The government has launched the 'Semicon 2.0' policy with a ₹1.27 lakh crore budget to strengthen the local semiconductor ecosystem. As part of this, the Centre for Development of Advanced Computing (C-DAC) is working on a $200 million project to develop an indigenous AI chip. This move aims to ensure strategic autonomy, though its success depends on overcoming significant manufacturing and cost challenges.
On August 31, 2026, the Government of India formally announced the 'Semicon 2.0' scheme, a massive initiative with an outlay of ₹1,27,500 crore. This policy is designed to build a complete semiconductor ecosystem, including design, manufacturing, and research. A core part of this broader vision is a specific, five-year project led by the Centre for Development of Advanced Computing (C-DAC) to design an indigenous artificial intelligence (AI) chip with a budget of $200 million, or approximately ₹1,900 crore.
The push for a sovereign AI chip is primarily a strategic response to global supply chain vulnerabilities and export restrictions on high-performance computing hardware. By designing its own chip, India aims to ensure that sensitive national data in areas like defense, aerospace, and public services can be processed without relying entirely on foreign-supplied technology. The project is focused on developing a 2nm chip, which refers to the tiny size of the chip's features—smaller features generally allow for faster and more efficient performance.
While the government is leading this effort, private companies are also aligning their strategies with the national focus on AI. For instance, HCL Technologies has made significant moves in the AI space, including a $150 million investment in the AI firm Sarvam AI and plans for a major AI data center in Odisha. These actions reflect a broader industry trend where Indian tech firms are positioning themselves to capitalize on the increasing demand for specialized computing infrastructure.
However, the path to building a sovereign chip is filled with technical and economic hurdles. The most immediate challenge is manufacturing. India currently lacks the high-end fabrication facilities required to produce advanced 2nm chips, meaning the country must continue to outsource manufacturing to global foundries, such as Taiwan Semiconductor Manufacturing Co. (TSMC). This reality means that while the chip design may be indigenous, the production process remains tied to external partners.
Furthermore, analysts highlight that sovereign AI projects face a difficult trade-off between strategic value and commercial cost-efficiency. Developing advanced semiconductor technology requires massive investment. Competing with global giants that already have established manufacturing and huge economies of scale is expensive, and domestic chips may struggle to match the low costs of global mass-produced alternatives. The true test for this project will be its transition from successful laboratory testing to deployment in real-world strategic environments by the 2030 target. Investors and industry watchers will likely monitor the progress of these manufacturing partnerships, the timeline for the first chip prototypes, and how effectively the government can scale these technologies for actual use.