--- title: "What does AI Pharmaceuticals' first profit tell us?" type: "News" locale: "en" url: "https://longbridge.com/en/news/278653999.md" description: "The AI pharmaceutical industry is expected to explode in 2024-2025, with XTALPI projected to achieve profitability in 2025, increasing revenue to 780 million yuan, marking its first turnaround to profit and becoming the first profitable AI application stock in the Hong Kong market. Global pharmaceutical companies are shifting their attitude towards AI to focus on foundational capabilities, but industry differentiation is intensifying, with some established companies facing sluggish performance. The challenges of AI pharmaceuticals lie in controlling costs and sustaining revenue, making XTALPI's profit model particularly important at this stage" datetime: "2026-03-11T03:45:45.000Z" locales: - [zh-CN](https://longbridge.com/zh-CN/news/278653999.md) - [en](https://longbridge.com/en/news/278653999.md) - [zh-HK](https://longbridge.com/zh-HK/news/278653999.md) --- > Supported Languages: [简体中文](https://longbridge.com/zh-CN/news/278653999.md) | [繁體中文](https://longbridge.com/zh-HK/news/278653999.md) # What does AI Pharmaceuticals' first profit tell us? In 2024-2025, AI will enter a true "explosion year." However, this explosion is not romantic: the iteration speed of large models is accelerating, the investment in computing power and talent is becoming heavier, and the industry is rapidly shifting from "who can build models better" to "who can calculate the accounts better." For almost all AI companies represented by large models, the competitive decisive factors are becoming clearer—first, whether costs can be controlled, and second, whether revenue can continue to grow. When AI enters hard technology fields such as pharmaceuticals and materials, these two challenges become even more difficult: data is scarcer, experiments are more expensive, validation takes longer, and failures are more common. Therefore, AI in pharmaceuticals has long been regarded as one of the tracks with "huge imaginative space but the hardest commercialization." Against this backdrop, the domestic AI pharmaceutical platform company XTALPI announced a positive profit forecast for 2025: Revenue will increase to no less than 780 million yuan, with a year-on-year growth rate of at least 193%; net profit after tax and profit attributable to equity holders of the company will be no less than 100 million yuan, turning losses into profits year-on-year, achieving annual profitability for the first time, and becoming the first AI application stock in Hong Kong to achieve profitability. At the same time, global pharmaceutical companies' attitudes towards AI have shifted from "decorative innovation" to "foundational investment": Sanofi has declared "All in AI," Eli Lilly has partnered with NVIDIA to invest $1 billion to co-build an AI laboratory, and AstraZeneca has obtained authorization for innovative long-acting peptide drugs developed with AI from CSPC Pharmaceutical Group for up to $18.5 billion. The industry consensus is increasingly unified: AI is no longer just an embellishment but a foundational capability to avoid being eliminated. However, the other side of pharmaceutical companies fully embracing AI is the ongoing differentiation in the AI pharmaceutical industry. The established overseas "SaaS" player Schrodinger has fallen into a dual slump in performance and stock price due to its transformation into independent research and development; Recursion, a representative of self-developed pipelines, has seen its losses widen and has been liquidated by NVIDIA. These cases point to a reality: the difficulty of AI pharmaceuticals has never been about "whether it can be done," but rather "whether it can continuously generate revenue under controllable costs" and "when it can truly make money." In contrast, XTALPI's profitability is therefore more worthy of discussion: why does it have an advantage in the stage of the AI arms race that tests cost and revenue models the most? ## Route Dispute: Build "Industrial AI Infrastructure" or Gamble on Pipelines While other AI pharmaceutical companies are still discussing when their pipelines can pass the ultimate "acceptance" of Phase III clinical trials, XTALPI has already pocketed the money earned from AI pharmaceuticals, proving its ability to generate profits from AI pharmaceuticals earlier than the pipelines of AI research and development. Although XTALPI's business model appears complex, its core essence can be summarized in one sentence: it acts more as an "enabler" of industrial AI infrastructure rather than a "gambler" personally engaging in drug development. XTALPI does not focus on self-developed drug pipelines but empowers partners' research and development through an "AI + automated research and development platform," and generates revenue through upfront payments, milestone payments, technical service fees, platform subscription fees, and a share of certain projects Therefore, it is difficult to summarize it with a single label, but it is certainly not a traditional CRO, nor is it pure SaaS, and definitely not a typical self-developed biotech. Traditional CROs are human-centric and do not develop as many algorithms and hardware as XTALPI does. If we trace back to the company's past earnings calls, we will see that the long-term goal of the company is to achieve super artificial intelligence in vertical industries. The market "does not understand," which essentially stems from its business model lacking a strictly comparable overseas sample. However, many great companies, including Google, faced a similar situation where most people "did not understand" in their early stages. It is well known that the core of artificial intelligence is still proprietary data. The purpose of hardware and business is directly aimed at data, and the goal of data is to achieve the "industry large model" that XTALPI desires. If XTALPI only talks about technology, we would actually be concerned. The management is also very focused on commercial implementation and financial revenue, positioning XTALPI as a "platform" from the very beginning, and has found a uniquely advantageous "platform-type" business strategy in its development, ultimately becoming the first company to make money in AI pharmaceuticals. On one hand, it deeply participates in innovative research and development with platform capabilities, and sharing data also provides opportunities to share the long-term upside of the innovation pipeline; on the other hand, it can obtain sustainable cash flow and relatively controllable risks. Its barriers come from a reusable combination of system capabilities—quantum physics computing, AI design, robot experiment-driven data closed loops, and a multi-modal R&D platform covering small and large molecules—this is not simply "piling people + piling computing power" that can be replicated. More critically, it avoids the most fatal uncertainty for AI pharmaceutical companies: the high clinical risk and cash flow black hole of self-developed pipelines. XTALPI does not rely on "betting on phase III clinical trials" to prove its value, but continuously validates its platform capabilities through orders, upfront payments, milestones, and revenue sharing. At the same time, platform-type businesses have scale effects: as models and processes continue to iterate and data continues to accumulate, XTALPI can strengthen its leading advantage through extensive cooperation. AI empowerment in scientific research and industrial R&D is a certain trend, with a huge incremental market, and platforms like XTALPI, which have been validated and selected by 17 of the top 20 global pharmaceutical companies, have a greater first-mover advantage and lower customer acquisition costs. AI empowerment in pharmaceutical R&D can enhance research efficiency, but the core is whether it can form an effective rather than money-burning business model. Although all roads lead to Rome, the choices of different companies will still lead to different performances. The AI pharmaceutical industry has a structural problem: if it follows the "self-developed pipeline" route, the real validation often has to wait until the later stages of clinical trials, especially phase III clinical trials, which are larger in scale and have clearer statistical significance. Thus, a script often appears in the market: early on, companies rely on "model narratives + pipeline quantity," targeting high-potential indications, leading to sky-high valuations; in the mid-term, pipelines begin to enter clinical trials, cash flow pressure increases, yet the validation of pipelines takes a long time, and if financing does not keep up, they face difficult trade-offs; once a key clinical trial fails, stock prices and fundamentals collapse simultaneously. There have been many typical comparisons overseas in recent years Schrodinger, with stable software service revenue, is a pioneer in the overseas AI pharmaceutical field. In its early years, it focused on drug development software services, supported by stable subscription revenue for steady corporate growth, maintaining a robust stock price over the long term. However, after increasing investment in self-developed drugs and transitioning to an "AI + Biotech" model, its performance has faced continuous fluctuations, with significant increases in R&D investment but delayed clinical results, leading to long-term pressure on both performance and stock price. Recently, Recursion, which was fully divested by NVIDIA, is also a typical representative of AI biotech, showcasing the drawbacks of the self-developed pipeline model. After completing its merger in 2024, Recursion cut a batch of clinical phase 2 and earlier pipelines with poor data, and in 2025, it further eliminated four pipelines, retaining relatively earlier-stage pipelines, with its revenue primarily coming from service income as an AI R&D platform. The adjustments in Recursion's pipeline indicate that the self-developed drug pipeline model is difficult to validate for its commercial value in the short term. In some therapeutic areas, even if the pipeline is destined to fail to reach the market, it may still successfully navigate through phases 1 and 2 clinical trials. Before the results of the larger, more diverse phase 3 clinical trials are revealed, it is essentially a high-risk gamble. Ultimately, even NVIDIA chose to exit completely, reflecting the market's risk pricing of the "self-developed big gamble." ## Moving the "Validation Node" Forward: Why the XTALPI Model More Easily Runs Through Financial Models In contrast, the key difference in the XTALPI model is that it moves the "validation node" forward. Under the collaboration model, XTALPI's value validation does not need to wait until phase 3—signing a contract and receiving the upfront payment means that the client is willing to pay for the platform's capabilities (rather than a small PoC—proof of concept). Advancing the pipelines it participates in to clinical milestones means that platform delivery drives real R&D progress. If there are subsequent sales or equity sharing, it provides more long-term upside potential. More importantly, the cost structure of milestone payments is more favorable. Once a project enters clinical trials, the major risks and investments of subsequent clinical trials are primarily borne by the pharmaceutical company; the milestone revenue obtained by XTALPI often does not require proportionate additional costs, thus being closer to high-margin incremental revenue. This allows XTALPI to excel in both major challenges of "controlling costs and increasing revenue": on the cost side, automation and platformization bring economies of scale, reducing marginal costs; on the revenue side, upfront payments/milestones allow for faster and more sustainable revenue realization. Currently, the company has officially announced that its collaborative pipelines have reached dozens, with clients including global leading pharmaceutical companies like Eli Lilly and innovative biotech firms, covering a rich pipeline from cancer and metabolic diseases to neurological and rare diseases. The larger the pipeline scale, the deeper the data and algorithm accumulation, the stronger the platform becomes, forming a positive cycle of "getting cheaper, getting more accurate, and being able to sign larger contracts." Additionally, XTALPI is favored by "small but beautiful" innovative biotech companies. If partners choose to license pipelines at an earlier stage, the rhythm of XTALPI's revenue sharing may further advance, thereby raising the revenue imagination space If "AI empowers scientific research and lands in industries" is an inevitable trend, then XTALPI's positioning is more like the infrastructure for industrial AI applications: it can not only undertake a large market but also isolate risks well, keeping certain income on its own balance sheet. Therefore, in the past year, there have been frequent national-level investigations and inspections of XTALPI, indirectly indicating two facts: the high value of the track and that leading enterprises are forming competitive advantages. ## Where the Moat Comes From: Not Models, but "Data Loop + Automated Production Capacity" In AI applications, what is truly scarce is not "having models," but "being able to continuously produce high-quality data and turn that data into reusable assets." For XTALPI to become an industrial enabler, the key lies in whether its capabilities are strong enough and whether its barriers are deep enough. XTALPI's moat concept is to create a new generation of infrastructure with algorithms, automation, and data, which is the well-known "quantum physics + AI + robotics" full-stack technology loop. This is a heavier, more difficult but more effective combination that can solve two issues: the automated experimental platform addresses the pain points of data accuracy and data silos in the pharmaceutical field; data feedback drives AI algorithm iteration, forming a loop of "delivery - data - model - re-delivery," allowing the model to continuously strengthen. As we know, the three core elements of AI are computing power, algorithms, and data. The computing power level is mainly provided by upstream sources, making it difficult to create a gap. The key factor that determines the moat, or creates a gap, is "data + algorithms." This is also where the "landing difficulties" of AI in pharmaceuticals lie: many companies' models appear strong but lack a sustainable supply of high-quality experimental data and closed-loop iteration mechanisms, ultimately remaining at the Proof of Concept (PoC) stage. A major challenge faced by AI in scientific research is how to obtain sufficient high-quality data to train effective models. Even the popular AlphaFold is still limited by insufficient complex structure data in drug development scenarios, affecting practical application results. A report in Nature in March 2025 also mentioned that AlphaFold faces the problem of drug data shortages, which directly impacts model performance and hinders the advancement of this tool in relevant scenarios. Therefore, an excellent AI pharmaceutical company must possess two key characteristics: the ability to efficiently acquire or independently generate high-quality data, and the capability to complete validation based on a mature experimental platform, continuously achieving algorithm iteration loops. This is precisely the core highlight of XTALPI's "quantum physics + AI + robotics" model. Relying on its self-built robotic laboratory research platform, it addresses data issues from the source and continuously transforms its business into data assets and algorithm assets, feeding back into model optimization, forming a positive cycle of "business development - data accumulation - algorithm iteration - service upgrade," establishing a differentiated advantage that is difficult to replicate. ## Can Growth Be Sustained? Of course, after delivering a report on turning losses into profits, the market is most concerned about whether XTALPI can continue to successfully operate the light asset model of "technical services + joint research and development," bringing sustained high growth in performance? Currently, time is still needed for verification, but from the current industry practices and cooperation progress, the company has at least provided positive answers in five core dimensions: First, the continuous extension capability of New Modality. From the perspective of industry development trends, the research and development bottleneck of single drug modalities is becoming increasingly prominent, and multi-modal drug development has become an important direction for breaking through the difficulties of drug development and expanding treatment boundaries. For AI pharmaceutical companies, overcoming high-difficulty targets that traditional research and development finds hard to break through through more diverse drug development paths is key to validating technology and algorithm capabilities. Currently, XtalPi has established integrated research and development capabilities covering various types of drugs, including small molecules and antibodies, and is accelerating its evolution. It has already promoted the implementation of platforms such as molecular glue, mRNA, peptides, and siRNA, fully demonstrating its deep technical accumulation and innovative strength, which correspond to new market opportunities and blue ocean spaces. Among them, siRNA, as a core subfield of small nucleic acid drugs, is experiencing a historic leap from "niche rare disease drugs" to "mainstream chronic disease treatment solutions," standing at the center of the global wave of innovative drugs, with enormous market growth potential. According to Frost & Sullivan data, the global small nucleic acid drug market size is expected to surge from USD 5.2 billion in 2024 to USD 20.6 billion in 2029. XtalPi's AI small nucleic acid design platform XtalSilence can design innovative siRNA molecules that significantly reduce off-target risks through AI iteration, precisely aligning with industry technical needs, and is expected to seize opportunities in this rapidly growing track, sharing market dividends based on technological advantages. The potential of molecular glue should not be underestimated either. As a new generation of proximity-induced therapy, it, along with PROTAC, constitutes the core direction of protein degradation technology, representing the most imaginative new molecular field in the medium to long term, and is the key to unlocking the treasure of "undruggable targets," with clinical and commercial value fully validated in the blue ocean market. XtalPi leverages physical computation simulation, high-throughput screening, and proprietary predictive tools, combined with AI-driven robotic laboratories, to tackle the core technical challenges of molecular glue development, thereby building differentiated competitive advantages. While overcoming more challenging targets through more drug development paths, XtalPi can even conduct internal competitions targeting different target mechanisms and drug modalities for the same disease, sending the best-performing molecules into clinical trials. This is also one of its core advantages that distinguishes it from similar AI pharmaceutical companies, helping to continuously broaden technical boundaries and cooperation scenarios. Second, the ability to continuously secure large orders. From XtalPi's nearly USD 6 billion scaled package order with DoveTree to its ongoing deepening cooperation with global leading pharmaceutical companies like Eli Lilly totaling USD 600 million in two phases, continuously signing heavyweight projects proves that its platform value has been highly recognized in the international market, and its ability to land large orders is sustainable, no longer relying on single projects to drive growth. Third, efficient advancement and high realization of cooperation pipelines. After obtaining technological empowerment from XtalPi, partners have smoothly advanced related pipeline research and development, with milestone nodes densely landing For example, in the past year, its collaborative achievements have been remarkable: projects in partnership with IM Motors have smoothly entered the clinical stage; the AI+RNA new drug RTX-117 developed in collaboration with Xili Technology successfully completed its first patient dosing, marking the project's entry into a critical clinical validation phase; the gastric cancer targeted drug developed with Xige Biotechnology not only smoothly entered clinical trials but also received a Galen Award nomination, highlighting the clinical value and industry recognition of the pipeline; in addition, Laiman Biotechnology, with the technical support of XTALPI, achieved 100% complete remission of hematological tumors and lupus erythematosus with a CAR-T at a one-thousandth dose, and has recently successfully completed nearly 200 million yuan in financing. Multiple collaborative pipelines are simultaneously advancing to the clinical stage, which to some extent validates the real efficiency and core drug value of XTALPI's AI technology in empowering new drug development. Fourth, relying on the broad application space of AI, continuously expanding the second and third curves. XTALPI is not merely an AI pharmaceutical company; it has also expanded into incremental markets such as consumer goods, new materials, and agriculture. In particular, it has successfully entered the consumer health field, where its self-developed AI molecular development platform ID4 has successfully designed two innovative topical active ingredients for hair growth—small molecule Remeanagen™ and peptide AquaKine™. Testing data shows that subjects using these two AI-designed hair growth molecules can observe preliminary increases in hair follicle density as early as 14 days after use, indicating a rapid onset of effect. After 45 days of use, over 90% of subjects observed a visible increase in hair density; the average number of hairs lost by subjects decreased by 33% to 45%, and safety performance was excellent. Currently, both AI-designed hair growth molecules have successfully passed INCI (International Nomenclature of Cosmetic Ingredients) registration, and their combined formula has been completed with FDA cosmetic registration by the externally incubated consumer brand Groland, meaning that the product can now be sold through e-commerce channels. The market for hair growth products is undoubtedly significant. The product development strategy of "developing consumer goods to pharmaceutical standards" has already received official recognition from Eli Lilly's weight loss miracle drug Tirzepatide, becoming the biggest driver pushing pharmaceutical companies toward trillion-dollar valuations. XTALPI's successful leap from a pharmaceutical-grade R&D platform to consumer-grade active ingredients also brings new imaginative space and growth expectations; if this sector develops appropriately, it could bring explosive growth to XTALPI faster than drugs, warranting close attention. Fifth, relying on cash reserves to accelerate business and technological expansion. In the industrialization phase of AI, "cash flow and balance sheet" have become one of the core competitive advantages, representing the company's confidence to continuously invest in R&D, expand markets, and maintain competitive advantages. Since its listing, XTALPI has enriched its cash reserves through multiple rounds of placements and interest-free convertible bonds, with the zero-interest convertible bond maturing in 2027 amounting to HKD 2.264 billion, indirectly confirming its appeal among institutional investors. This also means that XTALPI has nearly 10 billion yuan in cash reserves, which can be used for rapid "buying" to fill technological gaps, expand its overseas client list, establish new business teams or joint ventures, including investing in potential upstream and downstream enterprises, transforming its super healthy cash reserves into higher profit margins and competitive barriers How to seize these opportunities? Compared to its peers, XTALPI, with ample cash on hand, has greater initiative. ## Summary Returning to the initial question: What does XTALPI's profitability tell us? It at least indicates that in the AI arms race, what is truly scarce is not the ability to "build models," but whether one can simultaneously solve two issues in real industries: reducing costs and increasing revenue. As a "new species," XTALPI integrates the imaginative space of AI in pharmaceuticals with the vast market of large models, while also avoiding the risks of self-developed pipelines through a platform empowerment model, moving value verification to the upfront payment and milestone stages. By continuously accumulating data and algorithms through a closed loop of "quantum physics + AI + robotics," it forms a scale effect that strengthens over time. How large XTALPI can grow in the future and how far this model can go still requires time to observe. However, it is undeniable that AI empowering scientific research and landing in industries is an inevitable trend. As the first profitable company among Hong Kong-listed AI application stocks, XTALPI's value is still on the rise. The core point is that XTALPI has successfully broken out of the "burning money without profitability" dilemma in the AI pharmaceutical industry, validating the commercial feasibility of AI technology empowering new drug development with tangible orders and pipeline progress. This is its long-term competitiveness and has explored a replicable and referenceable path for the entire industry ### Related Stocks - [XTALPI (02228.HK)](https://longbridge.com/en/quote/02228.HK.md) - [Minsheng Royal CSI Biotechnology Theme ETF (516930.CN)](https://longbridge.com/en/quote/516930.CN.md) - [China Merchants CSI Biotechnology Theme ETF (159849.CN)](https://longbridge.com/en/quote/159849.CN.md) - [E Fund CSI Biotechnology Theme ETF (159837.CN)](https://longbridge.com/en/quote/159837.CN.md) ## Related News & Research - [Clover Biopharmaceuticals Completes Enrollment in Phase 2 Trial for Combination Respiratory Vaccines](https://longbridge.com/en/news/280923090.md) - [08:33 ETDrugwatch Investigation Reveals Big Pharma Profits Continue to Outpace Billions in Legal Penalties](https://longbridge.com/en/news/281368752.md) - [The Next Big AI Winner Might Not Be a Tech Company](https://longbridge.com/en/news/281689441.md) - [Napster is Evolving in the AI Era](https://longbridge.com/en/news/281749361.md) - [MedPal AI Wins Strong Shareholder Backing at AGM as It Expands AI Health Platform](https://longbridge.com/en/news/281501359.md)