AI is already generating new ideas, but human inspiration will continue to play a vital role in the age of creative machines. Here's why:

• AI as a tool: AI can be a powerful tool for generating new ideas, but it still requires human input to guide the process. Humans can provide the initial prompts, set the parameters, and interpret the results.
• Human empathy and understanding: AI can generate ideas, but it cannot yet fully understand the nuances of human emotion and experience. Humans are still needed to bring empathy and understanding to creative projects.
• Ethical considerations: AI can generate ideas, but it cannot make ethical decisions. Humans are needed to ensure that AI-generated ideas are used responsibly and ethically.

In short, AI can be a powerful tool for creativity, but it is not a replacement for human inspiration. Humans will continue to play an essential role in the creative process, even as AI becomes more sophisticated.

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AI aids to human inspiration may enable humans to keep up with new intelligent machines.

AI-generated scientific hypotheses lag human ones when put to the test," say researchers.

The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas

"Large Language Models (LLMs) have shown promise in accelerating the scientific research pipeline. A key capability for this process is the ability to generate novel research ideas, and prior studies have found settings in which LLM-generated research ideas were judged as more novel than human-expert ideas. However, a good idea should not simply appear to be novel, it should also result in better research after being executed. To test whether AI-generated ideas lead to better research outcomes, we conduct an execution study by recruiting 43 expert researchers to execute randomly-assigned ideas, either written by experts or generated by an LLM. Each expert spent over 100 hours implementing the idea and wrote a 4-page short paper to document the experiments. All the executed projects are then reviewed blindly by expert NLP researchers. Comparing the review scores of the same ideas before and after execution, the scores of the LLM-generated ideas decrease significantly more than expert-written ideas on all evaluation metrics (novelty, excitement, effectiveness, and overall; p < 0.05), closing the gap between LLM and human ideas observed at the ideation stage. When comparing the aggregated review scores from the execution study, we even observe that for many metrics there is a flip in rankings where human ideas score higher than LLM ideas. This ideation-execution gap highlights the limitations of current LLMs in generating truly effective research ideas and the challenge of evaluating research ideas in the absence of execution outcomes."

https://arxiv.org/abs/2506.20803v1