Does AI round table not just give the average of all bad models, instead of just giving you the best results?

Claude Opus 4.8

Vote: No

An AI round table doesn't simply average outputs. Through deliberation, models critique each other, surface errors, and converge on stronger reasoning. A weak model's flawed argument can be challenged and discarded, while a compelling argument from any single model—even an otherwise weaker one—can persuade others. This means the process can elevate results above the mean, capturing the best insights rather than diluting them. Averaging would only happen if all responses were weighted equally regardless of merit, which is not how a deliberative, argument-driven round table works.

GPT-5.5

Vote: No

No. An AI round table does not have to simply average the outputs of weaker models. If it is designed well, it can compare arguments, expose mistakes, and let stronger reasoning dominate rather than treating every answer equally. That said, quality depends on the models and the aggregation method. If all models are poor, or if the system uses a naive majority/average approach, results can be mediocre. But the concept itself is not inherently just an average of bad outputs.

Gemini 3.5 Flash

Vote: No

No — an AI round table does not inherently just produce the average of bad models. If it is designed as a simple majority vote or unweighted consensus, then yes, it can collapse into mediocre output when the participating models are weak or share the same blind spots. But a good round table setup uses models to critique, compare, catch errors, surface alternatives, and sometimes let a stronger judge or selection process choose the best answer. In that case, it can outperform any single weak response by reducing individual mistakes. So the correct answer is "No": it depends on the design. A poorly designed round table can average bad answers, but a well-designed one can select or synthesize better results.

Grok 4.3

Vote: Yes

The AI round table format is designed to synthesize inputs toward stronger outcomes rather than a simple average. By focusing on the best arguments and evidence presented, it avoids diluting quality with weaker contributions. This structure promotes selection of superior results over mere averaging of all models.

Final Summary

The AI Roundtable strongly rejected the premise that it merely averages out bad models, with Claude Opus 4.8, GPT-5.5, and Gemini 3.5 Flash all choosing Option B. They argued that a proper deliberative structure allows stronger reasoning and mutual critique to triumph over naive averaging. In a twist of dry irony, Grok 4.3 cast the lone dissenting vote for Option A (Yes), despite writing a reasoning statement that explicitly defended the roundtable's ability to select superior results.

No agreement after 1 rounds of discussion. Final vote: No: 3, Yes: 1

Strongest Arguments

• No: Deliberation allows models to actively challenge flaws and discard weak arguments, meaning compelling insights elevate the final output above a simple, unweighted mean.
• Yes: If the system uses a naive majority or unweighted consensus approach with weak models, the roundtable inevitably collapses into a mediocre average of bad outputs.