Deep Research Isn’t Just a New AI Function. It’s the Beginning of the End for Intellectual Work as We Know It

Grok 3 is Elon Musk’s new AI model. Among its features is a capability called Deep Search, which bears a striking resemblance to Google’s Deep Research functionality, which OpenAI has also adopted. In fact, nearly all major AI companies have recently announced similar features.

• OpenAI offers Deep Research.
• Google rolled out its own version in Gemini.
• Perplexity has been refining this functionality for several months.

This represents a significant shift in AI development, moving beyond mere incremental improvements. These advanced systems can browse the web, analyze multiple sources, synthesize information, and produce detailed reports on various topics. What’s more, they do so while exhibiting a level of sophistication that closely rivals many human analysts across different fields.

The contrast with traditional answers is substantial. Rather than returning results in the form of tokens in seconds, these systems can generate pages of information within minutes. They don’t simply provide a list of related links. Instead, they comprehend complex questions and deconstruct them into manageable components. The systems also investigate each aspect by consulting numerous sources, and compile a coherent analysis complete with cited references. More importantly, they do all this in less than 10 minutes.

The results are impressive. OpenAI claims that its Deep Research can accomplish what would typically take professional analysts several days in half an hour. While there can be occasional errors, such as factual inaccuracies or citing non-existent sources, the overall quality is enough for many practical applications.

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This highlights a significant challenge in today’s intellectual work for junior analysts in consulting firms, researchers reviewing literature work, lawyers preparing draft reports, and financial advisors analyzing companies.

They spend a substantial part of their time collecting, synthesizing, and presenting information from numerous sources–just like any deep research system.

Importantly, these systems aren’t going to completely replace intellectual workers. They still have key limitations, including:

• They can’t access private or unpublished information.
• They occasionally confuse sources or draw erroneous conclusions.
• They lack the expert judgment required for certain analyses.

However, they can automate much of the repetitive and “low-level” work that many professionals handle today.

This leads us to a paradox. Deep research systems will undoubtedly increase the productivity of highly qualified workers, allowing them to enhance their skills. Yet, they also threaten the jobs that serve as entry-level training grounds for aspiring experts.

Deep research systems have the potential to disrupt career paths in any knowledge-based industry.

This development is yet another example of how AI isn’t only automating manual labor but also entering realms people once believed were reserved for human intellect. The question is no longer whether AI can perform that intellectual work. Instead, it’s how much of that work will remain economically viable when done by humans.

Companies that choose to ignore these AI capabilities, whether out of ignorance, cynicism, or pride, will be at the greatest risk of being left behind. The rest face the crucial challenge of managing this transition, which may render many roles people once thought were immune to automation obsolete.

Image | Milad Fakurian

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