Former OpenAI researcher Shyamal Anadkat is raising $15 million in seed funding for his new Indian startup, Empirical Machines. The company aims to use AI to speed up scientific discovery. As a private venture, it is not available for trading on public stock exchanges.
Empirical Machines, a new artificial intelligence venture led by former OpenAI researcher Shyamal Anadkat, is targeting a $15 million seed funding round. The startup is currently in advanced discussions with major investment firms like Peak XV Partners and Bessemer Venture Partners. If the deal closes at the projected $80 million valuation, it would represent a significant early-stage bet on deep-tech AI in India.
Unlike many AI startups that focus on building consumer-facing chatbots or content tools, Empirical Machines is positioning itself in the scientific discovery sector. The company plans to use AI models to analyze complex chemical compounds and speed up experimental processes. The founding team includes professionals who have previously worked at organizations such as Google DeepMind, OpenAI, and Sarvam AI. This technical background is a key reason for the interest from prominent venture capital firms.
While the funding reflects growing investor confidence in the Indian AI ecosystem, it is important to note that Empirical Machines is a private company. It is not listed on the National Stock Exchange (NSE) or the Bombay Stock Exchange (BSE), and its shares are not available for purchase by retail investors. The capital raise is strictly an institutional private equity transaction.
The broader Indian AI landscape has shifted significantly over the past year. Investors who were once cautious are now actively seeking out startups that possess a clear technological advantage, rather than those that simply act as intermediaries for third-party AI models. However, this sector comes with inherent risks. Deep-tech and scientific AI startups face high operational costs, primarily due to the expensive computing power and specialized graphics processing units (GPUs) required to train and run these models.
For the company, the primary risk lies in execution. Bridging the gap between AI-generated insights and real-world scientific verification is complex and time-consuming. Success is not guaranteed, and the company will need to manage its cash flow carefully as it invests in heavy infrastructure. Investors watching the sector should monitor whether such startups can translate their research into practical, commercial applications that solve real-world problems. The ability of these firms to manage costs and deliver tangible results remains the most critical factor for long-term viability.