Semantic Drift: The Next Blindspot in AI Evaluation
The hidden reason GPT-5 feels blander than GPT-4
The Distinction
Current benchmarks focus on factual accuracy and hallucinations. But new evidence suggests another failure mode. Semantic drift: where outputs remain factually correct but lose the original purpose or intent.
Example: Descartes' "Cogito, ergo sum" recast as leadership advice about confidence. Factually fine, semantically hollow.
The Metric
We call this Purpose Fidelity: the degree to which AI preserves the meaning, context, and intent of source material. Early experiments show that semantic fidelity degrades far faster than factual accuracy over recursive generations.
Why Drift Happens
Semantic drift isn't random. It emerges from three converging forces:
Training Bias: pretraining on dominant narrative forms (e.g., explanatory or business-oriented text) nudges outputs into those grooves.
Safety Smoothing: fine tuning pushes models toward "safe" generalities, often flattening nuance.
User Convergence: most users lean on default prompts, reinforcing predictable phrasing and compressing variance.
Together, these create a pipeline from originality, to compression, to semantic collapse.
The Two Paths
For most users, this means convergence: voices and ideas flatten into sameness.
But early signs suggest a minority who approach AI as a thinking partner rather than a shortcut. They generate expansion instead: new metaphors, new language, new thought patterns. (One hypothesis: cognitive diversity, including neurodivergence, may play a role. But this requires testing.)
Why It Matters to AI Companies
Benchmarks miss it: Your evals show models "working" while meaning silently collapses.
Adoption risk: If users sense outputs feel hollow, trust erodes.
Differentiation risk: Companies that solve drift will own the narrative of "authentic AI."
Epistemic liability: Recursive retraining on semantically drifted outputs risks long-term model integrity.
What to Track
A Drift Index: monitoring Purpose Fidelity across domains and over recursive generations.
What to Build
Interfaces that surface intent, not just output.
"Friction by design" to disrupt over-compression.
Adaptive pluralism: multiple stylistic/semantic modes rather than a single flattened voice.
Framing Line for Execs
Benchmarks measure models. Drift measures users. If you're not measuring drift, you're flying blind.
Implications
Ignore drift, and you risk flooding the ecosystem with factually correct but semantically hollow text. Solve drift, and you not only protect epistemic stability but unlock new forms of human–AI co-thinking.
Further Resources:
[The Next Blindspot in AI Evaluation] - Medium
[Semantic Drift PDF Archive] - Offbrandguy
[Semantic Drift Full Documentation] - Zenodo
[Semantic Drift Research Notes] - Figshare