Abstract: The financial market plays a crucial role in supporting firms in advancing basic technologies. This study investigates how the investors in the A-share market in China value the firms with different layers of artificial intelligence (AI) technology. Specifically,we classify firms with different layers of AI technology into three groups-basic,mixed,and application-and examine the initial public offering (IPO) prices and the subsequent returns of these AI firms. Our sample consists of all AI firms that went public on China’s A-share market between 2007 and 2024. We identify the scope of AI firms by ①reviewing the “main business” and “business scope”sections of listed companies in the“cninfo.com”database to identify AI firms initially; ② reading“the main business of the issuer and changes since its establishment” in the IPO prospectuses to further identify AI business of these firms around the IPO time. The sample comprises 300 AI firms that conducted IPOs on the Shanghai and Shenzhen A-share markets,including the Growth Enterprise Market and STAR Market,from 2007 to 2024. Based on the Artificial Intelligence Standardization White Paper (2021) and ISO/IEC FDIS 22989 standards,firms are then classified into basic,mixed,and application layers. IPO initial day return is used to measure issuance pricing efficiency,and post-IPO long-term return after the initial day is used to measure price efficiency on the initial day. Our empirical analyses reveal the following findings:①AI firms in the basic layer exhibit significantly higher returns on the IPO day,outperforming firms in the application layer by 106.4%. The evidence indicates relatively lower offering prices for firms in the basic layer; ② firms in the basic layer exhibit significantly higher post-IPO long-term returns after the initial day,with 20-day Buy and Hold Abnormal Returns (BHAR) higher by 11.5%,60-day by 19.7%,120-day by 18.4%,and 240-day by 20.8%. The evidence suggests the underpricing of firms in the basic layer on the initial day; ③ Firms in the basic layer possess more invention patents but fewer total,utility,and design patents,consistent with the explanation of higher information asymmetry in pricing basic technologies. Firms in the basic layer also show weaker pre-IPO profitability but higher future earnings growth,aligning with the explanation tied to historical issuing PE limits in China’s A-share market and investor myopia; ④ Registration-based reform significantly reduces differences in initial returns and post-IPO long-term returns after the initial day across the AI firms with different technology layers. It suggests that the reform improves pricing efficiency for firms in the basic layer. This improvement is associated with the removal of pricing restrictions under the reform and the shift of focus from profitability metrics to the quality of information disclosure. Our study contributes to the literature by ① addressing the lack of research distinguishing basic,mixed,and application technology and examining the difference in valuation between the firms with different layers of technology and ② exploring the capital market’s support for the AI industry,a vital area for China’s economic growth. Additionally,this paper provides several practical insights for different stakeholders:① From the perspective of firms,basic technology can drive future financial performance and firms’ value growth. Therefore,companies should increase their investment in basic technology. At the same time,firms with basic technology should strengthen information disclosure to alleviate undervaluation. ② From the perspective of investors,since the IPO pricing of firms with basic technology remains relatively low,investors should enhance their understanding of the economic significance of basic technology and the value of related firms. It could facilitate the creation of higher investment returns. ③ From the perspective of policymakers,efficient pricing is important for the development of basic technology. The registration system reform has strengthened the capital market’s support for basic technology. Future policies should continue to optimize asset allocation and increase the efficiency of market pricing. The findings in this paper are based on the Shenzhen and Shanghai A-share markets in China,where the information environment and disclosure practices differ from those in the developed Western markets,which may have different IPO pricing strategies. Therefore,further research is needed to answer the question of whether our findings can be generalized to other capital markets.