There’s a quiet failure mode emerging in the age of AI.
It’s not hallucination.
It’s not bias.
It’s not even over-automation.
It’s something more subtle—and potentially more dangerous:
Averaging.
As companies increasingly rely on large language models (LLMs) to generate ideas, shape strategy, and guide decisions, they are drifting toward a shared center of gravity. Outputs become more polished, more coherent, more correct—and at the same time, less distinct, less risky, and less strategically interesting.
AI makes every team more productive while making every company more similar.
That is the paradox. And in domains where differentiation is the only moat, it is a serious problem.
What is “Averaging”?
Averaging is the tendency of LLM systems and workflows to produce outputs that converge toward high-probability, consensus-compatible responses—suppressing outliers, minority perspectives, and strategically differentiating ideas.
In simpler terms: LLMs compress not just knowledge—but variance.
They don’t just summarize what is known.
They standardize how it is expressed.
They flatten how it is applied.
This shows up as outputs that are:
– Fluent but familiar
– Structured but predictable
– Correct but forgettable
Why Averaging Happens
This is not a bug. It is a feature of the system.
1. Objective functions reward probability, not originality
LLMs are trained to predict likely continuations. The highest probability answer wins.
2. Alignment pushes toward safety
Models are optimized to be helpful and agreeable. That often suppresses contrarian thinking.
3. UX encourages convergence
Users ask for “the best answer,” not multiple competing ones.
4. Humans over-trust fluency
The more polished the output, the more we accept it—regardless of originality.
Where Averaging Breaks
In operational tasks, averaging is useful.
In marketing, strategy, and creativity, it is dangerous.
Marketing is not about correctness.
It is about differentiation.
The best campaigns are not the most probable.
They are the most distinctive.
AI will not make marketing wrong.
It will make it indistinguishable.
The Missing Variable: Taste
Most conversations about AI ignore the most important human contribution: Taste.
Taste is not preference.
It is:
– The ability to recognize what is interesting
– The instinct to choose what is non-obvious
– The judgment to reject what is technically correct but strategically dead
LLMs recognize patterns.
Taste breaks them.
Taste is what prevents convergence.
Taste is what creates advantage.
Taste is not the average of what worked.
It is the selection of what shouldn’t have worked—but does.
How Averaging Shows Up
You can see it everywhere:
– Brand positioning that sounds interchangeable
– Personas that feel generic
– Campaign ideas that are “good” but forgettable
– Messaging frameworks that mirror competitors
Each output passes individually.
Together, they erase differentiation.
The Organizational Risk
LLMs are becoming consensus engines.
They validate executive assumptions.
They reinforce safe decisions.
They give authority to conventional thinking.
AI doesn’t just average ideas.
It averages conviction.
How to Overcome Averaging
1. Separate divergence from convergence
2. Prompt for conflict, not answers
3. Inject specificity
4. Use multiple perspectives
5. Measure distinctiveness
6. Use AI as a dissent engine
You do not beat averaging by asking for creativity.
You beat it by designing for disagreement.
The Real Opportunity
The future is not AI replacing humans.
It is AI + Taste.
AI provides scale and pattern recognition.
Humans provide judgment and differentiation.
AI shows you what is common.
Taste tells you what matters.
Final Thought
We are entering a world where everyone can generate “good” outputs.
Good is no longer enough.
Advantage comes from deviation.
The companies that win will not be the ones that follow AI.
They will be the ones that know when to ignore it.