Tech giants shift AI drug discovery race from model development to closed-loop experimental competition

Recent moves by technology giants including Anthropic and ByteDance signal a strategic pivot in AI-driven drug discovery—from competing on model capabilities to building integrated closed-loop systems.

From General Tools to Laboratories: Building Systemic Infrastructure

Anthropic has established a wet laboratory in the San Francisco Bay Area, while ByteDance's AI drug discovery subsidiary, Anew Labs, has completed its first external financing round as an independent entity, raising $290 million. Eli Lilly is also among the players deepening their involvement.

According to interviews conducted by Shanghai Securities News with executives and industry insiders from multiple AI drug discovery listed companies, the competitive focus is shifting toward "who can build industry-wide systemic infrastructure" and successfully establish a complete "data–model–experiment–data" closed loop. Correspondingly, industry division of labor is evolving from individual efforts to ecosystem-based competition and collaboration.

Previously, tech giants focused on general-purpose large language models. In this new wave of AI drug discovery, many are emphasizing a transition from the digital world to the physical world, extending their capabilities into real experimental closed loops.

This assessment is echoed within the pharmaceutical industry. Ma Jian, co-founder and CEO of Jingtai Holdings, noted that the barrier to entry in AI drug discovery extends beyond models: pure computation and prediction in AIDD (AI-driven drug discovery) are insufficient.

From the tech giants' perspective, extending capabilities into drug discovery laboratories is a natural progression. Mao Hua, Senior Partner and Managing Director of Frost & Sullivan China, believes that as computing power becomes more abundant and large models iterate rapidly, functional differences between models are narrowing. For tech companies to build competitiveness in pharmaceuticals, they must secure access to automated experimental platforms, proprietary data assets, and pipeline IP.

Lai Caida, co-founder, Chairman, and CEO of Jitai Technology, pointed to Anthropic, OpenAI, and Nvidia as key players making such moves.

Demand-side dynamics are also shifting. According to securities analysts, large pharmaceutical companies are increasingly integrating AI into drug discovery, preclinical, and R&D processes. AI investment is transitioning from "experimentation" to a routine component of R&D budgets. The division of labor between supply and demand is becoming clearer: tech companies provide models and computing power, pharmaceutical companies contribute scenarios and pipelines, and experimental capabilities are a shared area for reinforcement.

However, the gap between capability upgrades and commercial realization remains. Zhang Chenyang, Senior Consulting Director of Frost & Sullivan China's Healthcare Division, noted that while large models are evolving from auxiliary prediction to autonomously generating hypotheses, designing molecules, and calling upon wet-lab experimental validation data, clinical success rates, R&D timelines, and return on investment still require time to verify.

From Model Competition to Closed-Loop Competition: The Data Flywheel as the True Moat

Compared to the previous wave of AI drug discovery, the driving forces have changed—and so have the evaluation criteria. Lai Caida stated that AI can propose hypotheses faster, but whether those hypotheses hold true still requires real experiments, animal studies, clinical trials, and long-term medical validation. Therefore, the most important factor going forward is not simply increasing computing power, but building infrastructure that connects AI with real biological experiments.

This shift in evaluation criteria is also redirecting competitive positioning toward the true bottleneck. Zhang Chenyang noted that high-quality data is only a surface-level bottleneck in AI drug discovery. The real bottleneck lies in establishing an experimental system that can continuously generate high-quality data and drive autonomous model iteration—particularly standardized wet-lab experiments, negative result data, and a "prediction–experiment–feedback" closed loop. Ma Jian added that what the AI drug discovery industry truly lacks is a "data flywheel plus an engineering-driven dry-wet lab closed loop."

Ren Feng, co-CEO and Chief Scientific Officer of Insilico Medicine, believes that the advantages of independent AI drug discovery companies are becoming increasingly clear: automated dry-wet lab closed loops and high-quality data, combined with validated AI innovation capabilities, continuously expanding areas of innovation, and deeply integrated large-model training platforms.

Multiple industry insiders believe that the key to competition is shifting from individual AI tools to the ability to form a complete "data–model–experiment–drug and clinical validation" closed loop. Whoever successfully runs this loop holds the "ticket" to the next stage.

From "Going It Alone" to "Ecosystem Competition and Collaboration"

How does the intensive entry of tech giants into AI drug discovery affect the industry? The consensus among industry insiders is clear: this is not a simple zero-sum game, but an ecosystem reshaping.

Ren Feng predicts that all biotech and even pharma companies will eventually be empowered by AI, and the entry of tech giants only accelerates this trend. AI drug discovery companies and tech giants are not in direct competition. The optimal model is for each to focus on their strongest areas: large companies provide general-purpose base models, computing power, and foundational algorithmic capabilities, while AI drug discovery companies leverage their strengths in biological understanding, wet-lab experience, and clinical validation. Together, they can achieve a 1+1>2 effect.

Notably, the division of labor in the industry is shifting from individual efforts to ecosystem-based competition and collaboration. Tech giants bring base model capabilities and computing power; global pharmaceutical companies hold clinical and commercialization resources; and independent AI drug discovery companies provide data flywheels and dry-wet lab closed loops. These three parties form a new industrial ecosystem through co-building, co-creation, and licensing models.

Regarding the future landscape, Ma Jian predicts that industry consolidation will continue. On one hand, AI drug discovery has entered the clinical validation phase, where moats are shifting from single-point models to systemic capabilities. Companies lacking data accumulation and engineering-driven closed loops will face greater challenges. On the other hand, the enhancement of model and experimental capabilities is lowering barriers in vertical domains, with opportunities shifting from "general platforms" to "deep verticals."

Industry evaluation standards are also changing. Insiders note that the criteria are shifting from valuation and narrative to data and milestones. In this sense, tech giants entering the field with capital, computing power, and talent are acting as "catfish" that activate the entire industry, accelerating AI drug discovery from concept validation to value realization.

(Source: Shanghai Securities News)