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India ranks 10th with $1.4 billion private investment in AI, US tops Top 10 league

China is a distant second at investments worth almost $8 billion, trailed by the UK at almost $4 billion

India ranks 10th with $1.4 billion private investment in AI, US tops Top 10 league

India has secured the tenth position in terms of private investment in artificial intelligence (AI) with an inflow of $1.4 billion, as reported by Bond Capital. The United States leads this ranking, with investments exceeding $67 billion, according to the global technology investment firm's release.

This data emerges as the Indian government emphasizes AI through initiatives like the IndiaAI mission and the Global IndiaAI Summit.

Following the US, China is in second place with nearly $8 billion in investments, and the UK is third with almost $4 billion. Other countries receiving significant private investments in AI include Germany, Sweden, France, Canada, Israel, and South Korea.

These investments highlight the US's strong capability to attract capital, talent, and an overall ecosystem that supports AI development, which is set to revolutionize the technology sector.

Bond Capital’s report, "Will Masters of Learning Master New Learning," indicates that of the 17 global publicly listed companies with market capitalizations exceeding $500 billion, 14 are based in the US. Nine of these companies operate in the tech and AI space, including giants like Microsoft, Nvidia, Apple, Alphabet (Google), and Amazon.

The report also notes that new-age internet companies now reach 100 million users much faster than those founded in the early 2000s. For instance, while companies like Netflix and LinkedIn took nearly a decade to reach this milestone, newer platforms such as OpenAI's ChatGPT achieved it in less than six months.

The US not only leads in investments but also hosts over 60 machine learning models, with China trailing at 15. India does not appear on this list.

A machine learning (ML) model is a system that uses data to identify patterns and make predictions or decisions without explicit programming for each task. It learns from examples and improves as it processes more data.

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