Why Indian Developers No Longer Start Their Day With a ChatGPT Tab

For many developers, ChatGPT was the first real introduction to AI-assisted coding. It made writing scripts, debugging, and even learning new frameworks faster and more intuitive.

But as AI tools move from experimentation to production environments, coding priorities are also shifting. Developers are no longer choosing tools based on familiarity; rather, they are focusing on reliability, reasoning ability, and how well these systems fit into real-world workflows.

That shows up in numbers. ChatGPT app’s market share dropped from 69.1% in January 2025 to 45.3% in February 2026, according to Apptopia data. Anthropic’s Claude briefly became the top free app on the Apple App Store in March, while Google Gemini surpassed 2 billion monthly visits for the first time in January, per Similarweb. 

LLM adoption in enterprises is following a similar trend. Menlo Ventures’ survey late last year found that while Anthropic now earns 40% of enterprise LLM spend, OpenAI’s share shrank from 50% in 2023 to 27% in 2025.

From Convenience to Capability

In early use cases, speed and accessibility mattered most. But as projects scaled, the cracks began to show, especially when AI tools struggled with complex codebases or tracking context. Some of these tools, especially GitHub Copilot, Tabnine, and the standard ChatGPT, were not equipped to understand the architectural or functional relationships across an entire project, leading to context loss.

“When working on large features, many AI tools struggle with long-term context retention. They overload with irrelevant information and lose track of the system’s architecture, which leads to hallucinated code and more refactoring,” Praval Pattam, a 21-year-old computer science student at NIT Calicut, and Co-founder and CTO of fintech startup IndiaTax.AI, explains in a conversation with AIM. His team now evaluates AI tools based on speed, reasoning, and consistency.

This is where tools like Claude are gaining traction, particularly the 3.5 Sonnet and 3.7 Sonnet models. Known for stronger reasoning and better context handling, they are increasingly being used to manage multi-file codebases, debug complex projects, and manage documentation.

According to Anthropic’s country brief, India ranks second globally by the share of total Claude.ai use, with over 45% users tapping it for software development and engineering roles. Some of the most common tasks performed by the tool in India include correcting code errors, modifying programs to meet customer requirements, upgrading interfaces, rewriting code, and troubleshooting.

“Claude stands out for its ability to hold context across long conversations. Its outputs tend to be more production-ready and require fewer corrections,” Aishwary Goswami, Senior AI Engineer at AI R&D company IntellifAI Labs, adds. 

At the same time, developers are not abandoning other tools entirely. Instead, they are becoming more selective, harnessing tools for specific tasks rather than relying on a single assistant.

The Rise of Multi-Model Workflow

The growing preference for certain AI tools is deeply tied to how these systems are evolving.

Mohit Sewak, Staff Software Engineer (AI Security Research) at Google Cloud, describes this as a shift from simple code generation to reasoning-driven systems.

“The evolution of large language models from reactive tools into autonomous, reasoning-capable agents represents a fundamental shift. Instead of merely predicting the next line of code, newer systems increasingly verify, test, and refine output, making them more reliable in complex workflows,” he notes.

Salmon Faroz, an ML Engineer-2 & Researcher at digital business services company Teleperformance (tp.ai), adds that the real difference between tools lies in training and alignment. “Models like Claude are optimised for instruction-following and long-context coherence,” he says, noting that this becomes critical when working across large codebases.

Meanwhile, Gemini is emerging as a strong contender, particularly in workflows that require handling large repositories or integrating code with documentation and other formats.

“These tools excel in different areas. Claude is strong in reasoning-heavy tasks, OpenAI models are strong in instruction-following and ecosystem integration, and Gemini is gaining ground in multimodal workflows. The trend is not replacement but orchestration,” explains Charulkumar Chodvadiya, AI Engineer and Researcher at healthcare IT SaaS provider Radin Health.

Rather than fully switching from one tool to another, developers are increasingly adopting a hybrid approach. Pattam describes this as choosing “the best tool for each job”—using one model for architecture, another for routine coding, and others for testing or prototyping.

This reflects a broader shift in the industry. Developers are moving away from simple chat-based interactions toward more integrated, agentic tools that can execute multi-step workflows. However, this transition is not without its challenges.

Faroz points out that while AI tools improve speed, they can also impact learning. “Developers who rely heavily on AI complete tasks faster but often retain less understanding,” he notes, raising concerns about long-term skill development. Anthropic’s own study, published earlier this year, revealed that developers using AI assistance scored 17% lower on tests covering concepts they had just coded, compared to those working without AI.

There are also technical risks. AI-generated code can contain subtle errors or vulnerabilities that are not immediately visible, making human oversight critical.

Enterprise Adoption

At the organisational level, this shift is becoming even more pronounced.

Anduri Roshan, a senior AI analyst at a tech company, says companies are no longer betting on a single platform. Instead, decisions are driven by performance, integration, and trust. “Organisations pick AI tools based on three things: does it work, does it fit into existing workflows, and can we trust it with our data,” he adds.

While GiftHub Copilot continues to dominate due to its deep integration into developer ecosystems, tools like Claude are gaining ground in terms of quality and reliability. Meanwhile, platforms like Cursor are building momentum through more agentic, workflow-driven experiences.

Roshan notes that while the current phase is marked by experimentation, the market will likely consolidate into a few dominant players. However, no single tool currently leads across all use cases.

The movement beyond ChatGPT is not a rejection; it is a sign of how quickly the AI ecosystem is maturing. Developers are no longer looking for a one-size-fits-all assistant. Instead, they are building workflows that combine multiple tools, each coming with its own strengths.