Long-term Investment Potential Analysis of Hong Kong Listed Company Jingtai Technology (2228.HK)

$XTALPI(02228.HK) Stock Price Trends and Market Performance

XtalPi Holdings (HKEX:2228) was listed on the Hong Kong main board on June 13, 2024, with an issue price of HKD 5.28, and briefly rose to HKD 6.58. Subsequently, the stock experienced significant volatility, reaching a high of HKD 7.2, before falling back to a low of approximately HKD 3.05 by the end of 2024. As of early February 2025, the stock price rebounded to around HKD 5, close to the issue price, reflecting a decline of approximately 7.6%. This rollercoaster performance reflects the market's sentiment towards the AI pharmaceutical concept 16.200)) A valuation correction has occurred, with the current price significantly retreating from its high point ( XtalPi Holdings Limited (HKG: 2228) Stock Price & Overview - Stock Analysis). The company's current total market capitalization is approximately HKD 16.97 billion (about USD 2.17 billion). It is worth noting that the company plans to issue 264 million new shares at a price of HKD 4.28 per share in January 2025, raising approximately HKD 1.13 billion. This issuance reflects investors' confidence in the company's long-term prospects in the Hong Kong market over the past year ( XtalPi Holdings Limited (HKG: 2228) Stock Price & Overview - Stock Analysis), but it also exerts some dilution pressure on the stock price in the short term. Overall, XtalPi Holdings, as the first AI-driven company in Hong Kong ( Sidley Represents XtalPi Holdings Limited in its Placing of New Shares | News | Sidley Austin LLP), has its stock price highly dependent on market sentiment and industry news. Under the influence of the global economic environment and Sino-U.S. technology tensions, Hong Kong technology stocks are generally sluggish, but XtalPi Holdings ( [Sidley Represents XtalPi Holdings Limited in its Placing of New Shares | News | Sidley Austin LLP](https://www.sidley.com/en/newslanding/newsannouncements/2025/01/sidley-represents-xtalpi-holdings-limited-on-its-placing-of-new-shares#:~:text=Sidley%20represented%20XtalPi%20Holdings%20Limited, The emerging field that managers are paying attention to.

Industry Trend: Biopharmaceuticals + AI and ( Sidley Represents XtalPi Holdings Limited in its Placing of New Shares | News | Sidley Austin LLP) The trend of integration between biopharmaceuticals and AI: In recent years, the application of AI in drug development has rapidly increased, becoming an important trend in the biopharmaceutical industry ( Welcome to PowerPoint ) as personnel hope to accelerate new drug discovery, improve R&D success rates, and reduce costs. According to Morgan Stanley's estimates, the application of AI in early drug development is expected to bring an additional 50 new therapies and over $50 billion in new revenue over the next decade. Major pharmaceutical companies are collaborating with AI startups to utilize artificial intelligence for target discovery, molecular design, and candidate drug screening. Several candidate drugs designed with AI assistance have already entered clinical trial stages, indicating that this technology is moving from proof of concept to actual output. However, overall, AI in pharmaceuticals is still in the early exploratory stage, and its effectiveness awaits more successful cases to prove. A ( [Tencent-Backed AI Drug Discovery Startup Xtalpi Files For Hong Kong IPO](https://bestofai.com/article/tencent-backed-ai-drug-discovery-startup-xtalpi-files-for-hong-kong-ipo#:~:text=Despite%20widening%20net%20losses%2C%20Xtalpi, The rise of generative AI over the past three years has continuously achieved technological breakthroughs in molecular design and biological big data analysis, with the industry having high hopes for technological advancements starting in 2024. On the other hand, many AI pharmaceutical startups have experienced a rollercoaster of valuations over the past few years: early capital (Tencent-Backed AI Drug Discovery Startup Xtalpi Files For Hong Kong IPO) saw significant stock price adjustments as the market rationalized. For example, the UK AI drug company Exscientia has seen its market value drop by about 75% since its U.S. stock market listing in 2021; Europe's BenevolentAI also saw a significant decline in stock price after going public via SPAC. This reflects investors' views on the industry's prospects (Tencent-Backed AI Drug Discovery Startup Xtalpi Files For Hong Kong IPO) that the global trend in the biopharmaceutical + AI field has great long-term potential but significant short-term challenges: major pharmaceutical companies continue to invest, and the market size is expected to grow at an annual compound growth rate of about 30%, but truly disruptive results will still take time.

The trend of integrating polymer material technology with AI: In addition to new ( Exscientia (EXAI) Market Cap & Net Worth - Stock Analysis **) applications increasingly being used in the research and design of new materials (especially polymer materials) The field of materials science has traditionally relied on repeated experiments to screen new materials, while AI and machine learning can accelerate this process through big data analysis and performance prediction. For example, AI can predict the properties of new combinations based on a large amount of existing materials data, helping to screen potential candidate materials under experimental conditions. The U.S. National Renewable Energy Laboratory (NREL) has developed a machine learning tool called "PolyID," which can screen millions of possible polymer structures at once, quickly finding sustainable polymers that meet specific performance requirements. Large chemical materials companies are also getting involved, such as Hitachi High-Tech, which has developed its own AI tools to shorten compound screening and simulation times. In fields such as storage, AI is being used to optimize material formulations, improve R&D efficiency, and reduce costs. This trend is expected to give rise to more efficient and greener new materials, with design solutions that reduce resource waste during the material development process through simulation Compared to pharmaceuticals, the marketization level of the materials + AI field is currently slightly lower, with relatively few startups focusing on this area, most of which are internal projects of large materials companies or research ( AI for enhanced Materials Development and Manufacturing | Plastics Engineering) and a few other companies have begun to extend their AI R&D capabilities to applications in agriculture, cosmetics, and energy. Overall, the prospects for A ( AI for enhanced Materials Development and Manufacturing | Plastics Engineering) are broad: it is expected to bring an efficiency revolution to the discovery and manufacturing of polymer materials, similar to that in drug development, contributing to sustainable materials ( AI for enhanced Materials Development and Manufacturing | Plastics Engineering) ( [AI for enhanced Materials Development and Manufacturing | Plastics Engineering](https://www.plasticsengineering.org/2023/12/ai-for-enhanced-materials-development-and-manufacturing-002734/#:~:text=Another%20example%20of%20this%20emerging, Xtalpi's core competitiveness lies in its comprehensive "AI + physical simulation + automated experimentation" technology platform. The company was founded by three MIT-trained quantum physicists, combining principles with artificial intelligence algorithms and cloud computing to develop an efficient platform for new drug and new material research and development. On the software side, Xtalpi has independently developed several platform tools: for example, the AtomePai integrated technology platform launched in 2017 and the Renova small molecule drug discovery platform; it has developed a high-precision molecular interaction calculation system XFEP, used for accurately predicting molecular interactions and properties. Subsequently, the company's technological landscape expanded from small molecules to large molecules, developing the antibody discovery platform "XupremAb" and the protein generation model "ProteinGPT," among others. These tools, combined with machine learning algorithms, can simulate the structures and properties of thousands of compounds or biomolecules in a virtual environment, efficiently screening for optimal candidates.

Unlike pure software companies, Xtalpi has also built a highly automated laboratory ("intelligent robotic wet laboratory"). The company has established facilities in Shenzhen, Shanghai, and other locations -on-its-placing-of-new-shares#:~:text=Founded%20in%202015%20by%20three,renewable%20energy%2C%20and%20advanced%20materials)) Robots and automated processes complete tasks such as polymorph screening, compound synthesis, and biological experiments. This deep integration of "dry experiments" (computational simulations) and "wet experiments" (actual experiments) ( About XtalPi) accelerates iteration speed. For example, XtalPi initially gained fame for drug polymorph prediction, predicting the crystal structures of small molecule drugs through quantum physics calculations ( About XtalPi) for solid-state screening. This capability is at the forefront globally and helps solve one of the key challenges in drug development.

In terms of competitive landscape, XtalPi faces competition from all ( About XtalPi) ( About XtalPi) *XtalPi's platform covers small molecules, antibody proteins, and even material molecules, spanning a wide range of fields. In contrast, some competitors focus on specific areas, such as Recursion, which emphasizes high-throughput imaging and phenotypic screening (biology-driven), Exscientia and Insilico, while Schrödinger excels in small molecule physical simulations. XtalPi's multi-domain layout means broader market opportunities but also requires multiple ( About XtalPi) ( About XtalPi) family establishment, emphasizing the combination of physical accuracy and AI algorithms. Its quantum chemistry calculation capabilities and proprietary force field development make simulation results more refined, in crystal structures ( [Welcome to PowerPoint](https://www.stifel.com/newsletters/investmentbanking/bal/marketing/healthcare/biopharma_timopler/biopharmamarketupdate_06.17.2024.pdf#:~:text=exchange%20after%20nearly%20%24115%20mil lion,new%20pathway%20for%20specialist%20technology)) strength. The established company Schrödinger has rich experience in this area and is a potential competitor; however, XtalPi emphasizes AI-driven automatic optimization. In addition, XtalPi's developed Prote ( About XtalPi) is at the forefront of the AI wave, using large models for biomolecular design, with a rapid pace of technological iteration.

• Experimental verification capability: XtalPi has established its own robotic laboratory, which is an advantage that many software-centric competitors lack. Exscientia has also acquired laboratories in recent years to verify AI-designed molecules, and Recur (About XtalPi) (About XtalPi) has conducted material-level validations such as screening. This end-to-end system helps improve the reliability of results—model predictions can be timely verified and fed back in internal experiments, forming a closed-loop optimization**.
• *Competition in the new materials field:** In the field of polymer and materials AI, XtalPi has almost no direct publicly listed company counterparts. Most AI new material research and development is still in academia or large enterprises (such as material giant BASF investing in AI research), and specialized startups like Citrine Informatics in the United States have not yet gone public. Therefore, XtalPi can be considered a pioneer in this sector. Its challenge lies in the need to rapidly expand its materials science team and industry know-how to match the pharmaceutical field ( About XtalPi) business model: **XtalPi primarily generates revenue by providing R&D services and solutions to clients. It does not sell drugs themselves but sells "innovation capabilities"—for example, providing candidate compounds for pharmaceutical companies and new formulation suggestions for material companies. This model is similar to that of a technology platform company, where successful cases will bring reputation and more collaborations. In contrast, some competitors (like BenevolentAI) attempt to incubate drug pipelines independently, which carries higher risks but also greater rewards upon success. XtalPi has so far chosen to co-create with partners rather than independently develop end products, taking a more stable path, but it may also parallelly incubate its own new drug projects in the future to share the value of the results.

Overall, XtalPi Technology constructs ( About XtalPi) a platform**, Its competitive advantage lies in integrating advanced algorithms with practical experimental capabilities, enabling it to provide customized R&D support for different industries. This comprehensive strength allows it to have a differentiated position among AI pharmaceutical startups. However, the company must continue to invest in R&D to maintain technological leadership, facing rapid advancements from global peers and the validation challenges of traditional R&D methods, as its technological competitiveness must withstand the test of time.

Business Model and Partners

Xtalpi adopts a platform service and collaborative R&D model, with its revenue primarily coming from providing R&D services and collaborative projects for other companies. According to the prospectus, the company has served over 100 biopharmaceutical companies and research institutions. These clients include both international pharmaceutical giants and leading domestic companies in China, as well as numerous small and medium-sized research teams, reflecting the depth of business ( Tencent-Backed AI Drug Discovery Startup Xtalpi Files For Hong Kong IPO) partnerships and commercial collaborations:

• *Global leading pharmaceutical companies:** Xtalpi has attracted the cooperation of large multinational pharmaceutical companies shortly after its establishment. As early as 2017, the company began collaborating with Pfizer to provide drug polymorph screening and crystal form selection services. Subsequently, it has engaged in project collaborations with Johnson & Johnson (J&J), Merck KGaA, and others. These collaborations typically take the form of customized R&D services or joint research, such as screening candidate compounds and predicting the physicochemical properties of drugs using Xtalpi's platform. ( Welcome to PowerPoint) A collaboration for AI small molecule drug discovery was established with Eli Lilly, with a potential total amount of up to $250 million. This milestone agreement includes upfront payments, R&D milestones, and subsequent sales sharing ( [Sidley Represents XtalPi Holdings Limited in its Placing of New Shares | News | Sidley Austin LLP](https://www.sidley.com/en/newslanding/newsannouncements/2025/01/sidley-represents-xtalpi- holdings-limited-on-its-placing-of-new-shares#:~:text=Founded%20in%202015%20by%20three,renewable%20energy%2C%20and%20advanced%20materials)) discovered new drugs. If the cooperation progresses smoothly, Xtalpi will continue to receive milestone payments over the next few years and will also share the value of the results with Eli Lilly. This shows that top pharmaceutical companies have recognized Xtalpi's technology at a strategic level.
• Local Chinese Enterprises: As a company that has grown in China, Xtalpi has also deeply collaborated with domestic pharmaceutical and technology companies. For example, the client list includes well-known Chinese pharmaceutical company Hutchison Medi ( Tencent-Backed AI Drug Discovery Startup Xtalpi Files For Hong Kong IPO ) and Changjiang Life Sciences Technology, among others. These collaborations help Xtalpi establish a foothold in the local market and participate in China's pharmaceutical innovation projects ( Welcome to PowerPoint ). In addition, Xtalpi has also explored collaborations in the domestic energy and chemical sectors. According to Sidley law firm, the company's business has extended to industries such as oil, renewable energy, and advanced materials. Although specific partners have not been disclosed, the possibility of Xtalpi collaborating with large petrochemical companies or new energy materials companies to develop new catalysts and energy storage materials cannot be ruled out.
• Research Institutions and Startups: Among Xtalpi's clients are also numerous ( About XtalPi **) companies For these resource-limited R&D units, XtalPi's platform-as-a-service model provides strong outsourced R&D support and has also incubated and invested in several new drug R&D startups. By incubating promising AI discovery leads, XtalPi retains technical service revenue while acquiring equity or revenue-sharing rights in these startups. Once the incubated projects successfully enter clinical trials or even commercialization, XtalPi has the opportunity to share in extraordinary investment returns. ( About XtalPi) The practices of companies like Schrödinger not only expand the company's influence but also accumulate potential revenue points for the future.

Business Model Evaluation: XtalPi's current revenue model is relatively diversified, including R&D service fees, milestone payments from collaborations, and strategic investment returns. According to the company's disclosures, 100% of its revenue comes from providing R&D solutions to pharmaceutical and related industry clients. This means that short-term performance is highly dependent on the number and scale of collaborative projects. In recent years, XtalPi's revenue has grown rapidly, increasing from approximately RMB 63 million in 2021 to about RMB 174 million in 2023, with a compound growth rate exceeding 70%. The growth momentum mainly comes from new collaborative projects and broader commissions from existing clients. However, relative to the company's tens of billions in R&D investment and a valuation reaching hundreds of billions, this revenue base is still relatively small, and the company currently resembles a high-tech R&D service provider. To unlock greater commercial potential ( [Sidley Represents XtalPi Holdings Limited in its Placing of New Shares | News | Sidley Austin LLP](https://www.sidley.com/en/newslanding/newsannounce Xtalpi Holdings Limited aims to achieve higher value returns (for example, obtaining revenue shares after new drug launches) or expand standardized products (such as software licensing, laboratory automation equipment sales, etc.) to enhance the sustainability of earnings.

• Cooperation Outlook: Overall, Xtalpi is looking to deepen cooperation with several multinational pharmaceutical companies and further expand collaboration:
• If the current cooperation projects yield preliminary successful results (such as discovering new molecules that enter clinical stages), partners may increase their investments and even sign more project agreements. This will lead to a snowball effect of ( Welcome to PowerPoint) sustained interest, and other pharmaceutical companies that have not yet collaborated may also seek to engage with Xtalpi. As a Hong Kong-listed company, Xtalpi's brand recognition and financial strength will also facilitate the undertaking of larger-scale outsourced R&D projects.
• In materials and other industries, Xtalpi has the opportunity to replicate its cooperation model in the pharmaceutical field. For example, collaborating with new energy companies to develop more efficient battery materials, or working with agricultural chemical companies to develop new pesticide molecules. Successful cases will open up new revenue sources and also diversify reliance on the pharmaceutical industry.
• Additionally, the funds recently raised by the company can be used to expand the commercial team and global layout, such as strengthening business development in the European and American markets. Xtalpi has R&D centers in places like Boston, which facilitates its connection to the sea ( [Tencent-Backed AI Drug Discovery Startup Xtalpi Files For Hong Kong IPO](https://bestofai.com/article/tencent-backed-ai-drug-discovery-startup-xtalpi-files-for-hong-kong-ipo#:~:text=D Despite widening net losses, Xtalpi's overseas execution capability will determine whether the company can secure more multinational cooperation orders.

The risks to note are: the uncertainty of the transformation of cooperative results. Milestone payments from large pharmaceutical companies are usually tied to R&D progress; if the project does not meet expectations, subsequent payments may be missed. In addition, some large companies may reduce their reliance on external platforms in the long term. Therefore, Xtalpi must continuously prove its irreplaceable value, transforming cooperative relationships into long-term strategic partnerships rather than one-time purchases. At the same time, the company needs to manage resource allocation well when multiple projects are running in parallel to avoid compromising delivery quality. Overall, Xtalpi has established a broad cooperative ecosystem that includes pharmaceutical companies, material enterprises, and research institutions, which is both the cornerstone of current revenue and a springboard for future growth.

Leadership Team Background and Execution Capability

The leadership team of Xtalpi is composed of experienced scientists and entrepreneurs, whose backgrounds and execution capabilities are one of the important factors for the company's success. The company was co-founded by three PhDs in quantum physics, all of whom were trained or conducted postdoctoral research at the Massachusetts Institute of Technology (MIT), representing typical academic-to-entrepreneurship talent. Specifically:

• Shuhao Wen – Co-founder, Chairman of the Board. PhD in Physics from the Chinese Academy of Sciences, conducted postdoctoral research at the University of California and MIT, and served as a visiting professor at Zhejiang University. He has extensive research achievements in computational physics and quantum chemistry, having published dozens of papers that have been cited over 2,100 times. Dr. Wen was selected as one of Fortune's 40 Under 40 business elites in China, reflecting his recognition in business leadership. He is responsible for the company's strategic direction, bridging technology and business.

• Jian Ma – Co-founder, Chief Executive Officer (CEO). PhD in Physics from Zhejiang University, postdoctoral researcher at MIT, specializing in quantum computing and numerical simulation, with multiple papers published in top journals. Jian Ma 曾 ( [Welcome to PowerPoint](https://www.stifel.com/newsletters/investmentbanking/bal/marketing/healthcare/biopharma_timopler/biopharmamarketupdate_06.17.2024.pdf#:~:text=companies%20%E2%80%94%20is%20a%20welcome, The title of "Eli Lilly" is held by the CEO, who leads the company's daily operations and global team management, advancing scientific research and innovation into commercial implementation. Ma Jian is also recognized as a "high-level overseas talent" and a leading talent in Shenzhen, indicating his significant recognition in the domestic industry.

• Lai Lipeng – Co-founder, Chief Innovation Officer (CIO). He holds dual bachelor's degrees in Physics and Mathematics from Peking University, a Ph.D. in Physics from the University of Chicago, and a postdoctoral fellowship at MIT. Dr. Lai has a deep understanding of the development and operation of pharmaceutical-related software and has published multiple papers. He is responsible for the company's technological innovation and new product development and is a key figure in promoting multidisciplinary technology integration.

The academic background of the leadership team ensures that XtalPi possesses solid research capabilities and keen insights into cutting-edge technologies. At the same time, they have demonstrated outstanding execution and resource integration capabilities during the entrepreneurial process:

• Since its establishment in 2015, the company has rapidly expanded from three founders to establishing research and development centers in four locations across China and the United States (Shenzhen, Shanghai, Beijing, Boston), with hundreds of employees. This rapid expansion is attributed to the management's execution capabilities in talent recruitment and team building.
• The founding team has successfully attracted substantial financing support. By the time of the IPO, XtalPi had raised approximately 7.32 ( Welcome to PowerPoint) from globally renowned technology and investment institutions such as Sequoia Capital and SoftBank. The ability to attract such a large amount of top-tier capital reflects the management's exceptional ability to communicate the company's vision and execute plans to investors.
• The team has successfully delivered projects and expanded customer relationships ( About XtalPi) from scratch, demonstrating the leadership's ability to persuade large clients with strict requirements and deliver valuable ( About XtalPi) reputation as planned
• Since its inception, XtalPi has received numerous industry honors, such as being selected as one of the "Top 50 High-Tech Companies in China" by MIT Technology Review in 2020. These awards reflect the industry's recognition of the company's innovation capabilities and growth quality, and indirectly demonstrate the effective leadership of the management team.

In terms of execution challenges, XtalPi's leadership team is relatively young and has limited experience in managing large multinational enterprises, which may pose challenges for future growth. However, the advisory team includes seasoned professionals (including members appointed by investors). Overall, XtalPi's leadership team combines top-notch research backgrounds with pragmatic entrepreneurial execution capabilities. Their past performance (rapid financing, IPO, securing major partnerships) has proven their ability to drive the company forward. In the future, while maintaining leadership, they aim to significantly enhance commercial revenue and navigate the international regulatory and market environment. Given the team's track record of converting research success into business opportunities, it can be expected that

Financial Performance and Future Growth Potential

XtalPi Technology is currently still in the investment phase, with significant losses. According to the company's disclosed data, revenues from 2021 to 2023 were approximately RMB 63 million, RMB 133 million, and RMB 174 million, respectively, showing a rapid growth trend In particular, revenue grew by approximately 96% year-on-year in 2022, and continued to grow by about 27% in 2023 on a high base. This growth was mainly driven by the expansion of AI research and development services: in the first half of 2023, the company's revenue reached 80 million RMB, an increase of 86% compared to the same period last year. As of mid-2024, HKD 1.1 billion (approximately USD 27 million). It can be seen that the company has grown from a scale of tens of millions a few years ago to nearly 200 million RMB in annual revenue today, validating the feasibility of its business model and market demand. Correspondingly, the company continues to incur significant losses. Due to R&D investment, talent costs, and laboratory construction Sino Biopharmaceutical and CK Life's net loss in the first half of the year reached 891 million yuan (approximately 125.7 million USD). This loss amount is more than ten times the revenue during the same period, indicating that the company is increasing its investment to seize technological advantages and market opportunities. It is expected to continue incurring losses in the hundreds of millions in the fiscal year 2024. According to market data, XtalPi's net loss in the last 12 months was approximately HKD 2.73 billion, with the loss scale even exceeding revenue by a hundred times (possibly including one-time listing-related expenses or equity incentive costs). Such a high loss rate puts pressure on financial performance in the short term but is a typical characteristic of AI R&D companies. The company's cash flow and capital reserves are relatively ample after the IPO and subsequent financing. In 2024, the company raised approximately HKD 989 million through its IPO, and in early 2025, it raised HKD 1.13 billion through a rights issue. These funds provide the company with several years of operational and R&D funding, allowing it to focus on technology development and market expansion without the pressure of profitability. In the coming years, with the advancement of projects such as the previously mentioned collaboration with Eli Lilly, if milestones are achieved, significant capital injection (totaling hundreds of millions) may occur. Additionally, once new drugs developed in collaboration enter late-stage clinical trials or even the market, XtalPi may receive milestone bonuses or revenue sharing, which will significantly enhance revenue scale and gross profit levels.

• Future Revenue Growth Potential: XtalPi's growth potential comes from the following aspects:

• Business Expansion: The company plans to expand its customer base and project numbers, including horizontally expanding paid collaborations in new materials, agriculture, and other industries, as well as vertically conducting more and deeper projects with existing pharmaceutical clients. Given the overall high growth expectations for the AI pharmaceutical market (with a compound annual growth rate of approximately 29% from 2024 to 2030), XtalPi, as a leading player, is expected to at least maintain growth rates comparable to the market. If it can stand out in the competition, there is potential for growth to exceed the industry average.

• Monetization of Major Collaborations Providing follow-up flexibility for future revenue (such as Eli Lilly's $250 million agreement): When projects reach the expected milestone payments, this type of significant milestone revenue may lead to a jump in revenue in a given year. At the same time, potential new agreements with other major pharmaceutical companies can also bring one-time technical scale.

• Service Productization: As the platform matures, XtalPi has the opportunity to package some of its R&D capabilities into standardized products (allowing customers to directly use its AI model platform) or sell laboratory automation equipment and systems (such as XtalPi's independently developed intelligent experimental stations). This strategy is similar to Schrödinger's software licensing business, which has high and sustainable gross margins. If XtalPi moves away from a purely manual service customization model, once productization is successful, revenue is expected to double and profit margins to improve.

• Harvesting Incubated Projects: The new drug projects incubated and invested in by XtalPi The new drug launched by Biopharmaceutical and CK Life Sciences may be acquired at a high price by a large pharmaceutical company, and XtalPi, as a shareholder/technology holder, can obtain substantial returns. Although the timing of this uncertain income is difficult to predict, it is a typical "Black Swan" positive event that brings a leap in financial performance.

• Policy Support and Market Expansion: As one of the first 18C specialized and innovative technology listed companies in Hong Kong, XtalPi is expected to benefit from the policy support for science and technology enterprises in China and Hong Kong, including research subsidies and tax incentives. These favorable conditions can reduce cost burdens and increase net income. In terms of overseas markets, if XtalPi actively develops clients in Europe and the United States to obtain income in USD/EUR, it can also mitigate risks.

• Risk Factor Analysis: In the long term, investors need to consider:

• Technical and R&D Risks: The success rate of AI drug discovery has not been validated on a large scale. If XtalPi's technology does not significantly outperform traditional R&D methods in actual projects, clients may reduce renewals or collaborations. This is a common risk across the entire AI pharmaceutical industry—( About XtalPi). XtalPi has invested heavily in R&D, but technological breakthroughs are uncertain and cannot guarantee commercially viable results.

• Long Commercialization Cycle: The pharmaceutical R&D process itself is lengthy. It typically takes 5-10 years from discovering candidates to drug market launch. Even if AI accelerates the early stages, subsequent clinical trials and approval processes still require time. This means that even if many of XtalPi's collaborative projects successfully enter clinical trials, their substantial contribution to the company's revenue may not materialize for several years. During this period, the company needs to continue investing without quickly seeing terminal results, testing its financial strength and management patience.

• ( Artificial Intelligence In Drug Discovery Market Report, 2030) There are numerous competitors in the pharmaceutical field, with dozens of startups and internal teams from large pharmaceutical companies competing to develop similar technologies. Although XtalPi has a first-mover advantage domestically, internationally, companies like Recursion, Exscientia, and Insilico ( About XtalPi) have already achieved significant success (for example, AI-designed drugs achieving breakthrough efficacy), potentially leading in market promotion and customer acquisition. XtalPi needs to maintain its technological and business model leadership to prevent clients from being taken by competitors. Additionally, large tech companies are also venturing into the biopharmaceutical AI field (such as Google's DeepMind in protein structure and Microsoft's investment in BioGPT), and the potential disruptive power cannot be ignored.

• Talent and Team Risks: Talent in the interdisciplinary field of AI research is extremely scarce, and XtalPi's business expansion has a pressing demand for top algorithm scientists, medicinal chemistry experts, and biologists. As the company grows, how to continuously attract and retain talent in both China and the U.S. is a significant challenge Competitors and tech giants may poach talent with high salaries, so the company needs to establish a good corporate culture and incentive mechanisms. The loss of any key R&D talent could slow project progress.

• Regulatory and Ethical Risks: AI applications in medicine are under the scrutiny of regulatory agencies. In the future, countries may introduce regulatory guidelines for the application of AI models in the medical field, requiring higher transparency and validation, which could increase compliance costs. Data privacy and security are also concerns; XtalPi needs to ensure that research data (especially cross-border data) is used in compliance with the law. Additionally, if the molecules recommended by AI have unexpected issues (such as overlooking certain toxicity risks), it also involves ethical responsibilities, necessitating the establishment of prudent internal review processes by the company. ( Sidley Represents XtalPi Holdings Limited in its Placing of New Shares | News | Sidley Austin LLP) **XtalPi spans both China and the U.S., and the current geopolitical tensions may bring uncertainties. For example, the U.S. may impose export controls on Chinese high-tech companies or restrict American companies from cooperating with them, which could affect XtalPi's operations in the U.S. or its collaborations with American companies. On the financial side, the risk of decoupling between the Chinese and U.S. capital markets is also worth noting; fortunately, the company has chosen to list in Hong Kong, where financing channels are relatively secure. The liquidity and investor structure of the Hong Kong market differ from those in the U.S., which has both advantages (local investors prefer concepts and have policy support) and disadvantages (the proportion of international institutional allocation is relatively small). The company needs to strengthen investor relations and expand international visibility to mitigate the valuation impact caused by regional market fluctuations.

• Financial Risks: In the short term, XtalPi is highly reliant on external financing to maintain operations. If changes in the market environment lead to financing difficulties, the company's high cash-burning model will be hard to sustain. Currently, the company has accumulated a considerable amount of funds through IPOs and placements, but in the long term, if it cannot achieve breakthrough growth in revenue or turn losses into profits, the stock price may remain under pressure, and further financing could dilute shareholder equity. In an environment of rising interest rates and inflation, investors' patience with cash-burning companies is decreasing, which poses significant pressure on XtalPi.

In summary, XtalPi's financial situation reflects the typical characteristics of high growth and high investment coexistence. Whether it can achieve a transformation from "high investment for potential" to "high output to realize value" will be key to the company's long-term investment value. Given the current industry trends and company resources, XtalPi is expected to achieve exponential revenue growth in the coming years through continuous technological leadership and business expansion However, in this process, the company needs to effectively manage various risks to ensure its financial health and sustainability, creating long-term returns for investors.

Comparison of Global Similar Investment Opportunities

The "AI + Pharmaceuticals/Materials" sector that JingTai Technology operates in has players participating globally. For investors, it is necessary to compare JingTai with similar overseas companies to understand its relative competitiveness and differences:

• Recursion Pharmaceuticals (NASDAQ: RXRX): [Scale and Positioning] An AI biotechnology company listed in the United States, with a market capitalization of approximately $1 to $1.5 billion. Recursion focuses on high-throughput cell imaging and phenotypic screening, building a unique platform containing vast biological experimental data, using machine learning to find new drug clues. Unlike JingTai, which emphasizes chemical simulation, Recursion follows a biological experiment-driven AI approach. [Cooperation and Progress] Recursion has signed collaborations with large pharmaceutical companies such as Roche and Bristol Myers Squibb, has multiple joint development pipelines, and received a strategic investment of $50 million from NVIDIA to build a biological model supercomputing platform. It is also advancing new drug projects in various disease areas, with some candidate drugs entering clinical phases I/II. In comparison, Recursion entered the clinical stage earlier, but its AI mainly focuses on discovering new targets and mechanisms of action, and is not as comprehensive as JingTai in chemical molecular optimization. [Market Performance] Recursion's stock price fell after its IPO in 2021, but surged in 2023 due to NVIDIA's investment. Its current valuation is comparable to JingTai. However, Recursion's revenue mainly comes from milestone payments from collaborations with pharmaceutical companies, with approximately <$20M in revenue in 2023, and no product revenue yet. Investing in Recursion is betting on its vast biological data and AI to find "treasures in the black box." JingTai and Recursion serve as contrasts: one focuses on biological big data, while the other emphasizes physical simulation, each with its advantages.

• Schrödinger (NASDAQ: SDGR): [Scale and Positioning] A well-established computational chemistry company founded in 1990, with a market capitalization of approximately $2 billion, close to JingTai. Schrödinger's core business is molecular simulation software, which is widely used in drug and material design, generating stable annual revenues of hundreds of millions of dollars. At the same time, the company also uses its platform to participate in joint drug development, receiving milestone payments and revenue sharing. [Technical Comparison] Schrödinger has strong capabilities in physical modeling and computational accuracy, with its software validated over decades. However, in recent years, Schrödinger has gradually increased its investment in AI (machine learning), while JingTai has integrated AI from the start, potentially exploring more cutting-edge algorithms. Both emphasize the combination of computation and experimentation: Schrödinger collaborates with multiple pharmaceutical companies (such as BMS) to advance new drugs and claims progress in its self-developed pipeline; JingTai has its own laboratories and more diverse application scenarios. [Market Performance] Schrödinger has a mature business model, with software business revenue expected to exceed $200 million in 2023, with high gross margins, but is in a loss state due to investments in self-developed new drugs Its stock price once soared to over $100 per share in 2021 due to the hype around AI drug development, but later fell back to around $30. For investors, Schrödinger offers two types of value: pay-as-you-go software profits and options for drug success. Jingdai currently has insignificant software revenue and relies more on service contracts, with short-term commercial maturity not matching that of Schrödinger, but it has greater imaginative potential (especially in materials and new businesses). If one is optimistic about computation-driven innovation and desires stable cash flow, Schrödinger is the benchmark; if one is optimistic about more aggressive AI innovation and is willing to wait, then Jingdai has different attractions.

• Exscientia (NASDAQ: EXAI):【Scale and Positioning】A UK-based AI drug development company, listed on NASDAQ in 2021, currently valued at approximately $700 million to $1 billion (significantly reduced since its IPO). Exscientia specializes in AI-generated and optimized small molecules, proposing a collaborative design concept of “AI doctors + human doctors,” and has successfully advanced several AI-designed candidates into clinical trials (covering areas such as immuno-oncology).【Business Model】Exscientia has cooperation agreements with several pharmaceutical companies (such as Sanofi and Roche) and also has its own research pipeline, adopting a dual-track approach of “collaborative R&D + independent R&D.” It has also established its own laboratory facilities to ensure a closed loop from target identification to preclinical testing. This is similar to Jingdai's model, both aiming to build end-to-end capabilities.【Market Performance】Exscientia's stock price has significantly declined since its IPO, with investors concerned about its cash burn rate and clinical progress. However, the company is well-funded and has a rich project reserve. Currently, Exscientia's revenue mainly comes from collaboration income, amounting to only tens of millions of dollars annually, far from profitability. Compared to Jingdai, Exscientia has already demonstrated the feasibility of AI-designed molecules entering human trials, which is a milestone in the industry; while Jingdai has not publicly disclosed any of its molecules entering clinical trials. However, Jingdai's advantage lies in a larger market space (involving materials, etc.), backing from the Chinese market, and a diverse customer base, unlike Exscientia, which relies more on a few Western pharmaceutical companies. Investors' choice between the two depends on which aspect they value more: speed of technology implementation (Exscientia is a step ahead) or business diversification (Jingdai is broader).

• Insilico Medicine (not listed, preparing for IPO):【Background】Insilico is an AI drug company operating in both the US and Hong Kong, founded by Russian scientists, currently headquartered in Hong Kong Science Park. The company is one of Jingdai's most direct competitors in China and globally. Insilico's advantages lie in generative AI (its molecular design engine Chemistry42 is reportedly very powerful) and biological AI (such as PandaOmics for target discovery).【Achievements】Insilico was the first to announce the world's first AI-designed drug entering clinical trials (ISMC-1 for idiopathic pulmonary fibrosis), which is currently undergoing Phase II trials This has set a benchmark for the industry. Insilico has also collaborated with several pharmaceutical companies, such as Eli Lilly and Sanofi, and has received equity investment from Takeda Pharmaceutical. Its recent financing round reportedly valued the company at over $1 billion. Both Jingdai and Insilico operate in a dual-base model in China and the U.S., with similar business models, but Jingdai chose to enter the capital market earlier, securing more financial ammunition. If Insilico goes public in the future, investors will directly compare the progress of their pipelines and customer acquisition capabilities. Currently, Insilico is slightly ahead in self-developed pipelines, while Jingdai has expanded in customer breadth and materials field, each with its own strengths.

• BenevolentAI (AMS: BAI): [Lesson] This is a case to be wary of. BenevolentAI, a unicorn, has significant collaborations with AstraZeneca and was once regarded as a star company. It was valued at up to €1.5 billion when it went public via SPAC in Amsterdam in 2022, but due to the failure of its core drug in clinical trials and massive cash burn, its stock price plummeted over 90% in 2023, with a market value dropping to less than €200 million, leading to operational difficulties. This underscores the high risks of AI drug development: even if the technological concept is good, if it fails to translate into successful drugs, the commercial value may fall short of expectations. Jingdai currently does not bear the burden of failed clinical trials for self-developed drugs, but BenevolentAI's experience reminds investors to reasonably assess risks for AI research companies that do not yet have mature products, and not to invest solely based on conceptual hype. Jingdai needs to solidify investor confidence with tangible milestone progress (such as advancing collaborative projects to the clinical stage) to avoid repeating BenevolentAI's over-promising but under-delivering pitfalls.

• Comprehensive Comparison: Jingdai Technology has its own characteristics in the global competitive landscape: based in China and the U.S., spanning drugs and materials, primarily platform services, and empowered by multiple collaborations. In contrast, some of its American and European counterparts have already gained an edge in advancing drug pipelines, while others have stable software revenue support. However, Jingdai's Chinese background also brings advantages—huge domestic market demand, policy support, and cost advantages (the cost of R&D personnel is relatively lower than in the West), which helps it run its technology and service models at a lower cost. At the same time, Jingdai has established financing channels through its listing in Hong Kong, ensuring ample financial ammunition, while some overseas peers are still seeking financing or IPO windows.

For investors, if they are optimistic about the AI + research long-cycle track, both Jingdai and its global peers are worth paying attention to: they can diversify the risk of a single company's technological route failure through a portfolio investment Specifically regarding JingTai, its large market capitalization reflects the market's recognition of its broad vision, but it also means that the current valuation includes a significant amount of expectations for future success. In contrast, some overseas peers have become relatively "cheap" after experiencing valuation corrections, which may provide greater upside potential. However, JingTai's unique selling points (materials + AI, deep cultivation of the Chinese market, etc.) are difficult for other companies to replicate. If these selling points translate into performance, JingTai's long-term returns could be more substantial. Therefore, on a global scale, JingTai Technology represents an AI innovation company that combines diversified business layout and regional advantages, with the opportunity to secure a place in long-term competition. However, investors should also concurrently track the progress of international peers to assess whether JingTai maintains its lead or if there are changes in the industry landscape, allowing for timely adjustments to their judgment of its competitive position.

Conclusion

As a technology innovation company integrating artificial intelligence, quantum physics, and automated experimentation, JingTai Technology has pioneered a unique business model in the fields of biopharmaceuticals and new materials research and development. The company boasts an excellent founding team of scientists, strong financial backing, and a rich global collaboration network, demonstrating strong potential in technological strength and market expansion. At the industry level, AI-enabled research is seen as a major trend that could disrupt drug and material innovation in the next decade, and JingTai is at the forefront of this wave. Its collaborations with several top pharmaceutical companies and its continuously growing revenue prove the feasibility of its business model and its value creation capability.

From a long-term investment perspective, JingTai Technology has the potential to become a high-growth, high-return company. If the company's AI platform can continue to iterate and assist partners in successfully developing blockbuster products, JingTai, as the behind-the-scenes contributor, will reap substantial economic returns and industry prestige, with its market capitalization expected to rise accordingly. However, it is equally important to note that high technical risks and long R&D cycles mean that the uncertainty of the company's future performance is also high. In the AI pharmaceutical sector, success often requires a long period of accumulation, during which market sentiment and capital support may fluctuate. The dramatic volatility of JingTai's stock price since its listing reflects this uncertainty.

Therefore, investors interested in JingTai Technology should maintain a long-term perspective and good risk awareness. In the short term, the company may continue to be in the investment phase, with breakeven still requiring time, and the stock price may fluctuate with industry news. In terms of investment allocation, positions should be determined based on individual risk tolerance. Overall, JingTai Technology represents one of the leading forces in the "AI + technology research and development" field in China and even globally: its technological competitiveness and business expansion capabilities position it to play a key role in the future landscape of pharmaceutical and material innovation. If you are optimistic about the paradigm shift that artificial intelligence brings to pharmaceuticals and materials and are willing to bear the uncertainties of emerging industries, then JingTai Technology deserves to be considered a potential stock in your long-term investment portfolio. However, it is essential to continuously monitor the company's R&D progress, the realization of collaborative results, and changes in the competitive landscape, as only when these factors validate optimistic expectations can JingTai's long-term investment value truly be realized Source:

• JingTai Technology prospectus and announcements, financial news reports
• Industry research and authoritative media reports (Forbes, Endpoints, etc.)
• Company official website information and investment bank analysis summaries
• Market data and comparative information within the industry