The AI Browser Shift: What Founders Should Build Next

Conversation around Perplexity’s new AI browser, Comet, has been steadily growing. Interest accelerated further in mid-October when OpenAI introduced its own AI browser, pushing the idea of “AI-native browsing” into mainstream discussion. Together, these launches have raised a broader question: what happens when the browser itself becomes an AI interface?

Users are already adapting to changes like AI Overviews occupying a large part of Google’s results page and AI Mode becoming an entire tab and showing us that browsing is moving from static page navigation to goal-driven interaction.

This change affects more than consumers. Teams building web products — from developer tools to SaaS dashboards — will feel it directly. Workflow components such as APIs, retrieval layers, and plug-ins may start to sit one layer lower in the value chain as the browsing interface becomes more intelligent and more capable of reasoning.

Before exploring what this means for founders, it helps to define what an “AI browser” is and how this interface actually works.

What exactly is an “AI browser”?

In simple terms, an AI browser is a browser that lets you interact with the web using text instead of clicks. The text instructions are translated into instructions to reach a goal, much like Cursor, but for interacting with the web.

A short goal (“compare these pages,” “find the relevant docs”) is translated into steps taken inside the browser itself. Technically, it operates through three core layers:

1. Parsing layer: Reads and interprets what’s on the page; its performance still depends on your internet speed and local rendering.
2. Reasoning layer: Uses a model or agent to decide what to do with that information, whether to summarise, compare, extract, or act on the content.
3. Interaction layer: Controls the interface itself — moving the cursor, clicking, scrolling, and executing tasks on the page.

This differs from traditional AI agents, which rely on APIs and webhooks to act. AI browsers bring that reasoning into the web interface directly, letting users keep familiar workflows while still gaining automation and assistance.

Everyone’s Reaction to AI Browsers

Before looking at how founders are reacting, it’s worth understanding what the broader audience thinks. The public response so far has been a mix of curiosity and caution.

To gauge sentiment, let’s examine discussions surrounding Perplexity’s Comet, OpenAI’s Atlas, and Dia Browser by Browser Company, which has been available for roughly six months. Across Reddit and early-access forums, three major sentiments keep recurring.

The groundwork that came before this wave

“The ideas have been around for years. They were built in the wrong place.”

Browser automation has existed for a long time, just not in a form anyone could rely on. Selenium, BrowserStack, and Playwright proved the browser could be scripted, but they lived in the QA world. Useful for tests, but couldn’t account for dynamic page UIs that could be changed across applications. This background made them a natural launch point for early AI workflows, because they already understood how to deal with unstructured pages.

Then came the Chrome extension wave. Merlin, Otter, Glasp, and Tactiq helped with quick tasks, but extensions can’t shape how a browser works. Arc Max (The Browser Company) went further with AI features inside the interface, yet it still operated as a layer on top of Chrome.

These products had strong ideas. The container held them back. Extensions and test frameworks were never the right foundation for a real, goal-driven browsing experience.

The new wave of AI browsers takes a new approach. Instead of stitching automation around Chrome, teams are building the browser as the execution layer. The workflow sits in one place. It behaves predictably and doesn’t fall apart when Chrome ships an update.

Emerging usecases for AI broswers

The first set of workflows is already showing up in “this actually works” posts from users. When people stick with an AI browser for more than a quick demo, they keep using it for things like:

• Pulling documents and PDFs into one place
• Comparing multiple pages or tabs
• Summarising long articles and YouTube videos
• Scanning dashboards and reports
• Building quick lists of links, tools, or products
• Running fast, agent-style searches across several sites

Independent reviews echo the same pattern. AI browsers are consistently framed as “built for research,” with people leaning on it to synthesise information from multiple tabs, keep sources attached, and live alongside their normal browsing instead of replacing it.

Atlas vs Comet: AI Browser Showdown

The first durable use cases are research-style workflows and light automation around existing browsing habits. That’s the baseline users already say feels good enough to keep.

Where does this leave Indian AI startups?

India already has proof that browser-adjacent companies can become global infrastructure. BrowserStack turned “test in real browsers” from a QA headache into a SaaS category and became a $4B company doing it. It didn’t build a consumer browser. It owned the layer that made every browser more useful to developers.

The AI browser wave opens a similar window.

Most Indian SaaS and devtools teams already build for the web — dashboards, internal tools, APIs, and workflows that live inside the browser. As AI browsers mature, these products become “targets” for agentic actions: pages to read, buttons to click, forms to fill, states to monitor. That means Indian founders don’t have to build a competing browser to participate. They can own the reliability layers that make AI-native browsing work:

• Testing, observability, and replay tools for AI agents operating inside browsers
• SDKs that make apps agent-friendly by exposing stable actions instead of fragile selectors
• Logging and audit rails between AI browsers and enterprise data

The next open frontier is monetization. As AI browsers start to intermediate how users find and act on the web, search itself fragments. Whoever builds the visibility and optimization layer for AI results — call it AI-SEO — will define how the new web is indexed, ranked, discovered, and most importantly, monetized.

There’s also a gap around intellectual property: if AI browsers read and summarize everything, how do creators get credit or licensing value from that access? For Indian AI browsing founders, that means there is room to build frameworks of data licensing, memory control and attribution for browser-agent contexts. 

India already contributes heavily to the training data layer behind AI. One recent study estimates around 50,000 freelance data annotators and 20,000 full-time annotators in India, servicing a global data-annotation market projected at over $7 billion by 2030. Annotation startups like ContentWhale and PixelAnnotation are evolving from simple image-tagging to complex workflows for 3D models, robotic tasks, audio ASR, and multilingual datasets.

What to do if you are building a browser?

For people building toward this, there’s some good news. Chromium — the base for Chrome and most new AI browsers — might soon be separated from Google or partially sold. That would open a real path for independent teams to build AI-native browsers without inheriting Google’s ecosystem lock.

Both Perplexity’s Comet and OpenAI’s Atlas are built on Chromium, proving the base is solid and ready. It may also explain why Google hasn’t launched its own AI browser yet — losing control of Chromium would mean losing control of the browsing stack itself.

If Chromium does open up, or frankly even if it doesn’t, smaller teams have a rare window to move faster than the giants. The big players are busy building heavy, closing wall ecosystems; the real opportunity is to make something lighter and actually useful.

Beyond the fundamental goal of building a truly AI-native browser, some things can be fixed right now:

• Pluggable AI layer: Let users or enterprises connect their own model endpoints instead of forcing them into a single provider. It’s the fastest way to win enterprise credibility.
• Local memory and embeddings: Keep memory on-device, focused on privacy-first, faster recall, and easier compliance.
• Embeddable reasoning agent: Let teams plug in their own agent frameworks (like Vertex, LangGraph, n8n, or CrewAI) to create a hybrid system instead of hardcoding one reasoning stack.
• Interoperable RAG connector: Make it easy to connect custom backends — from lightweight options like Chroma to heavier systems like Weaviate — so teams can bring their own data without friction.
• Sandboxed execution and anonymization: Isolate the reasoning process with clear controls on memory and encryption so it can’t access sensitive local data.

And unlike the Apple-first launches we’ve seen, this can start on Windows and Linux, where most engineers and early adopters already live.

AI platforms are pulling more control to the center, but progress still comes from the edges. The next generation of companies won’t compete on models — they’ll compete on experience, trust, and execution.

If you’re building where users and agents meet, or rethinking how the browser becomes an interface layer, reach out.

You can reach out to me via [email protected]