attracting foreign investment, nor the size of that investment. It does not provide a timeline for when Chinese AI might become the default in developing countries, beyond the five-to-ten-year window mentioned by one economist. It does not disclose the specific data center projects that are delayed, nor the specific regions affected. It does not provide details on the ROI calculations for hyperscaler capex. These are gaps that buyers and operators should seek to fill through their own due diligence.
The strategic picture, however, is clear. The AI market is bifurcating. The U.S. retains the frontier, but China is winning on cost and accessibility. For European robot service, this means more options, more price pressure, and more complexity. The winners will be those who can navigate a multi-platform world, assess capability on a use-case basis, and build resilience into their infrastructure dependencies.
Sources
https://www.cnbc.com/2025/11/26/cnbc-china-connection-newsletter-capital-ai-sector-valuations-vc-us-bubble-nvidia-deepseek-chatgpt.html
Published by Vigla Media OÜ (Estonia).