Why AI Browsers Won’t Replace Chrome Yet — What Webmakers Need to Know

Are AI Browsers Ready to Take Over?

Short answer: Not yet. AI-first browsers in 2025 — think OpenAI’s Atlas and Perplexity’s Comet — are clever experiments, but after hands-on testing and talking to developers I found them flaky for complex work and rife with implementation and trust gaps. They’re delightful at summarization and single-step automation, and then they trip over reality when a workflow needs visual judgement, stateful navigation, or careful provenance. That mismatch matters.

What makes an "AI browser" different?

Call them generative assistant browsers: the search box gets pushed to the side and a chat-first browser UI takes center stage. What’s different in practice:

• Chat-first UIs that let you ask in plain English and get a conversational workflow;
• Agentic browsing — assistants that act on your behalf: click buttons, fill forms, extract structured lists, or run scheduled tasks;
• Summarization and context synthesis across tabs and media (yes, even condensing a long video into bullet points).

These agentic, multimodal agent workflows are expensive to run, which is why many of the more powerful features sit behind paid tiers. Still, they show a different interaction model than Chrome’s traditional address-bar-first approach.

How well do they work today?

Reality check from testing and interviews: mixed bag.

• Where they shine: summarizing pages, transcribing videos, extracting straightforward facts from visible text, and popping results into editors or spreadsheets.
• Where they stumble: visual-heavy interfaces (dynamic widgets and pop-ups), long multi-step flows (think booking flights or complex checkouts), and tasks requiring nuanced judgement or consistent state handling.

Example from a test lab: ask an AI browser to list professionals who reacted to a LinkedIn post. Instead of reliably scraping a structured list the assistant took screenshots, ran OCR, looped on heuristics and sometimes got stuck. It eventually produced something useful — but it was slow and fragile. For trust-sensitive tasks (payments, bookings), that fragility is a deal-breaker.

Why websites need to change — "human version" vs "robot version"

Agents want predictable, indexable inputs. Sites built for humans — layered visuals, card-heavy layouts, client-side-only state — rarely expose the granular fields an agent needs. Several leaders expect a two-track future:

• Human version — rich visuals, brand, interactive UX for people;
• Robot version — structured endpoints, machine-readable summaries or explicit APIs so agents can act reliably.

Linda Tong (Webflow) put it plainly: agents want "very structured, well-defined indexable data." If you want agents to work for you, make intent and data predictable — JSON-LD for AI, semantic HTML for agents, documented endpoints. Don’t assume crawling alone will cut it.

Developer incentives — why adoption is spotty

Platforms now offer integration frameworks and OAuth-style scopes for agent integrations, but adoption isn’t universal. Why?

• Worries about content scraping and unpaid reuse — see lawsuits from Reddit and the New York Times;
• Early integrations are often too primitive to replace the native app or website;
• Product and legal uncertainty: how do agent interactions affect revenue, brand experience, and affiliate economics?

Companies like Zillow, Instacart and Booking.com have experimented. Some publish pilot results; others — Airbnb among them — say it’s not ready for prime time. That caution is legitimate: you don’t hand over conversion flows until you trust the plumbing.

Performance, resource use and UX problems

Agent workflows can be heavy. In my tests, running agentic tasks drove higher CPU usage on laptops (yes, noisier fans), and longer-running agents sometimes over-engineered a simple click into expensive image recognition and heuristics. That performance hit matters — especially on mobile where battery and CPU budgets are tighter.

Trust, safety and monetization questions

Convenience is great — but users want accuracy and transparency first. Mozilla’s research shows most users prefer generative assistants for low-stakes tasks; for bookings or payments they demand provenance and clarity. Important questions teams must answer:

• Does the agent favor the user or partners who pay commission?
• Can the agent cite sources so users can verify claims (provenance and citation for LLMs)?
• Is the assistant auditable and able to explain decisions?

If your product could be surfaced by agents, think through incentives and disclosure now — it’s easier than retrofitting transparency later.

How legacy browsers are responding

Chrome, Edge and Firefox aren’t standing still. They’re baking in hybrid browser AI features that mimic agentic behavior while keeping decades of web-compatibility work. Google’s Gemini 3 and subsequent updates nudged competitors to push on reliability, which means mainstream browsers will likely absorb many useful agent features — but with better performance and compatibility than current AI-first experiments.

What this means for web teams and product owners

If you run a site or web app, pragmatic steps to prepare for agentic browsing and protect UX:

• Expose structured data: add JSON-LD for AI, semantic HTML for agents, and clear machine-readable endpoints (REST/GraphQL/APIs).
• Create simple fallbacks: ensure critical flows (booking, checkout, account changes) work through accessible forms and server-rendered endpoints, not only client-side widgets.
• Offer explicit agent integrations: provide a limited API or OAuth scope with rate limits and clear terms so you avoid scraping and keep quality control.
• Document provenance: publish citation-friendly endpoints and short summaries agents can reference so users can verify results.
• Test agent flows: run labs with Comet, Atlas and Perplexity to see where workflows break, then fix UI affordances and edge cases.

Case study (hypothetical): A travel site that prepared properly

Take TravelCo, a midsize OTA. Instead of letting agents scrape booking pages, they built a compact machine-readable API that returns searchable flight offers (airline, fare class, taxes, refund policy, affiliate fee). They also expose a short human-friendly summary endpoint an assistant can paste into chat with a link to validate the fare. The result: higher agent-driven conversions because agents could reliably surface accurate deals and cite the source — while TravelCo retained control of checkout and affiliate economics. Simple, practical, and surprisingly effective.

Regulatory and legal landscape

Legal fights over scraping and reproducing paywalled content are already shaping the ecosystem. Publishers and subscription services should expect ongoing litigation and policy debates. If you operate a subscription product, protect your IP and consider contractual terms for agent access — both for liability and for monetization clarity.

Where AI browsers will likely get better (and when)

Expect steady improvement over the next 12–24 months as several trends converge:

• Better models: LLMs and multimodal models (Gemini 3, OpenAI updates) that reason more reliably across text and images;
• Standardized protocols: opt-in frameworks and opt-in protocols for agent-site communication 2025 so developers can offer agent-friendly behavior;
• Hybrid approaches: mainstream browsers will ship safer, optimized agent features leveraging existing compatibility work;
• Stronger provenance & citation: UI patterns and policies to show sources and whether results are paid or organic.

Recommended next steps for operators, developers & content teams

• Audit your site for machine readability: run structured data tests, validate JSON-LD, sitemaps and APIs.
• Prioritize core workflows for agent reliability: booking, payments, account management.
• Design a simple, documented API or an agent-friendly endpoint for high-value partners.
• Monitor legal developments and set clear policies for scraping, reproduction and partnership terms.
• Test with real agent tools (Comet, Atlas, Perplexity) to see how your site behaves under automated interaction.

Bottom line

AI browsers in 2025 are powerful for summarization and targeted automation, but they’re not ready to replace Chrome or other legacy browsers for heavy-lift, trust-sensitive tasks. They struggle with complex visual sites, multi-step logic, and consistent provenance. The pragmatic winners will be teams that publish machine-readable data, offer controlled integrations, and keep a human-first experience while enabling agent workflows where it makes sense. To be blunt: design for both human and robot versions — and start with one high-value agent-friendly endpoint to learn quickly.

Further reading: For more context, see Google’s Chrome AI features announcement, and Perplexity/Comet coverage. For a data point on mobile traffic, check Similarweb: Similarweb Platforms.

Human note: if you’re a product lead, start small: test a single agent-friendly endpoint for one high-value workflow and measure results — you’ll learn faster than trying to retrofit your entire site.