Summary of Key Points
As the first Chinese company to go public in the US this year, Dasearch experienced a "bloody listing" after its initial public offering (IPO): Its stock price plummeted from the issue price of $17, and although it briefly rebounded, it still fell by more than 54% compared to the issue price. Its market value is now less than $400 million—nearly a 90% decrease from its peak of $3.5 billion in 2018, and even lower than its total fundraising amount of $1.2 billion. Approximately 60% of the funds raised were supported by existing shareholders, Ant Group, indicating a lack of interest from market investors. The underlying issues include: an AI strategy that lacks substantial research and development (R&D) support, a contraction in its main business due to compliance requirements, a high market share but weak revenue-generating capabilities, and persistent trust challenges in the used car industry.
I. The Truth Behind the "Bloody Listing": Investors Voting with Their Feet
On the first day of trading, Dasearch's stock price was almost halved. Despite some recovery, its market value fell far short of expectations. There are two main reasons for this:
- Relyance on Existing Shareholders for Fundraising: Of the $51 million raised, Ant Group subscribed for $30 million (60%), indicating that external investors were not enthusiastic about the company.
- Unsolid Valuation: Dasearch tried to market itself as a leader in "used car AI infrastructure," but its financial data exposed weaknesses—low R&D investment and declining revenue, which are insufficient to support a high valuation for a technology company.
In simple terms, the market did not buy into its AI narrative or its ability to generate profits, leading to a sharp drop in the stock price.
II. Why Doesn't the AI Story Hold Water? Excessive R&D Expenses Are the Problem
Dasearch claims to be the "first stock in the AI application infrastructure sector," but its prospectus reveals issues with its R&D efforts:
- Decreased Absolute Investment: R&D expenses in 2025 were $84.66 million, a 15% decrease from $100 million in 2024 and even less than $85.60 million in 2023.
- Passive Increase in the Percentage of Revenue: The proportion of R&D expenses as a percentage of revenue rose from 9.4% in 2023 to 12.5% in 2025. This increase was not due to higher investment but rather because revenue decreased from 948 million to 677 million (a nearly 30% drop), causing the percentage to rise.
- Lack of Competitiveness: Leading SaaS companies invest between 20% and 30% in R&D, while Dasearch's investment of just over $80 million is not enough to build a significant competitive advantage in AI.
In other words, its AI efforts seem more like a marketing label without substantial financial investment, which naturally skeptical investors do not believe in.
III. Contraction of the Main Business: Cutting Losses for Compliance, but Losing Revenue
Dasearch's revenue declined by nearly 30%, mainly due to the reduction of its finance-related services:
- Disposal of B2B Financial Intermediation: It cut off its business of connecting car dealers with banks at the end of 2024 because of stricter lending regulations, as required for overseas listings.
- Failing C2C Service: Its "Rent First, Buy Later" model was once popular due to low down payments, but consumers mistook it for a installment purchase. It turned out to be a financing arrangement where the car did not belong to the buyer, leading to numerous complaints and subsequent termination of the service.
While these moves cleared the way for the IPO, they also resulted in a significant loss of revenue. In other words, Dasearch sacrificed profitable operations to meet listing requirements, only to find itself with reduced earnings after going public.
IV. The Embarrassing High Market Share: Covering 90% of Dealers but Failing to Generate Profit
Dasearch's "Dafengche" system dominates the used car dealer market with over 90% market share, yet its ability to convert this into profit is weak:
- Free Strategy Hinders Revenue Generation: The Dafengche system is mostly free for dealers, and attempts to generate revenue through additional services have been unsuccessful due to low willingness to pay. For example, a dealer in Wenzhou stated that the paid version was merely for reference purposes, and pricing decisions were still based on experience.
- Dealers' Financial Strains: In the first half of 2025, 73.6% of used car dealers were in亏损, with average profits of only $1,500 per transaction. They see digital tools as luxury additions rather than necessities.
As a result, Dasearch is trapped in a cycle where its wide market coverage leads to no customer payments and thus limited price increases, preventing its main business from thriving.
V. The Core Pain Point of the Used Car Industry: Trust Issues
Both Dasearch's B2B model and Uxin's C2C model face the non-standard nature of the used car market:
- Unclear Vehicle Conditions: Each vehicle has unique conditions, and consumers fear buying accident-prone cars, making it difficult for dealers to set prices and reducing transaction efficiency.
- Traffic Is More Valuable Than Tools: Dealers prefer to pay for traffic that brings them customers (e.g., from platforms like Douyin or paid search services) rather than for management tools. Platforms with C2C traffic, such as Duyonde and Autohome, can combine both traffic and tools to compete with Dasearch.
Dasearch's AI technology merely adds a price reference feature to its system without addressing the fundamental trust issues in the industry, preventing it from breaking through market barriers.
Conclusion
Dasearch's "bloody listing" reflects a mismatch between its ambitious narrative and its actual capabilities. Its AI strategy lacks R&D support, its main business has shrunk due to compliance requirements, and its high market share fails to generate significant profits. This IPO is not the end of the story but rather a test of its ability to deliver on its promises—whether it can truly develop its AI technology or find new ways to make money.
(The entire analysis is written in plain language, avoiding technical jargon, and each point is supported by specific data and examples to help non-financial readers understand the challenges behind Dasearch's IPO.)