Welcome to The AI Shift by Inc42, our all-new newsletter that delves deep into the world of artificial intelligence, LLMs, big tech giants and the major trends sweeping the Indian startup and tech ecosystem. Here’s the fourth edition; do send us your feedback and suggestions so we can improve as we go along!

For years, retail investors have craved one thing — a cheat code that could help them maximise returns by cutting through noise, timing trades better, and managing risk more efficiently. From Telegram tips to YouTube gurus, the search for this edge has been unending.

AI promises to change this equation even if that promise isn’t entirely new. There were robo advisors before built on rule-based models, based on static data, and pre-defined risks. They automated for use-cases like upselling, but didn’t really “think” on investors’ behalf.

With the advent of generative AI, the likes of Groww, Zerodha, INDMoney and other digital brokers have evolved. They are increasingly using GenAI models and tools to summarise market information, surface relevant signals, answer portfolio-level questions, and reduce the cognitive overload that comes with modern trading.

But this shift raises uncomfortable questions.

If AI can analyse data faster and spot patterns humans miss, how close can it get to actual trading decisions? Where does assistance end and automation begin? And in a market as tightly regulated as India, how do platforms ensure that AI-powered insights do not nudge users into risky behaviour?

And finally, as AI embeds itself deeper into the retail investing stack, how much control are humans willing to give up?

Embedding AI Into The Discount Broking Stack

Retail trading is cognitively demanding. Investors juggle multi-asset portfolios, shifting intraday margin, volatile option chains, earnings data, and an endless stream of news. AI co-pilots are emerging as the layer that can condense this complexity into insights that traders can absorb and act on faster.

At INDMoney, AI has been designed to sit squarely in the insight layer. It synthesises signals, summarises information, and answers portfolio-level questions in natural language, but never crosses into execution.

“We draw a hard architectural line between probabilistic insight and deterministic execution,” said Kausal Malladi, CTO at INDMoney. “AI reduces the time-to-insight, but trading is ultimately about risk appetite, which is a subjective, human variable.”

The thesis is gaining ground across brokerages — let AI handle cognitive load, not capital.
“If traders follow AI signals without thinking, it can cause big mistakes. AI should help you make decisions. It should not make decisions for you.” a Groww spokesperson added.

Also, instead of rolling out generic chatbots or surface-level assistants, Indian online brokers are embedding AI deep into their core systems. The goal is not conversation, but context.

Various platforms like Zerodha, Groww, INDMoney, and FYERS have different approaches to how they plug in AI systems. For instance, both Zerodha and Groww offer model context protocol (MCP) support, which lets you connect to Claude or ChatGPT for AI-assisted workflows.

At INDMoney, Malladi explains that AI is woven tightly into data-retrieval pipelines, internal engineering processes, and customer operations. However, its AI models do not ‘know’ market data. They are simply fed structured, real-time inputs from internal systems and reason only within that........

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