Why the Wild West logic doesn’t work for AI Search
In conversations around AI search, one sentence keeps coming up again and again:
“AI search is like SEO 20 years ago – the Wild West.”
It sounds compelling. But it’s strategically dangerous.
Because the comparison explains less than it promises.
It romanticizes the past – and distorts how we interpret what’s actually happening.
If you see AI search as a gold rush, you look for quick wins.
If you understand it as a system, you build substance.
And that’s exactly where it’s becoming clear who actually gets it.
TL;DR
- The “Wild West” is a distorted metaphor – the winners were never the fastest, but the most prepared
- The current debate suffers from cognitive dissonance: superintelligence vs. control via text file
- Short-term hacks work – but not for models built on trust and continuity
- The only sustainable advantage comes from real information gain
The romance of lawlessness
The Wild West has become shorthand for a phase without rules.
For speed. For opportunism. For: just go for it.
It’s a convenient interpretation. And a wrong one.
It ignores that even then, chaos didn’t win – structure did.
Not the fortune hunters, but those who were prepared.
The myth lives on through survivor bias:
we remember the few who succeeded – and forget the many who failed.
The real winners were different:
Those who built infrastructure.
Those who understood risk.
Those who created clarity.
Not the ones who rushed in blindly.
Seen through that lens, much of what is currently sold as “AI search strategy” looks more like a digital gold rush:
programmatic sites flooded with generic AI content, hoping that something sticks.
That’s not a system.
That’s experimentation at scale.
And experimentation is only a strategy if you actually learn from it.
The new greenhorns
It’s worth noticing who’s most active right now.
Often, it’s the organizations that neglected organic search for years.
Now AI shows up – and suddenly there’s pressure to act.
Not driven by conviction.
But by uncertainty.
The pattern is predictable:
People reach for what’s readily available.
Tactics instead of strategy.
Tools instead of understanding.
And not rarely, they’re guided by a new wave of “experts” offering certainty where analysis would be the more honest starting point.
This pattern isn’t new.
Only the playing field has changed.
Superintelligence vs. text file
One of the most revealing contradictions in the current debate:
On one side, we talk about LLMs as a fundamental disruption –
systems that reorganize, evaluate, and surface knowledge.
On the other, we believe we can steer them with something as simple as a llms.txt file.
That’s more than a technical misunderstanding.
It’s an attempt to regain control in a system that has become more complex.
We respond to an unfamiliar situation with familiar patterns.
The problem is:
those patterns belong to a different phase of search.
Even if some of these approaches work in the short term, their half-life follows a familiar curve:
what’s easy to reproduce becomes irrelevant quickly.
SEO history is full of examples.
The economics of shortcuts
Yes, many of these tactics are working right now.
At least on the surface.
But the real question isn’t if they work.
It’s for whom – and for how long.
For short-lived models, the logic is simple:
extract visibility while it’s there, then move on.
That logic doesn’t apply to brands.
You can’t rebuild trust on demand.
You can’t risk long-term visibility without consequences.
What we’re seeing is a dangerous category error:
tactics from disposable business models are being applied to systems that depend on continuity.
That can produce results in the short term.
In the long run, it contradicts the very model it’s applied to.
There’s also a structural aspect that often gets overlooked:
many AI systems rely on existing search indices for retrieval.
Which means they implicitly inherit:
- established quality signals
- relevance metrics
- working spam filters
The supposedly “new” space is less independent than it appears.
It builds on existing structures – just at higher speed.
The real moat: information gain
As content production becomes a commodity, competition inevitably shifts.
It’s no longer: who can produce more?
But: who contributes something that isn’t already there?
That difference is information gain.
The gap between what a model can generate –
and what only exists within the reality of your organization.
These are not abstract ideas, but very concrete sources:
Proprietary user data.
Insights from production and customer interactions.
Expert judgment.
Systems or tools that actually solve problems.
None of this is easily reproducible.
And that’s exactly where real competitive advantage emerges.
But it doesn’t come for free.
It requires structure.
Access to data.
And the ability to turn implicit knowledge into something explicit.
If that were trivial, it would already be standard.
AI as an enabler
This is where AI actually fits in.
Not as a machine to scale average content.
But as a tool to unlock depth.
That distinction matters.
AI can help make previously implicit knowledge accessible.
It can reveal structures hidden in day-to-day operations.
And it can reduce complexity to a point where it becomes genuinely usable.
But it always builds on something.
Without proprietary data, real experience, and substance, its output remains interchangeable.
At that point, it only scales what already exists.
The real opportunity isn’t more content.
It’s better access to knowledge.
Conclusion
The gold rush is a powerful image.
But a weak model.
It invites us to confuse speed with strategy
and activity with progress.
Those who follow that pattern will see results.
But they won’t build anything that lasts.
Those who understand how the economics of visibility are shifting
focus on different things:
on data instead of volume,
on interpretation instead of output,
on substance instead of shortcuts.
One final question:
If everyone can generate content –
how will you be found, and recognized?
More Notes On Search?