OpenAI GPT Store: Opportunities for the Indian Developer Ecosystem The GPT Store flips the script for traditional dev shops, shifting from people-based billing to outcome-based models.

OpenAI's ChatGPT is now getting closer to becoming a full-fledged app store but with of course the touch of AI into it.

Earlier this week, OpenAI announced that it is now allowing developers to submit apps for review and publication in ChatGPT. According to OpenAI, these apps are now bringing in new context and letting users take actions like order groceries, turn an outline into a slide deck, or search for an apartment.

The company is also launching an app directory within the chat interface wherein users can browse featured apps or search. Developers can also use deep links on other platforms to send users right to their app page in the directory.

"This is just the beginning. Over time, we want apps in ChatGPT to feel like a natural extension of the conversation, helping people move from ideas to action, while building a thriving ecosystem for developers. As we learn from developers and users, we'll continue refining the experience for everyone. We also plan to grow the ecosystem of apps in ChatGPT, make apps easier to discover, and expand the ways developers can reach users and monetize their work," said OpenAI in the blog post.

ChatGPT opening up to developers comes weeks after a new pilot started by Razorpay, National Payments Corporation of India (NPCI), and OpenAI, and backed by banking partners Axis Bank and Airtel Payments Bank. As part of the pilot, users in a familiar can ask ChatGPT to order items from BigBasket. The AI agent will check the catalog, serve you some alternatives, and after your consent, it places the order via Razorpay's payments stack.

ALSO READ: Breaking Down Agentic Payments, UPI's AI Moment, And More

OpenAI's new move also opens up a new avenue for Indian developers, especially those pivoting to AI. For context, India is home to a massive base of developers, and have a sizable presence in typical app stores from Google and Apple.

GitHub recently said that India will have over 57.5 million developers by 2030. It will also be one of the largest developers hubs globally. It also disclosed that more than 21.9 millions are currently building on GitHub in India and is second only to the US, Business Standard reported citing 2025 GitHub Octoverse.

According to Jaspreet Bindra, co-founder of AI&Beyond, Indian developers can either go big with 'Global-First' tools for fat margins or niche down with 'India-Specific' GPTs, leveraging local context and language to build a defensive moat. The latter might offer a more sustainable competitive advantage.

"With OpenAI launching an app store directly inside ChatGPT, global distribution is now available by default, which fundamentally shifts the competitive advantage from reach to depth of intelligence. India-specific GPTs built for vernacular markets can certainly generate early traction, as language nuances and cultural understanding still act as an initial defensive moat. However, I don't see this as a binary choice—what matters more is the order in which developers build," Dr Xi Zeng, founder of Chance AI, told Entrepreneur India.

"My strong view is that developers should first build global-first capabilities and then express them through India-specific insight. The hardest problems in AI—such as model orchestration, reliability, evaluation, and user experience—are global problems. If Indian developers solve these at a world-class level, they gain access to global margins and long-term relevance. Local context alone is no longer a durable moat. Language translation and basic cultural adaptation are being rapidly commoditised by foundation models, which means an 'India-only GPT' relying purely on vernacular data may be defensible for months, not years. The sustainable advantage comes from owning a deep capability—such as visual reasoning, workflow intelligence, or domain judgment—and then using Indian context as a multiplier rather than the core value," Zeng added.

Having said that, there are worries that Indian developers or developers in general can get 'Sherlocked' as the platform adds native features. Will this initiative really help build a lasting environment for Indian startups, or is it just going to be a cheap way to test ideas before they become full-fledged products?

"To avoid getting 'Sherlocked', focus on specific use cases or industries OpenAI might not prioritize, like healthcare or finance. Build custom GPTs that solve real problems, and you'll be golden," Bindra said.

Techugo CEO Abhinav Singh adds that if developers merely put thin layers over the primary features of the model, then it is very likely that they will get "Sherlocked." The most secure way is to have what OpenAI cannot easily transform into a commodity: workflow depth, proprietary data loops, integrations, and industry-specific outcomes.

"The GPT Store must not be perceived as the last point of the journey for serious startups. It will be the discovery and validation layer that will be the final product. Just like a low-cost lab, it can be utilized to verify demand, estimate people's willingness to pay, and analyze user behavior. Currently, a GPT has to be recognized as an application that is being used frequently and has business value; it must be developed into a full product with its own distribution, billing, and data advantages first. Startups viewing the GPT Store as the final destination will be the ones who eventually lose their differentiation and customer ownership," Singh further explained.

Experts are also of the opinion that the GPT Store flips the script for traditional dev shops, shifting from people-based billing to outcome-based models. And developers must adapt by focusing on high-value services and AI-enabled use cases. To objectively track their growth, they need to create a custom GPT, a revenue stream which focuses on user engagement (active users, retention, and usage patterns) and conversion rates (users upgrading to paid plans or premium services).

Zeng notes that the GPT Store represents one of the most significant shifts for India's service-based development ecosystem. It enables service firms to move from time-based billing to IP-led, repeatable revenue by productising the deep client and workflow knowledge they already possess. In effect, it turns service shops into potential product labs—but only if they change how they measure success."

"Two metrics matter more than anything else. The first is repeat usage, or habit formation. Not installs or demos, but how often the same user comes back without being prompted. If a GPT is not used repeatedly by the same customer, it is a demo, not a business. The second is willingness to pay without customisation. The moment revenue depends on bespoke prompts, client-specific tuning, or heavy manual intervention, the model reverts back to services. A viable GPT product must be able to answer one simple question clearly: would someone pay for this even if I didn't customise it for them? If the answer is no, it's not a product yet," he said.