When Ankush Sachdeva decided to skip IIT Kanpur’s final placement season in 2014, he wasn’t chasing a grand startup dream — he was chasing a hunch. That hunch, sparked by a chance discovery on Facebook and fuelled by relentless product experimentation that followed, would eventually become ShareChat, a social media platform that today reaches over 200 million monthly active users (MAU) across the country.
As a final-year student, Sachdeva used to tinker with a debating app called Opinio along with his college peers and eventual co-founders Bhanu Pratap Singh and Farid Ahsan. While promoting it in a Sachin Tendulkar fan group on Facebook, he stumbled across something odd: 50,000 people were sharing their phone numbers in the comment section of a post, asking to be added to WhatsApp groups where jokes and memes were being shared. “It was bizarre,” he recalled. “But it made me realise that there was a deep need for vernacular content, and no one was addressing it.”
Over the next 48 hours, they created several WhatsApp groups using those phone numbers, and the result was surprising. Strangers coordinated effortlessly to share jokes, memes, and videos in Hindi, Tamil, Telugu, and other regional languages. The energy of this interaction lit the first spark for ShareChat.
In January 2015, Sachdeva formally registered ShareChat, borrowing Rs 30,000 from his parents towards the paid-up capital and promising to find a job if it didn’t work out in two years. This is also the time they raised their first seed cheque from India Quotient.
Seed-to-scale
The first iteration of ShareChat was a public chatroom app that mimicked the WhatsApp group dynamic but made it discoverable and scalable. But users weren’t interested in chatting, they just wanted content. A pivot to chatbot-based content aggregation followed, but even that felt clunky. Users would log onto the platform, download the content, and leave. The platform needed a mechanism to keep users on the app.
So then came the version that stuck: a content feed that gives an infinite scroll experience. The engagement metrics spiked overnight with shares to WhatsApp jumping 5x, and retention doubling.
By October 2015, the app had just a few thousand users, but momentum was building. To seed content, the team ran over 100,000 WhatsApp groups using automation scripts, scraping popular memes and jokes, and feeding them into ShareChat. In April 2016, they enabled user uploads. Within a week, user-generated content overtook all bot-sourced material.
“People demanded credit,” Sachdeva recalled. “They wanted their names on their content, they wanted profiles and followers. What was a simple content sharing platform, these creators pulled a social network out of it,” he said.
Between 2016 and 2018, the team doubled down on building out a recommendation engine and optimising the feed. Initially, the focus was on encouraging users to follow accounts whose content they wanted to see, but what worked was a “trending feed” that showed algorithmically selected content. That insight helped ShareChat double down on its trending feed algorithm and hit 10–15 million MAU by 2018, and also raise a $100 million round . But just as growth picked up, trouble arrived.
The Chinese challenge
In 2018, ByteDance’s TikTok was gaining significant traction in India, as was its vernacular social media platform Helo. Sachdeva claims that Helo was a clone of ShareChat, which copied its UI elements and scraped its hashtags.
Though a court case followed and ByteDance was asked to make some changes, the damage was done. “While we probably had a better content and product team but whenever we would launch something, they would copy it and get much better engagement than us. We eventually realised that the underlying ML-powered recommendation system that they have is far superior to ours,” Sachdeva said.
This is what pushed the company to invest in building an AI-driven recommendation system by hiring talent from London and the Bay Area in 2021. In 2023, ShareChat launched its first deep learning recommendation model and saw a 10 percentage point lift in retention.
Enter Moj
The unexpected ban on TikTok in India in 2020 created another moment of opportunity. Within 30 hours of the ban, ShareChat launched Moj -— a short video platform powered by the same recommendation engine. Moj hit nearly 10 million downloads within the first week.
Today, both ShareChat and Moj have over 100 million MAU each. But with capital drying up post-2022, the company had to focus on turning profitable by slashing marketing spend by 95% and relied entirely on its algorithm to hold traffic. At the same time, the team worked to make its machine learning models capital-efficient. By February 2025, ShareChat was cashflow positive. The firm is now betting on short-form, vertical video dramas created by regional creators using generative AI tools. “Everyone’s fighting the same algorithmic race,” Sachdeva said. “But our battle now is about differentiated content”.