🌐 Field Note: The Crossover Point — When Discovery Becomes Deterministic
There’s a quiet inflection underway in how decisions form.
Recent data from airOps Research suggests that by mid-2026, ChatGPT may surpass Google in raw traffic. That projection is interesting — but it’s not the thing that matters most.
What matters is where the decision now resolves.
This isn’t simply a shift from Google to ChatGPT.
It’s a shift from search to resolution.
From Browsing to Resolution
For two decades, discovery functioned as browsing.
Search engines returned lists.
Users compared options.
Decisions formed slowly, externally, and often inconsistently.
AI systems behave differently.
They don’t present libraries.
They act more like consultants.
Their job is not to show everything, but to arrive at a satisfactory resolution with minimal variance. That changes the role of discovery entirely.
Increasingly, the answer is the decision environment.
The Resolution Shortlist
In the legacy model, the shortlist lived in the user’s head — assembled after hours of research, reading, and comparison.
In the AI model, the shortlist forms inside the system itself.
The model evaluates:
coherence across sources
internal consistency
stability under compression
grounding in real experience
Once that evaluation is complete, the output is no longer a set of options — it’s a resolved path forward.
The resolution is the shortlist.
Why Optimisation Stops Working
This is the point where many familiar tactics begin to fail.
AI systems don’t reward optimisation in the traditional sense.
They don’t respond to frequency, formatting tricks, or surface-level relevance.
Instead, they reuse what is:
coherent across contexts
low-variance over time
grounded in lived or operational reality
structurally easy to resolve to again
This isn’t preference.
It’s a systems constraint.
Reusing unstable or noisy structures increases entropy.
Reusing coherent ones reduces it.
What Changes From Here
As this dynamic strengthens, something subtle but important happens:
The system no longer feels the need to choose between certain options.
Some entities become part of the resolution machinery itself — not because they were selected, but because reopening the question no longer improves the outcome.
At that point, discovery becomes deterministic.
Not because competition disappears —
but because the system has already learned where it can safely begin.
Closing Thought
Traffic curves will fluctuate.
Interfaces will change.
New tools will emerge.
But the deeper shift is already in motion.
Markets are moving from being searched to being resolved.
And in that world, the advantage doesn’t belong to whoever shouts loudest —
but to whoever the system learns it doesn’t need to second-guess.