Era Of AI Superapps: So Close, Yet So Far

Apr 8, 2026 - 11:03

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Era Of AI Superapps: So Close, Yet So Far

AI companies are quickly moving beyond being simple platforms to becoming AI superapps. A case in point is OpenAI, which recently announced to raise $122 Bn to build an “AI superapp”.

The rationale? “As models become more capable, the limiting factor shifts from intelligence to usability. Users do not want disconnected tools. They want a single system that can understand intent, take action, and operate across applications, data, and workflows,” as per OpenAI.

The AI giant added that its superapp will bring together ChatGPT, Codex, browsing, and its broader agentic capabilities into one agent-first experience.

With this announcement, it seems that OpenAI is moving away from a chat-first interface to an agent-first user experience, where you have conversations and instruct the AI to take actions on your behalf across multiple areas, including coding, browsing, agentic tasks, and research, among others.

In a podcast, OpenAI president Greg Brockman remarked that the AI superapp will aim to provide personal artificial general intelligence (AGI), or a personal assistant that represents you in the digital world. The use case for it will be for both personal and business users, just as a computer is fit for both.

Currently, there have been no new capabilities announced, but Brockman mentions that users should expect a richer experience for what they already do with ChatGPT.

India Already Behaves Like A Superapp Market

For many in India, this idea is not entirely new. In fact, it is already playing out in subtle ways.

Aakrit Vaish, the cofounder of AI-focused VC fund Activate, believes that AI apps like ChatGPT have already started behaving like superapps in India.

According to him, apps like ChatGPT are already being used as a one-stop solution for many different needs, much like a superapp. Instead of using separate apps for writing, searching, learning, or even casual conversations, many users just go to ChatGPT for everything. So, behaviour-wise, it is already acting like a superapp, even if it wasn’t originally designed as one.

He argues that India’s opportunity may diverge at the execution layer. While global superapps may dominate digital workflows, India still requires deep integrations with physical services and last-mile delivery.

“That’s where I think the superapp opportunity lies in India,” Vaish said.

For Indian AI startups, this means the fundamentals remain unchanged. Building in India still comes down to three things: creating a novel AI experience, having proprietary insight into user behaviour, and building strong data or supply chain integrations for last-mile delivery.

In that sense, the rise of a global AI superapp does not necessarily disrupt local innovation models. Instead, it raises the bar for what differentiated experiences need to look like.

Vaish also cautioned against blindly copying this approach, noting that companies like Sarvam should stay focused on building enterprise AI infrastructure rather than chasing a unified superapp strategy. “It could just be a distraction for us,” he cautioned.

Rise Of Intent Layer

If there is one way to understand AI superapps, it is this: they aim to become the interface for intent.

Ashutosh Singh, cofounder of RevRag AI, frames it as a natural evolution. “AI is becoming the interface. Instead of switching between tools, users want to express intent in one place and systems to orchestrate everything.”

This fundamentally changes how software is discovered and used. Instead of navigating tools and workflows, users simply state what they want done.

But Singh adds an important caveat. Superapps may be the entry point, but not the entire system.

“Superapps will be great at initiating tasks, but real execution still needs deep context, data, and workflows inside individual apps,” he said

What emerges is a two-layer structure: superapps act as the entry layer, while in-app agents handle execution.

This suggests that while companies like OpenAI may control how work begins, they may not fully control how it is completed.

The Battle For Control

According to Sanchit Vir Gogia, founder and CEO at Greyhound Research, the AI industry is moving away from small, basic tools that only offer simple features or shortcuts. Because these tools do not have their own unique data or special ways of doing things, they are starting to look unnecessary. Their features are not disappearing, but they are being absorbed into giant tech platforms.

This means these smaller companies can no longer charge money for their tools since the big platforms now include those same features for free.

Since AI is moving from just giving suggestions to actually finishing tasks on its own, businesses need much stronger oversight and safety checks to make sure the software is behaving correctly.

The market is now splitting into three layers of competition:

Some companies are fighting to be the main screen where you start your work every morning.
Others are focusing on being the safety and trust experts who keep everything under control.
A third group is working to make sure all these different systems can talk to each other easily, so that one single company doesn’t end up controlling everything.

Overall, Gogia explains that the concept of AI superapps is not just about building a better product, but about who holds power. When one company controls everything — the tech, the interface, and how tasks are completed — the focus moves from how smart the AI is to who controls it. This includes identity, permissions, connectors, workflow logic, memory, audit trails, telemetry, and policy enforcement. Once this layer is established, organisations begin to depend on it to run processes.

Where AI Superapps Work And Where They Break

The usefulness of AI superapps depends heavily on where they are applied. People do not want to keep switching between tools, repeating context, or manually stitching workflows together. A system that can understand intent and carry it across applications removes a lot of this friction.

But this comes with trade-offs. When one system becomes the place where work starts, progresses and finishes, it naturally creates dependency. Over time, the question shifts from whether it works well to how much control users have over it, and how easy it is to move away if needed.

This model works best in environments where tasks are clearly defined and can move quickly from intent to execution. Software development, customer operations, and parts of knowledge work fit well here because of their structured workflows and measurable outcomes.

However, challenges begin to surface in more complex settings. In regulated industries, systems need to explain decisions and ensure accountability. Similarly, fields like design and video require collaboration, versioning, and rights management, which are hard to unify in a single interface. In a nutshell, these systems work well where workflows are predictable and bounded, but struggle where complexity and accountability play a bigger role.

The Weekly Buzz: Anthropic Blocks OpenClaw For Subscribers

Starting April 5, Anthropic restricted Claude subscription access on third-party tools such as OpenClaw, a move announced by Boris Cherny, creator of Claude Code, on X.

Users can still access Claude through these tools via extra usage bundles or an API key, but the free ride on subscriptions is over. Cherny framed it as a capacity issue—subscriptions weren’t designed for the usage patterns these external tools generate.

The open-source community pushed back fast. OpenClaw creator Peter Steinberger said he and investor Dave Morin tried to talk Anthropic out of it, managing only to delay the decision by a week.

He pointed to what he saw as a pattern — Anthropic rolling popular features into its own closed ecosystem before cutting off third-party access. One user called it a “greed move” dressed up as a capacity problem, arguing that it is not a good thing for people who can’t afford API costs.

Some have already moved on. Developers swapped out all Anthropic models on OpenClaw for Kimi K2.5 Qwen, and MiniMax M2.5, saying the experience was indistinguishable—except for the bill, being cheaper.

Whether Anthropic’s reasoning holds up or not, the move signals something real: as AI tools mature, the platforms building on top of them are increasingly at the mercy of the infrastructure layer beneath.

Startup In The Spotlight: Mindcase

Enterprise decision-making depends on timely, accurate intelligence. But the systems used to gather this data have not kept up. Key signals (like competitor pricing, product assortment, consumer trends, and market shifts) are spread across multiple sources like marketplaces, review platforms, directories and brand websites. Most companies still rely on manual research or rigid dashboards to make sense of this data, making it hard to track fast-moving changes or act quickly.

Founded in 2024, Bengaluru-based Mindcase is building an AI-native intelligence platform to solve this gap. Its platform uses specialised AI agents to continuously track data across ecommerce platforms, location sources, competitor websites, and social channels.

An intent-driven layer then interprets business questions, identifies relevant data, and turns it into clear outputs like dashboards, benchmarking reports, and actionable insights, instead of static spreadsheets.

Most market intelligence tools are designed for static reporting, not for fast-changing environments. Mindcase is targeting this gap by building a system that continuously tracks and interprets external signals, without requiring teams to restart research every time their needs change.

Prompt Of The Week

What prompts and hacks are CTOs, CEOs and cofounders using these days to streamline their work?

Here’s Maaz Ansari, cofounder and CRO at Oriserve, with his suggestion for turning dense enterprise AI research into actionable strategic briefs:

“Analyse how AI-driven platforms are reshaping enterprise workflows across Healthcare, BFSI, and Hospitality in India and global markets.

Identify the most impactful use cases of AI (such as intelligent agents, workflow automation, predictive analytics, customer experience orchestration, fraud detection, and real-time decision systems) and evaluate how they are being implemented across sectors.

Examine which AI capabilities (for example, data integration, interoperability, scalability, security frameworks, personalisation engines, and human-AI collaboration models) are seeing the highest adoption and why.

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