China's securities regulator will expand the STAR Market's fifth listing standard to cover artificial intelligence large-model companies, while the Shanghai Stock Exchange published detailed eligibility rules on June 17. The moves, announced at the 2026 Lujiazui Forum, create a clear IPO pathway for AI firms that have at least one product at scale, and they arrive as capital markets debate how to value companies whose worth defies traditional discounted cash flow models.
CSRC Chairman Wu Qing said the policy will "actively support high-quality AI large-model enterprises in going public" as well as quantum technology, bio-manufacturing, and embodied intelligence companies. The SSE's new Guidance No. 10 requires that an applicant's main business be in ongoing R&D or tech commercialization, with at least one large-model product already launched and scaled. Issuers must also demonstrate clear commercialization plans, show prominent market standing, and meet data security and intellectual property rules.
Valuation arguments collide with a $75 billion IPO
"There is no conversation today that doesn't involve artificial intelligence," said Gokul Laroia, Morgan Stanley Asia CEO, during the forum. He called China the only market outside the U.S. with a full AI ecosystem and said current capital levels are "only a small fraction of what is required." Two weeks earlier, SpaceX had priced the largest IPO in history at $75 billion, fueling the valuation debate.
ICBC President Liu Jun noted that a DCF framework cannot justify SpaceX's market value above $2 trillion. "It has indeed surpassed that figure," he said. "It has designed its core technology with a future-option-like structure-a logic fundamentally distinct from traditional economic models." Liu argued that computing power scale and data density are becoming better axes for measuring AI firms, producing a tech premium that behaves like an option.
Others cautioned against ignoring risk. Philip Brown, CEO of M&G Bank, described "an upward frenzy that disrupts all forms of valuation," pointing to giants like NVIDIA and SpaceX whose multiples have no historical precedent. Cybersecurity and cloud risks, he said, create a vulnerability that "may not be evident now, but undoubtedly exists."
AI in practice: banking, insurance, and reinsurance
At a separate plenary, bank and insurance chiefs detailed how they are deploying AI. Agricultural Bank of China Chairman Gu Shu flagged large-model risks including poor interpretability and probabilistic accuracy problems, and said his firm uses "AI countering AI"-model cross-checking and business-data calibration-as a defense layer. Bank of China President Zhang Hui said AI is reshaping service delivery, value creation, and management structures, requiring systemic organizational changes.
China Pacific Insurance Chairman Fu Fan highlighted the complementary roles of Shanghai, which is scaling AI across its industrial base, and Hong Kong, which is concentrating on frontier AI in compliance and risk. China Re Group Chairman Zhuang Qianzhi argued that reinsurers must shift from capital-intensive to technology-intensive models, but he stressed that AI cannot replicate human reasoning: "The ability to infer broader principles from a single case-this is something artificial intelligence cannot replace."
The scaling of AI in the financial services industry is pushing professionals toward resources that focus specifically on AI for Finance, where the intersection of technology, regulation, and business models is reshaping careers.
Regulators set boundaries for the capital markets AI wave
Wu Qing said the CSRC will soon issue guidelines to improve safety and reliability in investment research and client-facing tools, and will crack down on illegal stock recommendations, rumor-mongering, and unlawful trading powered by AI. Gu Shu advocated "using AI to fight AI" through automated verification of model outputs. Christian Stracke called for a global principle-based framework that imposes strict oversight in high-risk areas while allowing low-risk experimentation.
Hungarian National Bank Deputy Governor Dániel Palotai said AI is a double-edged sword: it helps regulators detect fraud faster but could enable new forms of "shadow economy" and sophisticated evasion. He proposed a shared platform for real-time data exchange among regulators to block suspicious transactions. London's Christopher Hayward rejected the idea of adding new AI-specific regulatory bodies, saying the focus should be on finding effective mechanisms within existing institutions.
Why this matters for finance professionals
The SSE's concrete eligibility criteria-requiring a launched product, defensible tech advantage, and a commercialization plan-will shape which AI companies reach public markets, creating new deal opportunities and benchmarks for private valuations. At the same time, the forum's valuation discussions show that traditional DCF models are losing ground to option-like frameworks that price computing power and data density. Finance professionals who build literacy in these metrics, understand the new listing rulebook, and follow the coming regulatory guidelines will be better equipped to evaluate AI-related assets, structure capital raises, and manage the risks regulators are now actively codifying.
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