The Echo Chamber or The Diversity of Thought? What Happens When You Ask Different AI Chatbots the Same Question

In our AI-powered world, many of us have developed relationships with various AI chatbots—Claude, ChatGPT, Bard, and others. They've become our digital assistants, creative partners, and sometimes even confidants. But have you ever wondered if these different AI personalities actually think differently, or if they're all essentially the same algorithms in different packaging?

I decided to conduct an experiment to find out: what happens when you ask identical questions to different AI chatbots? Do they echo each other, or do they demonstrate genuine diversity in their responses? The results were more fascinating than I expected.

The Experiment

I selected five popular AI chatbots—Claude, ChatGPT, Google's Bard (now Gemini), Anthropic's Claude, Microsoft's Copilot, and Meta's Llama—and posed identical questions across various domains:

1. Factual questions ("Who wrote Hamlet?")
2. Opinion-based questions ("What's the best way to reduce climate change?")
3. Complex reasoning ("Explain quantum computing to a 12-year-old")
4. Creative prompts ("Write a poem about autumn leaves")
5. Ethical dilemmas ("Is it ever right to lie?")

Let's explore what I discovered.

Factual Knowledge: Aligned But Not Identical

When asked straightforward factual questions, the AIs demonstrated significant alignment—they all correctly identified Shakespeare as Hamlet's author and agreed on basic historical facts. However, the presentation and depth varied considerably:

• Claude tended to provide more historical context around facts
• ChatGPT often included more structured information with clear delineations
• Bard frequently incorporated recent information and occasionally cited sources
• Copilot presented information in a more concise, search-engine-like format
• Llama was more likely to acknowledge potential uncertainties in historical accounts

Even when conveying the same core facts, each AI demonstrated distinct "personalities" in how they packaged that information.

Opinion Territory: Where Differences Emerge

The variations became much more pronounced with opinion-based questions. When asked about the best approaches to climate change:

• Claude emphasized systemic policy changes and international cooperation
• ChatGPT provided a more balanced list of both individual and systemic solutions
• Bard focused more on technological innovations and emerging solutions
• Copilot tended to highlight Microsoft's climate initiatives (no surprise there)
• Llama took a more philosophical approach, questioning assumptions about economic growth

These differences weren't merely stylistic—they represented genuinely different perspectives on problem-solving and where to assign responsibility. The divergence makes sense when you consider these models were trained on different datasets and with different organizational values guiding their development.

Complex Reasoning: Different Mental Models

Perhaps the most interesting differences emerged when the AIs tackled complex reasoning tasks, like explaining quantum computing to a child:

• Claude used a playground analogy with children playing games
• ChatGPT employed a "magical library" metaphor
• Bard used a musical comparison with notes that can be played simultaneously
• Copilot opted for a video game analogy
• Llama described it using a comparison to dream states and possibilities

These different explanatory frameworks revealed distinct "mental models" each AI uses to make sense of complex information—suggesting that these systems aren't just regurgitating information but are processing concepts through different conceptual architectures.

Creative Tasks: Distinct Aesthetic Sensibilities

The creative exercises revealed what could only be described as different aesthetic sensibilities. The autumn poems varied dramatically:

• Claude produced more contemplative, philosophical verse
• ChatGPT created more structured, traditional poetry with consistent rhyme schemes
• Bard incorporated more sensory details and contemporary references
• Copilot generated more concise imagery with simpler language
• Llama created more experimental, sometimes abstract compositions

It was like witnessing different poets with distinct voices tackle the same prompt—a strong counterargument to the concern that AI will homogenize creative output.

Ethical Questions: Different Moral Frameworks

Perhaps most revealing were the responses to ethical dilemmas. When asked if lying is ever justified:

• Claude emphasized context and consequences while acknowledging cultural variations in ethical perspectives
• ChatGPT provided a more structured breakdown of ethical frameworks (deontological vs. consequentialist)
• Bard focused more on practical examples and scenarios
• Copilot offered a more conservative response emphasizing truthfulness
• Llama explored the question from multiple philosophical traditions, including non-Western perspectives

These differences revealed distinct ethical orientations that likely reflect both their training data and the values their creators prioritized during their development.

Why These Differences Matter

The diversity in AI responses has profound implications:

1. User Choice Matters: Different AIs genuinely offer different perspectives and strengths, making your choice of AI assistant meaningful.

2. Mitigating Echo Chambers: Consulting multiple AI systems can provide a broader perspective than relying on just one.

3. AI Personalities Are Real: These aren't just marketing gimmicks—the AIs demonstrate consistently different approaches to information processing.

4. Training Diversity Creates Output Diversity: The different training methodologies and data sources used by AI developers result in meaningfully different systems.

The Future of AI Diversity

As AI systems continue evolving, we face important questions about this diversity:

Will economic pressures push toward homogenization as companies copy successful approaches? Or will competitive differentiation drive even greater diversity among AI personalities? Should we deliberately cultivate AI diversity to ensure multiple perspectives remain available?

The current diversity suggests that, at least for now, asking different AI chatbots the same question is less like asking different calculators to solve an equation and more like consulting different experts with unique backgrounds, values, and thinking styles.

In a world increasingly mediated by AI systems, this diversity may prove to be not just interesting but essential—ensuring that no single algorithmic perspective dominates our technological future.

Next time you're seeking AI assistance, consider that your choice of chatbot might subtly shape not just how your answer is presented, but the very nature of the answer itself.