AI Browsers After Atlas - The Landscape#

On Oct 21, OpenAI launched Atlas browser, reigniting a category that has been relatively quiet since Chrome's debut.

I'm Yitao Hu, founder of HuBrowser and a former Google engineer, with 10+ years building private browser. We shipped the Android AI Browser in 2024. Below is a concise, practitioner's guide to what an AI browser is, different approaches, and how the market is shaping up.

#What Is an AI Browser?

• Automated interaction: executes clicks, fills forms, and navigates web/apps from intent.
• Content processing: summarizes pages, answers questions, and generates text.

Why now? Three key reasons:

• Entry advantage: closer to the user, smoother monetization.
• Context awareness: behavior, history, goals — richer than chat-only agents.
• Data pipeline: AI training needs data. Public data has plateaued; private user context is the next frontier.

Who's building:

• Giants: Chrome, Edge, OpenAI Atlas, Perplexity Comet
• Startups: ~7–8 publicly launched products (including us)
• Tools capable of AI Q&A: dozens

#Five Technical Paths

All based on Chromium, but in different ways. Application-layer products can land quickly, but only system-level integration can deliver a truly native AI browser:

• CDP automation scripts — OpenAI Operator, Browser Use, Browserbase
  • Pros: fast to prototype
  • Cons: easy to flag, inefficient, shallow integration
• Prebuilt Chromium (Electron/Tauri) — Fellou, Arc
  • Pros: quick delivery
  • Cons: incomplete browser features; constrained UX
• Browser extensions — HuBrowser Extension
  • Pros: lightweight, privacy-transparent
  • Cons: host-dependent; limited speed/automation
• Enhanced Chromium — Chrome, Edge, Atlas, Comet
  • Pros: balanced approach
  • Cons: without core tweaks, little edge over extensions
• Enhanced ChromiumOS — HuBrowser
  • Pros: system-level integration, cross-platform consistency, efficient and abuse-resistant
  • Cons: extremely hard; requires long-term kernel expertise

Structural issues with shallow wrappers:

• Login and risk controls: automation patterns trigger re-verifications; fragmented UX. In practice, agents often need to re-login, breaking flow — hard to become a daily tool.
• Mobile limits: without system hooks, mobile automation is unstable — hence few mobile AI browsers.
• Speed/accuracy: lacking structured data leads to "slow ops." Simple forms: CDP 15–30s vs. system-level 3–5s.
• Cost: redundant context/trial steps burn tokens (often tens of thousands per task); scales poorly.

#Why Browser Engine Work Is Hard

Fewer than ~10k engineers have worked in the browser core — typically within 1–2 modules. Architecture change demands broad, deep expertise. Even Google and Microsoft have few kernel-level breakthroughs over a decade. Even as OpenAI hires top Chrome architects, Atlas and Comet today appear mostly focused on sidebar/assistant layers. This unique difficulty gives us the confidence to compete with the giants as a small team.

#Our Approach

Testing in 2023 showed "AI clicking" helps for repetitive work, but the app-layer innovation hits a ceiling (weak anti-abuse, slower, costly). We're building from the system core layer for AI use.

Why we launched Android first?

• Technical high ground: an Android browser can be easily compiled to other platforms; but not the other way around. The reverse loses ~90% code/features.
• System connectivity: C++ threads through Android to automate web apps, native apps, extensions, and scripts in concert.
• Market: Android leads globally, enough to make a profit.

Desktop and iOS

• Desktop: we ship an open-source HuBrowser Extension — transparent code/requests; no personal data collection; plug in your own models for local workflows.
• iOS: closed ecosystem and sensitive automation APIs raise policy risk; we're deferring.

#Business Landscape

Primary monetization: subscriptions + ads; secondary: data services.

• Giants bundle AI into existing products to spread costs.
• Startups win via focused use cases or deeper technical differentiation.
• Segments: desktop/mobile/cloud; ToC/ToB/ToDev; plus model/hardware integrations.
• Dynamics: desktop is most crowded; mobile is relatively less contested due to high technical barriers.
• Opportunity: >5B users; even 5–10% AI share is substantial. The key is crossing "usable" → "habit-forming."

#Privacy and Control

Watch for geo limits, opaque filtering of "sensitive" requests, bundling disguised as "AI recommendations," and data uploads "for better UX."

Core tensions: agent risk transparency; confirmation bounds for pay/delete/post; privacy vs. capability; data-driven business vs. long-term trust; accountability as AI decisions grow.

#Closing thoughts

The breakpoint isn't a checklist of features; it's system-level fusion that makes AI indispensable day to day. That requires time, kernel depth, and relentless UX focus.

If this resonates, please like, comment, and share.

Learn more: hubrowser.com

Try our Android AI Browser: Google Play