China's AI Momentum and Infrastructure Investment Tension Dominate Media Narratives

China's AI Sector Gains Momentum One Year After DeepSeek's "Sputnik Moment"

One year after DeepSeek's low-cost generative AI model sent shockwaves through global markets, China's artificial intelligence sector is experiencing what observers describe as a sustained surge in momentum. Perscient's semantic signature tracking language asserting that DeepSeek or China is winning the AI race registered a z-score of 3.8 this week, the highest among all AI race signatures, and rose by 0.2 from the prior week.

The January 2025 release of DeepSeek's model, which performed comparably to ChatGPT and other leading American chatbots while requiring fewer resources, fundamentally altered the competitive environment. Wei Sun, principal analyst for AI at Counterpoint Research, told Business Insider that DeepSeek's approach represents a "striking breakthrough." The company has since captured approximately four percent of global chatbot market share, according to web traffic analysis firm Similarweb, and is expected to release its next AI model within weeks.

Shares in two leading Chinese AI startups, Zhipu AI and MiniMax, soared on their Hong Kong market debuts this month, with MiniMax's share price more than doubling from its IPO price. As one observer noted on X, Zhipu AI was long expected to lead China's foundation model efforts before DeepSeek emerged, and its successful listing with a market capitalization exceeding $7 billion signals broader investor confidence in the sector. Online hiring platform Zhilian Zhaopin reported a 39 percent increase in applications to AI-related jobs in the first three quarters of 2025.

This momentum persists despite significant constraints. Access to top-end chips from Nvidia remains restricted under White House policies designed to curb China's technological development. Yet Chinese firms have demonstrated strong adaptability. DeepSeek published a paper outlining more efficient approaches to AI development, and analysts suggest that China could meet domestic chip needs by approximately 2028. Nvidia CEO Jensen Huang recently acknowledged that DeepSeek's research may be "the most important AI paper that Silicon Valley researchers have read in the past few years".

Perscient's semantic signature tracking language arguing that big AI capital expenditure is needed to compete with China registered a z-score of 3.4, rising by 0.5 over the past week, one of the largest weekly increases among all signatures. Meanwhile, the signature tracking language asserting that the US must win the AI race remained elevated at 0.8. The framing of AI development as a strategic national priority appears firmly entrenched, even as China's new tech stock boom leaves its economic malaise behind.

Hyperscale Infrastructure Spending Continues Despite Growing Questions About Returns

China's competitive gains and narratives of strategic importance to America have intensified pressure on US hyperscalers to maintain aggressive investment. Perscient's semantic signature tracking language predicting continued expansion of massive AI infrastructure registered a z-score of 3.0, the second-highest among infrastructure-related signatures. However, it declined by 0.5 from the prior week, the largest weekly decline among elevated signatures.

The scale of investment remains staggering. Capital spending by six major US hyperscalers, including Microsoft, Amazon, Alphabet, Oracle, Meta, and CoreWeave, approached $400 billion in 2025 and is on track to hit $500 billion in 2026, then $600 billion in 2027. As one analysis noted, tech capital expenditure as a percentage of GDP nearly matched the combined scale of the largest capital projects of the twentieth century last year, reaching approximately 1.9 percent of GDP, compared to about 1.2 percent for nationwide broadband development, 0.6 percent for the Interstate Highway system, and 0.4 percent for the Manhattan Project.

Yet questions about returns are intensifying. Perscient's semantic signature tracking language indicating growing corporate caution around AI spending registered a z-score of 2.3, rising by 0.2 over the past week. As Politico reported, investors are increasingly bracing for signs that the AI profit story could fall short, given that Big Tech has not yet shown meaningful profits tied directly to AI. The market now demands tangible evidence of productivity gains and bottom-line impact.

Energy represents the most tangible constraint. The Economist noted that Texas grid operator Ercot has received requests for more than 226 gigawatts of power by 2030, nearly 100 times more than it approved in 2022. Data centers are projected to consume about 6.7 to 12 percent of US electricity by 2028, up from 4.4 percent in 2023. As the Wall Street Journal observed, if the AI business stalls, tech companies could be left with more than just data centers as stranded assets.

The signature tracking language arguing that promised AI efficiency improvements haven't materialized registered a z-score of 1.6, rising by 0.1 over the past week. A Forrester analyst noted that despite billions pouring into AI infrastructure, productivity gains aren't showing up in macro data. Bureau of Labor Statistics numbers tell a familiar story: productivity grew by 2.7 percent annually from 1947 to 1973, dropped to 2.1 percent from 1990 to 2001 after PCs went mainstream, and fell further to 1.5 percent from 2007 to 2019.

Goldman Sachs has warned that there is "a diminishing probability that all of today's market leaders generate enough long-term profits to sufficiently reward today's investors," with strategists noting that AI stock valuations resemble late 1990s bubble signals. Investor Michael Burry has stated that he expects return on investment to continue falling, with most AI companies going bankrupt and much of AI spending written off. However, heading into 2026, the vast majority of digital leaders surveyed report that AI remains a high priority and that their companies are getting measurable business value from investments.

AI Skills Emerge as a Workforce Imperative Amid Mental Health and Social Concerns

The infrastructure buildout is reshaping labor markets as employers increasingly treat AI fluency as a baseline requirement. Perscient's semantic signature tracking language emphasizing AI competency as a baseline professional requirement registered a z-score of 1.0, rising by 0.3 over the past week, one of the largest weekly increases among workforce-related signatures.

According to McKinsey's latest workforce research, the number of workers in occupations where AI fluency is explicitly required has grown sevenfold in just two years, from approximately 1 million in 2023 to around 7 million in 2025. That represents the fastest-growing skill category in US job postings. About one in ten job vacancies in advanced economies demands at least one new skill.

The World Economic Forum has emphasized that sustained productivity benefits will come through people's ability to harness the technology effectively. This requires addressing the "learning gap" between what AI tools can do and how well workforces can use them. Yet new ManpowerGroup data reveals a troubling disconnect: regular AI usage jumped by 13 percent to 45 percent of workers, while confidence in using technology fell sharply by 18 percent. More than half the global workforce reported receiving no recent training and no access to mentorship opportunities.

According to Forbes, professionals with AI skills earn 56 percent more than their peers without them. Meanwhile, AI is enabling workers to build second careers after hours, creating freelance consultancies, launching digital products, and experimenting with entrepreneurship, all while keeping full-time jobs.

The signature tracking language asserting that AI will generate new employment categories registered a z-score of 0.1, rising by 0.2 over the past week. As one software engineering leader noted, AI amplifies senior expertise but may starve the junior pipeline that creates it.

Perscient's semantic signature tracking language asserting that AI will increase mental health problems registered a z-score of 1.7, remaining elevated. The use of AI in mental health has outpaced both scientific validation and regulatory oversight, and millions are using AI-powered virtual therapists for emotional support. A new Stanford study reveals that AI therapy chatbots may not only lack effectiveness compared to human therapists but could contribute to harmful stigma and dangerous responses.

The signature tracking language arguing against AI for mental health purposes registered a z-score of 0.9, while the signature tracking language asserting that AI can provide meaningful mental health support registered 0.4. Reports of "AI psychosis" are emerging, with 17 documented cases according to Nature. As Time reported, therapy should be hard, which is precisely why AI cannot replace it. The concern, as one social media user observed, is that AI chatbots function as enablers rather than genuine therapeutic tools, validating users rather than challenging them toward growth.