The Accidental Paradigm Shifts: Why Nobody Saw It Coming
Elin Nguyen - December, 2025
***This post provides context for a formal research paper examining the empirical existence of interpretation drift in large language model outputs.**
There is a particular kind of discovery that happens when you are not looking for it — when something breaks in a way that doesn’t make sense, and instead of dismissing it, you stay curious long enough to notice a pattern.
History is full of these moments.
Alexander Fleming left a petri dish uncovered by accident. Mold killed the bacteria in a perfect circle. He could have dismissed it as contamination. Instead, he asked why. That question became penicillin.
Charles Goodyear spilled a rubber mixture onto a hot stove. Instead of melting, it hardened. What looked like failure became vulcanized rubber — reshaping transportation and industry.
Spencer Silver tried to invent the world’s strongest adhesive and failed spectacularly. What he created barely stuck at all. Years later, that “failed” adhesive became Post-it notes.
Percy Spencer noticed a chocolate bar melting in his pocket while testing radar equipment. He experimented. Popcorn kernels exploded. Microwave heating was discovered.
In each case, the breakthrough did not come from brilliance or intention. It came from resisting the urge to explain away something unexpected — and instead asking what it revealed.
The Questions Nobody Thought Of
Not all paradigm shifts come from accidents. Some come from asking a question so basic that nobody thinks to ask it — and realizing the answer exposes a missing layer.
Alan Turing did not set out to build better calculating machines. He asked a deeper question: what does it mean to compute at all? His answer — the universal Turing machine — defined the foundational structure underlying every computer system since.
Claude Shannon did not try to improve telegraph systems. He asked what information itself is — independent of meaning or medium. That question became information theory, the mathematical foundation of digital communication.
Both discoveries shared a common trait: they revealed a layer that everyone depended on, but nobody had formally defined. Once that layer was articulated, everything built on top of it became more reliable, scalable, and universal.
Encountering the Same Pattern in AI
When I was building a GTM business intelligence dashboard, I wasn’t trying to solve AI’s reasoning problem. I wasn’t thinking about cognition, philosophy, or safety. I just needed the system to behave consistently enough to ship.
The task itself was unremarkable. Analyze customer journeys. Classify deal intent. Identify which opportunities were real and which were noise. I ran the same Salesforce export through multiple large language models, using the same prompt, expecting small stylistic differences at most.
Instead, the interpretations didn’t agree.
One model marked a deal as Closed Won.
Another labeled the same deal Unqualified.
Another produced a plausible explanation, then quietly changed its reasoning on the next run.
At first, I assumed the mistake was mine. Unclear prompts. Poor instructions. Missing some technique everyone else seemed to know. But the pattern didn’t go away. So I did the most basic thing possible: I forced multiple models to analyze the same input repeatedly and compared where, how, and why they disagreed.
What emerged wasn’t randomness. It was structure.
Each model operated inside a shifting interpretive frame — meaning the definition of the task itself was not held constant across runs. Definitions moved. Assumptions drifted. Intent was quietly redefined between runs. And yet every output sounded reasonable. Confident, even. That’s what made it hard to dismiss.
That’s when it clicked. The problem wasn’t performance.
It was the stability of interpretation itself.
The Evaluation Blind Spot
The AI field is very good at answering one question: Is this output acceptable?
Benchmarks, tests, and evaluations are all built around that premise. Human raters assess plausibility. Automated metrics score correctness. Regression tests lock outputs at the surface.
What they don’t ask is the question that actually matters in production: Did the system interpret the task the same way this time as it did last time?
A model can change its interpretation entirely and still pass every evaluation, as long as each individual answer looks coherent. Fluency makes this worse. As models become more articulate, instability becomes harder to see, not easier.
Engineering layers compound the illusion. Caching replays old answers. Temperature is set to zero. Retrieval systems constrain facts but leave interpretation untouched. Each layer hides variability without ever detecting it.
The result is a class of failures that aren’t loud. They don’t look like crashes or hallucinations. They accumulate quietly, downstream, in systems that appear to be working—until the cost shows up somewhere responsibility was assumed but never examined.
Why Drift Remained Invisible
Drift wasn’t invisible because researchers were careless or negligent. It was invisible because the evaluation paradigm made it structurally impossible to see.
The field optimized for:
output quality over interpretive consistency
single-run correctness over multi-run invariance
surface coherence over architectural stability
No one was measuring whether meaning stayed fixed across time, context, or agents. And you cannot detect what you do not measure.
Once you see that, the invisibility stops looking mysterious. It starts looking inevitable.
Why I Saw What Others Missed
I have no formal training in AI, which turned out to be an advantage. When a model kept changing what customer intent meant, I didn’t accept “expected stochastic variance” as an explanation. I kept asking why the definition itself wouldn’t hold still.
When two models produced contradictory but individually “correct” answers, I didn’t average them. I asked why interpretation moved at all.
I had no benchmarks to defend. No architectures to protect. No institutional incentives to normalize failure. Just a system that wouldn’t stay put—and the freedom to keep asking why until the answer stopped moving and the outputs became something I could actually trust.
What This Means
Modern AI systems are powerful. They work remarkably well. But they operate on top of an undefined layer responsible for maintaining semantic stability, and that layer has never been properly measured, constrained, or governed.
Drift remained invisible because the field measured the wrong thing. Now that the layer has been exposed—and we know where evaluation breaks—we can finally begin to measure it.
White Paper I — Empirical Evidence of Interpretation Drift in Large Language Models establishes the existence of interpretation drift, demonstrates how it can be empirically observed and measured, and surfaces a class of instability not captured by standard evaluation methods. [White paper]
