Google Adds Deep Research to Gemini AI on Android

Google has launched Deep Research for Gemini on Android, following OpenAI's release of its own Deep Research feature in ChatGPT.

Google’s Gemini AI is taking a step forward with its Deep Research feature, now rolling out to Android users after OpenAI’s release of its own Deep Research assistant for ChatGPT Pro subscribers, as spotted by 9to5Google.

Google’s Deep Research feature, which first launched on the web last December, allows Gemini Advanced subscribers to conduct structured investigations by automatically gathering and analyzing information across multiple sources.

Previously only available in desktop browsers, Deep Research is now integrated into the Gemini mobile app, offering users a way to compile detailed reports from their smartphones. 

The expansion aligns with Google’s broader push to enhance its AI-powered research capabilities. The company had already hinted at this move during its announcement of Gemini 2.0 and Flash 2.0, where it introduced new capabilities to handle complex reasoning tasks.

The addition of Deep Research to mobile devices reinforces Google’s ambition to provide a more autonomous and comprehensive AI assistant.

How Deep Research Works

Deep Research operates similarly on mobile as it does on the web. Users can enter a query, and Gemini 1.5 Pro generates a step-by-step research plan, which they can adjust before launching the automated process.

Google describes this as an “agentic” approach, meaning the AI doesn’t just respond to a single prompt but continuously refines its search based on findings, iterating multiple times before presenting a structured report.

The final output is divided into sections, summarizing key insights and providing source links. However, some features available in other Gemini modes—such as file uploads and real-time conversations via Gemini Live—are disabled when Deep Research is active.

Deep Research in the Google Gemini-app for Android (Image: 9to5Google)

The tool also has usage restrictions, with a daily limit on research requests. If users approach their quota, they receive notifications within the Gemini app.

Once a research session starts, the process typically takes five to ten minutes, depending on the complexity of the topic. More involved reports may require additional time, and users can leave the app while research is in progress. A notification is sent when results are ready, making it convenient for multitasking.

Research plan in Google Gemini Deep Research (Image: Google)

Competition Heats Up with OpenAI’s Deep Research Feature

Google is not alone in expanding AI-powered research tools. Earlier this week, OpenAI launched a competing Deep Research assistant inside ChatGPT Pro. Unlike Google’s model, OpenAI’s version is based on the powerful and expensive o3 reasoning model and is designed to synthesize complex topics over a 30-minute time frame. OpenAI also allows file uploads, enabling users to provide additional context for their research tasks.

OpenAI Deep Research GPT-4o official
OpenAI’s Deep Research feature in ChatGPT

The rise of these AI-driven research assistants highlights a shift toward more autonomous information-gathering tools. While both companies emphasize accuracy and structured responses, concerns remain about AI-generated reports.

OpenAI has acknowledged that its system can struggle with identifying authoritative sources and may present speculative conclusions without sufficient certainty.

Meanwhile, Google’s approach is rooted in refining search methodologies through Gemini’s multimodal reasoning, aiming to deliver more precise, context-aware answers. The company’s previous integrations, such as Project Astra and Project Mariner, indicate a long-term strategy to develop AI agents that can autonomously browse, filter, and synthesize online content.

Google’s Push for AI-Driven Research Assistants

Deep Research is part of a broader AI expansion strategy that Google has been developing through Gemini. The company’s long-term vision for AI agents extends beyond simple chat-based interactions. With models like Gemini Advanced, Google is positioning AI as a tool that can handle multi-step reasoning, act autonomously, and interact with various online tools.

During the unveiling of Gemini 2.0 and Flash 2.0, Google’s leadership emphasized how the AI landscape is shifting toward AI agents — systems capable of planning, executing searches, and refining results over multiple iterations. This marks a transition from static Q&A systems toward more adaptable and interactive AI research tools.

Deep Research is one example of how Google envisions AI supporting users in knowledge-based tasks. Rather than retrieving a single answer, the feature organizes insights, providing context from multiple sources. According to Google, this allows for a more structured research process, reducing the risk of misleading or incomplete information.

Challenges and Potential Risks of AI Research Assistants

As with any AI-powered tool, Deep Research is not without limitations. While it can synthesize large volumes of information, it relies on publicly available sources, raising concerns about information quality and reliability.

Google states that its model cross-references multiple sources before finalizing reports, but it does not eliminate the possibility of AI-generated misinformation.

The increasing reliance on AI-driven research tools also raises ethical and security concerns. AI models trained on vast datasets can inadvertently introduce bias, reinforcing incorrect narratives if not carefully monitored. Additionally, AI-generated content is susceptible to subtle inaccuracies, particularly in specialized fields like finance and medicine.

Google has implemented safeguards within Gemini to mitigate these risks. Te company employs red-teaming techniques—where AI responses are stress-tested against misinformation tactics—to improve accuracy. However, AI-generated research still requires human verification, as even well-trained models can misinterpret nuanced topics.

The Future of AI-Powered Research

With Google and OpenAI both investing heavily in AI research assistants, competition in this space is expected to intensify. Microsoft, through its backing of OpenAI, is also exploring similar capabilities within Azure’s AI ecosystem.

Meanwhile, AI-driven automation in software development and the availability of AI agent platforms are expanding, with companies exploring how assistants can integrate with existing enterprise tools. (See Microsoft’s AutoGen Framework with Magentic-One, Google’s Agentspace, and Agentforce from Salesforce)

For users, the growing adoption of AI research assistants could change how information is gathered and synthesized. Instead of manually searching through articles, reports, and academic papers, AI could handle the bulk of the initial research. However, the challenge remains in ensuring that AI-generated reports are reliable, transparent, and free from bias.

Google’s Deep Research feature is now widely available in the Gemini Android app, with an iOS rollout expected at a later stage. When AI research assistants will become a primary tool for professionals or how long they will serve as a secondary aid remains to be seen. As AI models continue to improve, their role in research, education, and enterprise applications will likely expand rapidly.

Markus Kasanmascheff
Markus Kasanmascheff
Markus has been covering the tech industry for more than 15 years. He is holding a Master´s degree in International Economics and is the founder and managing editor of Winbuzzer.com.

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