Indian music composer Santhosh Narayanan said that he is using the new Chinese model, DeepSeek, to create songs for his upcoming films Sikandar, starring Salman Khan, and Retro, starring Suriya and Pooja Hegde.
“DeepSeek has been so good for me personally for coding in Reaper, so much so it has reduced my stem export time by well over 60% for Retro and Sikandar. Great times ahead for AI reasoning and GenAI. So much more can be done and competition is great for creators and users,” he said in a post on X.
Reaper is a digital audio workstation (DAW) software used for recording, editing, mixing, and producing audio. It is popular among musicians, sound engineers, and producers for its flexibility, low cost, and extensive feature set.
When a user joked about whether Narayanan is endorsing DeepSeek, he responded on X, stating, "LOL, it has already cost Nvidia $500 billion. It doesn't require my promotion."
Narayanan, a prominent figure in Tamil cinema, is known for his innovative approach to music composition. He debuted as an independent music director with the 2012 Tamil film Attakathi and has since worked on acclaimed projects across Tamil, Telugu, and Malayalam cinema.
In 2021, Santhosh composed for Sarpatta Parambarai, marking his fifth consecutive collaboration with director Pa. Ranjith. The same year, he worked on the independent music project Enjoy Enjaami, which became a viral sensation, clocking 19 million views within one week of its release. It currently has about 474 million views.
His work often blends traditional and contemporary elements, earning him recognition in the Indian film industry.
More recently, Santhosh composed music for the film Kalki 2898 AD, directed by Nag Ashwin and starring Prabhas and Kamal Haasan. The success of this project has led to his Bollywood debut, as he has been invited to score the background music for the upcoming film Sikandar, directed by A.R. Murugadoss and starring Salman Khan.
DeepSeek recently released its latest model, DeepSeek-R1, which is on par with OpenAI’s o1. It was trained using NVIDIA’s lower-capability H800 processor chips and was budgeted under $6 million.
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