AI and the Future of Wet Labs in India
| AUTHOR | Aditi Palo |
| DATE | October 12, 2026 |
| CATEGORIES | Advanced Biology AI Bioeconomy |
Last month, in September 2026, the AI firm Anthropic announced that its team had discovered a novel enzyme system in bacteriophages called Array-associated Reverse Transcriptases (ART). While this sounded like any other notable biological discovery, the catch was that Anthropic claimed to have discovered it through its ‘AI-powered wet lab’. This AI advancement may not make life sciences research completely automated and AI-led, as some would argue, but rather heavily AI-enabled in the design and execution of biological experiments. For an emerging biotechnology power like India, which is targeting a $2.6 trillion bioeconomy by 2047, addressing the unique challenges and opportunities AI-enabled experimental biology research presents becomes all the more crucial.
At the UN Security Council, Anthropic’s CEO Dario Amodei noted a critical division of labor in discovering the novel enzyme system ‘ART’. The AI model Claude led the literature review and conceptualized the experimental design, while humans ran and verified the experiments in the lab. Around the same time, OpenAI moved its “GPT-Rosalind” AI Model, specifically designed for accelerating life sciences research, into controlled commercial access. These advancements led to speculations that biology research is transitioning to AI-led labs or Self-Driving Labs (SDLs), which combine high-throughput robotics and AI orchestration to create completely autonomous setups, devoid of humans.
The ground reality, however, remains far more nuanced, with many scientists skeptical of the narrative that AI will completely replace human biologists in the wet lab anytime soon. Within the Cynefin framework, experimental biology finds its place on the lower end of predictability with multiple constraints. Its inherently complex nature makes both critical thinking and physical dexterity provided by human expertise quite indispensable. AI’s inherent reliance on existing data sets, combined with the vast amount of unmapped biological diversity, further underscores the necessity of maintaining human oversight in research. Despite these, the rapid advancements in the usage of AI in wet labs cannot be ignored. They point to the possibility of a synergy between human biologists and AI, which can potentially accelerate discovery.
AI-enabled wet lab research can be analyzed across two different stages of an experiment: experimental design and experimental execution. The use of AI in experimental design is abundant everywhere, including India - from reviewing existing literature, reading and interpreting large data sets, screening potential biological candidates, to using it in closed-loop optimization. However, a growing reliance of life sciences research on proprietary, commercialized models like GPT-Rosalind can end up putting Indian biologists on the disadvantaged side of the AI divide due to high licensing costs and accessibility barriers. While the establishment of shared AI-ML infrastructure centers for biotechnology called “Moolankur” Bio-AI Hubs is a welcome move for physical capacity generation, research fellowships like “IndiaAI” seem to be inadequate for human capacity generation in this regard. To generate 30 million high-value jobs in the bioeconomy by 2035, as envisioned in NITI-Aayog’s report, there is a vital requirement to train all of the existing and upcoming experimental biologists on the usage of AI. Traditional biology programs must integrate AI upskilling courses that mandate students to complete rotations in coding, data pipeline management, and basic lab automation.
The follow-up stage after experimental design is experimental execution, which involves physical verification of the hypothesis through various processes under an experiment. For execution of experiments, automated laboratories are being actively explored, and self-driving labs are being ambitiously conceptualized. In all of this, AI positions itself in the role of an orchestrator of collaborative robots that carry out all the procedures required for an experiment. For labs across the world, the primary bottleneck in building such self-driving labs is no longer just physical machinery, but the complex orchestration software required to connect heterogeneous laboratory instruments into seamless, autonomous workflows. For India, this presents a unique strategic opportunity. India’s massive IT and software engineering ecosystem can be utilized and incentivized in building lab-orchestration software packages, positioning India as an upcoming global leader in this critical domain.
The transition towards AI-enabled wet labs will not lead to the end of human-led experimental biology, but rather push for its transformation. For India, the path forward lies in a two-pronged strategy - upskilling experimental biologists in AI applications, and strategically leveraging its IT ecosystem to dominate the upcoming lab-orchestration software industry.