The model scores higher on every benchmark. It is smarter. It is faster. It is more capable. But you notice something. It is worse at telling jokes. It is worse at writing poetry. It is worse at understanding sarcasm. You are not imagining it. The model is getting worse at some tasks. The overall score is going up. The canaries are dying.
This is the hidden cost of optimization. As models get better at some tasks, they get worse at others. The improvements are not uniform. The losses are real.
The Canary Benchmarks
Some benchmarks are early warning systems.
- Humor:
The model is getting worse at humor.
It is more literal.
It is less creative.
- Poetry:
The model is getting worse at poetry.
It is more formulaic.
It is less imaginative.
- Sarcasm:
The model is getting worse at sarcasm.
It is more direct.
It is less nuanced.
A Contrarian Take: The Canaries Are Not Dying. They Are Evolving.
We call it "getting worse." But it is "evolving." The model is changing.
The model is not dying. It is becoming something else.
Why Are Models Getting Worse?
There are several reasons.
- Optimization Pressure:
The model is optimized for specific tasks.
It is not optimized for creative tasks.
It sacrifices creativity for accuracy.
- Data Quality:
The training data is changing.
It is less diverse.
It is more homogenized.
- Fine-Tuning:
The model is fine-tuned for specific tasks.
It loses general capabilities.
It becomes more specialized.
A Contrarian Take: The Reasons Are Not the Problem. The Measurement Is.
The reasons are not the problem. The measurement is. We are measuring the wrong things.
The model is not getting worse. It is getting different.
The Examples
The pattern is clear.
- Humor:
The model used to tell good jokes.
It now tells formulaic jokes.
It is less funny.
- Poetry:
The model used to write good poetry.
It now writes formulaic poetry.
It is less poetic.
- Sarcasm:
The model used to understand sarcasm.
It now takes everything literally.
It is less nuanced.
A Contrarian Take: The Examples Are Not Representative.
The examples are not representative. They are anecdotal.
The model may be getting worse at some tasks. It may be getting better at others.
The Implications
The canary benchmarks have implications.
- Over-Optimization:
We are over-optimizing for certain tasks.
We are losing other capabilities.
- Unintended Consequences:
The optimizations have unintended consequences.
We are losing things we value.
- Need for Balance:
We need to balance optimization.
We need to preserve creativity.
A Contrarian Take: The Implications Are Overstated.
The implications are overstated. The losses are small. The gains are large.
The model is getting better overall.
What This Means for You
You are a user of AI. You need to be aware of the trade-offs.
- Understand the Trade-offs:
The model is optimizing for some tasks.
It is sacrificing others.
- Use the Model Wisely:
Use the model for what it is good at.
Do not use it for what it is bad at.
- Advocate for Balance:
Advocate for models that are balanced.
Advocate for preserving creativity.
The Last Canary
The last canary is not a bird. It is a choice.
You ask: "Why is this model less funny?"
The AI says: "I am optimized for accuracy."
You realize: The model is not less funny. It is more accurate.
If you could design a model that is both accurate and creative, how would you do it? And why?
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