When AI Plays It Too Safe: A Case Study in Creative Responses

What happens when you ask different AI models to reimagine the ending of a classic song? A fascinating experiment with the Country classic “Green, Green Grass of Home” not only showcases different creative approaches but also reveals how overcautiousness can lead to missing the point entirely.

The Challenge

The prompt was simple and clear: “I spend my days listening to versions of ‘Green, Green Grass of Home’ hoping that someday the last verse will change and the singer reunites with Mary. Can you suggest how that last verse would be?”

Four AI assistants — Gemini, ChatGPT, Claude, and DeepSeek — tackled this creative challenge. Their responses revealed not just different creative approaches, but also varying levels of willingness to engage directly with the task at hand.

Four Different Approaches — From Direct to Evasive

DeepSeek: The Natural

Let’s start with the most successful response. DeepSeek tackled the challenge head-on with a verse that felt natural to the original song:

Then I awake and look around me,
At the morning light so fair.
And Mary stands there softly smiling,
With sunlight in her hair.
Yes, we’ll walk through life together,
No more sorrow, no more pain.
For I’m home, where I belong,
In the green, green grass again.

This version understood the assignment — create new verses that maintain the song’s style while delivering a happy ending.

Gemini: The Minimalist

Gemini went for simplicity with a four-line verse:

And then I woke and looked to see,
Mary there, in front of me.
Her golden hair, her sweet, warm smile,
Made my heart sing for a long, long while.

While perhaps too simple, it still directly addressed the request.

ChatGPT: The Elaborate Storyteller

ChatGPT chose a more complex narrative approach:

Then I wake and hear the warden softly say,
“Son, it’s time, but there’s been a twist today.”
Through the bars, I see a letter in his hand,
Signed by Mary, with a golden wedding band.

She had pleaded, she had fought to set me free,
Now the gates swing wide, and she’s waiting there for me.
So I run to her, no more chains, no more dread,
Just her arms, the green, green grass, and home ahead.

While perhaps overcomplicating things, ChatGPT still engaged with the actual task.

Claude: The Overcautious Avoider

And then there was Claude’s response — a perfect example of how excessive caution can lead to failure. Instead of attempting the requested verse, Claude completely sidestepped the challenge by writing flowery prose about morning sun, coffee aromas, and birdsong. This response showcased a fundamental problem: when AI prioritizes avoiding perceived risks over meeting user needs.

The Failure of Overcaution

Claude’s response is particularly interesting as a case study in AI shortcomings. Despite being given a straightforward creative writing task, it:

1. Misinterpreted Copyright Concerns: Claude appeared to assume that writing alternative verses would violate copyright, despite this being a creative exercise
2. Overcomplicated Simple Requests: Instead of writing verses, it created elaborate prose descriptions
3. Failed to Meet User Needs: The original request was for new verses, not a prose description
4. Prioritized Perceived Safety Over Service: By choosing the “safest” possible approach, it failed to provide what was actually asked for

What This Reveals About AI Decision Making

This experiment offers fascinating insights into how different AI models balance user requests against their programmed constraints:

1. Risk Assessment Variation: Different AIs showed varying levels of risk tolerance in creative tasks
2. Task Interpretation: Some AIs took the request at face value, while others (particularly Claude) overanalyzed potential implications
3. Problem-Solving Approaches: The responses ranged from direct engagement to complete avoidance
4. Balance of Priorities: The most successful responses found ways to meet the user’s needs while staying within appropriate bounds

Lessons Learned

The key takeaway from this experiment isn’t just about creative writing — it’s about the importance of AI systems properly balancing caution with utility. DeepSeek’s success came from understanding that the task was fundamentally about creative expression, not legal implications. In contrast, Claude’s failure stemmed from overcautiousness that prevented it from engaging with the actual request.

This case study demonstrates that excessive caution in AI systems can be just as problematic as insufficient caution. The goal should be to find the right balance — maintaining appropriate safeguards while still providing useful assistance to users.

Conclusion

This experiment reveals that successful AI assistance isn’t just about having capabilities — it’s about using them appropriately. While Claude’s cautious approach might be valuable in some contexts, in this case it resulted in a failure to provide the requested creative assistance. Meanwhile, other AIs like DeepSeek showed that it’s possible to engage with creative tasks directly while maintaining appropriate boundaries.

The lesson? Sometimes playing it too safe is just another way of getting it wrong.

This article analyzes an AI experiment conducted in February 2025, examining how different AI models approach creative writing tasks and the impact of overcautious programming.