OpenAI’s deep research aims to outthink analysts

OpenAI's new deep research tool promises to slash weeks of data analysis into hours by combining automated processing with sophisticated reasoning capabilities.

The tool departs from traditional AI assistants, using multi-step analysis and interactive questioning to generate structured reports. OpenAI says it’s built for people who do intensive knowledge work in areas like finance, science, policy and engineering. The launch signals OpenAI's strategic expansion into the growing market for AI-powered research solutions.

"This isn't just about automation—it's about improving AI's advanced reasoning ability in new ways," says IBM Principal Research Staff Member Chuang Gan, who has been leading the development of another system called Satori. "By training AI models to solve complex problems with large-scale reinforcement learning, we can accelerate breakthroughs that would otherwise take much longer."

The drive to enhance AI's scientific capabilities has sparked an intensifying race among tech giants, each vying to unlock AI's potential as a research powerhouse. Deep research breaks down complex queries into smaller steps, searches the web, analyzes documents through multiple passes and synthesizes the information into comprehensive reports. Unlike standard AI responses, deep research actively browses online sources, checks multiple references and builds its answers iteratively while showing its work through a visible chain of thought.

The tool combines web browsing, document analysis and specialized functions to tackle complex tasks. When given a question, it searches extensively through available sources and verifies information across multiple references, and it can handle tasks ranging from academic research to market analysis to technical documentation.

OpenAI claims that deep research is particularly effective at extracting specific details from large documents, comparing information across sources and providing well-documented conclusions. The system is currently available to Pro users with a limit of 100 monthly queries, reflecting its compute-intensive nature, though the company says it will gradually roll out to other subscription tiers.

“Deep research can identify, assess, reassess and synthesize information for knowledge-intensive tasks in mere minutes—functions that would typically take humans several hours to complete,” says Timothy DeStefano, Associate Research Professor at Georgetown University’s McDonough School of Business. “This represents a potentially significant productivity boost for users, which could enhance corporate innovation.”

He notes, however, deep research may struggle with questions requiring proprietary data, making its effectiveness dependent on the specific data needs of its application.

“While the USD 200-per-month price tag is affordable for many professionals in corporate America, it may be cost-prohibitive for some individuals or firms, potentially leading to disparities in productivity,” he added. “Additionally, given the tool's skill-biased nature, it could contribute to wage disparities, echoing patterns observed during the last digital revolution.”

As OpenAI expands its deep research initiative, IBM is taking a different approach to advancing AI reasoning systems with its Granite family of models. Meanwhile, Gan and other researchers have developed Satori, an open-source AI framework that enhances reasoning through self-reflection.

Like OpenAI’s reinforcement learning from human feedback (RLHF), which was instrumental in training ChatGPT, Satori improves its reasoning abilities in two phases. First, it fine-tunes its analytical framework on a smaller scale. Then it uses reinforcement learning to refine its reasoning across larger datasets.

"Satori is designed to enhance AI's ability to conduct scientific discovery and problem-solve," Gan explains. "It's not just about automation, but about refining AI's capacity to apply learned knowledge to new domains."

Gan says the emergence of deep research and Satori highlights a growing focus on AI tools for research and commercial applications. In finance, AI-powered quant trading tools already comb through massive datasets to identify patterns faster than human analysts. Similarly, AI assistants are reshaping legal research, summarizing case law, drafting legal arguments and even predicting case outcomes.

One of the biggest markets for AI-driven research is pharmaceuticals, where companies are leveraging AI to identify new drug candidates. Venture capital firms and private equity investors are also eyeing AI research tools to streamline due diligence processes. Rather than spending weeks analyzing financial and market reports, financials and competitive landscapes, firms could use AI to generate analyses that highlight key risks and opportunities.

OpenAI’s move into this space follows Microsoft’s Copilot for Finance and Bloomberg’s GPT-powered finance assistant, which can automate complex research tasks. Meanwhile, China’s DeepSeek is taking a different approach with DeepSeek-R1, an open-source model built for deep reasoning in math, coding and language. Unlike its closed-source rivals, DeepSeek-R1 is freely available under an MIT license, making it a cost-effective option for researchers and businesses. As AI-powered research heats up, these competitors are pushing the boundaries of machine reasoning—and changing how information is analyzed.

Some experts warn that such powerful automation tools could have unintended consequences. "If we're not careful, AI will turn into a research parrot—it'll repeat what's already out there instead of digging deeper," IBM Senior Research Scientist Marina Danilevsky said on a recent episode of IBM’s Mixture of Experts podcast.

The reliability of AI-generated research presents another consideration. IBM Senior Research Scientist and Master Inventor Nathalie Baracaldo Angel emphasizes the need for careful vetting. "It's important to know where your data comes from," she says. "If we don't attribute research to a particular system, we risk amplifying mainstream narratives while ignoring the outliers—the very things that lead to real innovation."

Content creation patterns could also shift in response to these tools. In the same way that websites previously optimized for search engines, researchers and others might begin tailoring their work for AI summarization. "Before, it used to be SEO for search," Danilevsky says. "Now, it's going to be SEO for agents. If my document is more likely to be summarized by OpenAI's research tool, I have an incentive to tailor it for AI consumption rather than for human readers."

IBM Distinguished Engineer Chris Hay points to broader implications for knowledge creation. "If people rely too heavily on AI-generated research, we might see a 'bubble' effect where only the most commonly cited sources get reinforced," he says. "Valuable outlier insights might be ignored."

Despite the promise of AI-driven research tools like deep research and Satori, experts caution that automation alone isn’t enough. While AI can process vast amounts of data, extract insights and refine its own reasoning, the human element remains critical—especially when it comes to navigating new research frontiers and ensuring that novel insights aren’t overlooked.

“To have really fresh information, then we do really need humans in the loop,” Baracaldo Angel says. “A lot of things that are going to be cutting-edge in organizations, they'll still have humans involved and talking to other people—and I think that's actually part of the magic.”