Can Agentic AI Really Replace Scientific Research?

Deep Research: A New Model from OpenAI

DeepSeek, a Chinese AI company, recently topped the Apple App Store’s free downloads in the U.S. with its new AI assistant, R1. The model, developed at a fraction of the cost of OpenAI’s chatbot, received rave reviews. President Trump called it a “wake-up” call for the entire American tech industry. Now, just days later, OpenAI is striking back with its new agentic capability, Deep Research.

What is Deep Research?

Deep Research is a new model accessible through a button on the ChatGPT app and website (available to Pro users only, for now). It can analyze, synthesize, and interpret massive amounts of varying data types (text, graphs, PDFs, and more) in five to 30 minutes, compared to hours or days of work by a human. Developers say it overcame the gap of its previous reasoning model and adapted to technical domains, but it lacks the aptitude to tackle real-world contextual challenges.

Can Agentic AI Really Produce Novel Understandings?

OpenAI’s choice of name, Deep Research, is provoking. Are these generative AI reports really research? And more specifically, can they replace scientific research? “The ability to synthesize knowledge is a prerequisite for creating new knowledge,” OpenAI wrote in its introductory post. “For this reason, deep research marks a significant step toward our broader goal of developing AGI [artificial general intelligence], which we have long envisioned as capable of producing novel scientific research.”

Can Agentic AI Really Replace Scientific Research?

Researchers think of their work as more than synthesis and rigorous repetitive validations; it often comes down to that moment of inspiration, a spark of illusive creativity that keeps us going. Not so different, we often feel, from the spark of artistic creativity. OpenAI has already challenged the latter with Sora. A year later, almost to the day, it is challenging our concept of scientific creativity.

Is Reasoning and Consolidating Enough?

Is reasoning and consolidating vast amounts of methodologies and existing hypotheses enough to make the leap to a discovery? Think about past revolutionary discoveries, like IVF. Could an AI suggest such a revolutionary way of conception, unlike any previously learned data from the natural world? Similarly, the amazing ability to edit our DNA with CRISPR, used in biotech today from gene therapy in medicine to agriculture, came about through an original idea to emulate, in a sense, bacterial mechanisms – ingenious, and original.

Conclusion

Undoubtedly, agentic AI could expedite the process of applying known methodologies to data and allow quicker steps down a previously planned route. It can be a wonderful analyst, or assistant. However, at least for now, these models haven’t demonstrated the ability to come up with new methodologies – and challenge existing, vast, knowledge or presumed truths. If that day comes, it will truly be revolutionary.

FAQs

Q: Can agentic AI really replace scientific research?
A: No, at least not yet. While it can analyze and synthesize vast amounts of data, it lacks the ability to come up with new methodologies and challenge existing knowledge.

Q: What are the limitations of agentic AI?
A: Agentic AI is prone to hallucinations and overconfidence, and it lacks the ability to convey uncertainty of its own findings.

Q: Can agentic AI be used for action-taking tasks?
A: Yes, OpenAI has already rolled out Operator, a model that can perform actual real-world tasks for users. However, this raises concerns about the potential for AI to make decisions without human oversight.