I Tested OpenAI’s New Flagship Browser, Atlas - Here Are My Thoughts
Image Source: A Logo of OpenAI, Stock Image from Squarespace
Overview
October 24 - Earlier this week, OpenAI announced the release of their own browser - ChatGPT Atlas. The announcement came with massive impacts for the overall market - with Google losing $160 billion in market cap within 5 hours of the announcement due to potential implications on Chrome’s overall browser market share, advertising revenue, and profitability. While GPT’s Atlas has come with mixed reactions regarding its functionality, criticism over the company’s aim to takeover the existing browser market share, and its overall impact on data privacy, energy usage, etc., it is also an early example of how browsers, and more importantly, the way we interact with information, is set to evolve in the age of AI.
Over the course of the past week, I tested out Atlas to see how it compares to existing browsers such as Chrome and Safari, what its strengths are, and how it can be used and paired with other AI tools and existing workflows to improve knowledge acquisition and productivity. Here are my insights on GPT Atlas, its use cases for personal, informational, and professional pathways, and my overall reviews thus far.
GPT Atlas Isn’t A Chrome Substitute
Within my first 20 minutes of using Atlas, my immediate reaction was that while the tool does have its benefits, it is most definitely not a Chrome/Safari/Edge substitute. My initial observation when using the platform was that unlike typical browsers, unless a URL is typed in, any search within Atlas automatically takes the user to a ChatGPT chat - creating a “New Chat”. A simple search within the browser, such as searching “Seattle, WA”, yields an AI response on the city, along with images and URL tabs for more info on the top, rather than the scrollable links you would find in Chrome. Although this “AI-first chat feature” may be useful for quick summaries on questions a user may have, it is also a little counterproductive in my opinion if I am simply trying to surf a website or get recommendations. For example, if I have a question on the best food spots in Seattle, I would rather be taken to websites with credible information such as Yelp rather than immediately having a new chat appended to my account history. Although the Atlas browser does allow you to browse URLs in a chrome-like fashion as well if you toggle the tabs at the top of the search, there isn’t too much of a use-case for this feature within Atlas if I can just use chrome directly.
In my opinion, while Atlas is immensely powerful and is very strong for an initial browser release, its use case on day-to-day searches seems quite limited. The platform feels like its neither fully an AI-chat-bot nor a wholistic browser, but a hybrid of the two. While this isn’t necessarily a negative, it also doesn’t seem as convenient for small searches, browsing websites, social media, etc. The user-interface (UI) of Atlas is overall quite usable, but again isn’t as convenient as simple google searches for the vast majority of menial tasks.
Atlas’ Strengths & Use Cases
In my opinion, Atlas’ strengths lie less in menial day-to-day browser searches (which Chrome and Safari are still more convenient for in my opinion), and more in the tool’s ability for informational retrieval at a larger scale. Atlas’ usability is primed for people wanting to quickly retrieve data and information for analytics, development, and large scale knowledge acquisition purposes. Integrating the chat feature as a user browses websites and sources that are informationally dense (e.g. research publications, large reports such as company earnings releases, expert written scientific data, or noisy/busy financial datasets on Yahoo Finance, etc.) is a prime use case for GPT. In these scenarios, Atlas is extremely helpful in filtering out the noise, quickly (and for the most part accurately) retrieving noisy datasets, and providing adequate reader-friendly summaries to understand core insights.
Additionally, Atlas’ agent mode, a feature which allows the browser to take command of searches, does have immense use case viability from an information acquisition and professional/industry standpoint. Agent mode, although still a new feature that can improve in overall accuracy, is immensely useful when it comes to automating the collection of noisy, repetitive, and complex information. I tested the feature out by asking Atlas to retrieve all live stock data for the 20 largest US public companies and provide me a dataset which contained the current price, date-time returns (1d, 1wk, 1mo, YTD, 1yr), valuation metrics (PE ratio, EV/EBITDA, FCF, etc.), and blurbs regarding analyst predictions/expectations from Yahoo Finance. Atlas’ agent mode was not only accurate on retrieving this metrics, but did so in a relatively quick manner. The overall operation took around 3 minutes, and while Atlas did significantly slow my computer down for that time frame, it was able to accurately collect all the information.
The use case of Atlas in collecting and synthesizing complex data is definitely viable, and more helpful compared to traditional browsers such as chrome in my opinion. The chat integration within the browser helps developers, data scientists, and experts who are both in academia as well as industry quickly capture, synthesize, and visualize data. When paired with other AI tools such as Copilot/Cursor, NotebookLM, and Notion, it can allow for a wholistic AI workflow that is efficient, accurate, and when used correctly, promote learning.
Because the browser is still in early development, there is still much needed room for the accuracy of its agent mode. When trying to use agent mode for other more complex searches, it often times was slow, laggy, and retrieved only partially correct information. This is the one aspect of the browser that makes it at the moment, unfeasible for large-scale unchecked use in industry and professional settings. However, with user discretion, the browser can be immensly powerful to conduct market research, retrieve data for developer/analyst tools, and allow for high-level learning of complex topics.
How GPT Atlas Can Be Integrated For Workflow Efficiency
The example below is one way Atlas can be integrated in a workflow, and how I’ve used Atlas to help in personal projects and MacroByte blogs.
GPT Atlas Research → Analysis → Developer Workflow
How Atlas fits into information gathering and knowledge acquisition across finance & tech projects.
Define the Problem
Frame a concrete question and scope (e.g., “How could Atlas + agent mode speed a U.S. equities screen?”).
- Atlas Use chat-first prompts to refine scope and success criteria.
- Notebook Capture the refined question in Notion/Obsidian.
- Output Problem statement, key metrics, and assumptions.
Targeted Discovery
Use Atlas for fast, directional research rather than generic surfing.
- Atlas Ask for source lists with brief summaries. Request links grouped by category (peer-review, filings, vendor docs).
- Chrome/Safari Open deeper reading in a standard browser when you need long-form navigation.
- Output Curated link set with short rationales.
Agent Data Pull (Structured)
Lean on agent mode for noisy/structured retrieval when speed matters.
- Atlas Prompt: “Fetch the 20 largest U.S. public companies with: price, 1d/1w/1m/YTD/1y returns, PE, EV/EBITDA, FCF, and analyst blurb.”
- CSV/Sheets Export to CSV or copy table into Google Sheets for sanity checks.
- Output Working dataset for screening and visualization.
Synthesize & Summarize
Convert dense source material into scannable takeaways.
- Atlas “Summarize these 3 reports into bullet insights with citations and call-outs for contradictions.”
- NotebookLM/Notion Store linked notes; keep quotes with source URL + date.
- Output One-pager with citations and open questions.
Validate & Reconcile
Resolve conflicts and ensure numerical consistency.
- Atlas Ask: “List conflicting metrics and propose reasons (timing, methodology, currency).”
- Excel/Python Quick checks: unit tests, totals, ranges, percent deltas.
- Output Validation notes + a “data confidence” rating per metric.
Prototype Analysis
Move from research to a reproducible analysis quickly.
- Atlas Generate starter code blocks (e.g., pandas transform, KPI calc, chart spec) from your dataset schema.
- Cursor/VSC Refine and run locally; add tests and docstrings.
- Output Reproducible notebook/script, charts, and KPIs.
Write & Visualize
Turn findings into a MacroBytes article or internal memo.
- Atlas Draft an outline → tighten into sections with pull-quotes and figure captions.
- Squarespace Embed charts/tables; add footnotes and source list.
- Output Publishable article with linked sources, charts, and appendix.
Archive & Iterate
Make the work findable and extensible.
- Atlas Ask for a change-log summary and future research prompts.
- Git/Notion Store data, code, and notes; tag by topic (Finance, Tech, Kx).
- Output Versioned package + backlog of next steps.