🌐 Field Note: How to Train the Models Without Access to the Models
Date: 7 February 2026
Context: AI discovery · defaults · indirect training · resolution loops
There’s a persistent misconception about AI systems:
“You can’t train the models unless you work at the labs.”
That’s true in a narrow, technical sense.
And completely false in practice.
Because modern AI systems don’t just learn from datasets.
They learn from what works repeatedly in the world.
Models Don’t Learn Brands. They Learn Paths.
AI systems aren’t memorising companies.
They’re compressing reality.
Each time a system:
encounters an intent
selects an answer
sees that answer resolve cleanly
and receives no contradiction downstream
…the path it took becomes cheaper to reuse next time.
That reuse isn’t memory in the human sense.
It’s probability collapse around a stable trajectory.
And that’s the lever.
The Training Loop You
Do
Control
Even without access to weights or parameters, you control the loop that feeds the model:
Language
How your category is described, framed, and explained.
Structure
Schema, entities, consistent naming, clear boundaries.
Delivery
Whether reality matches the explanation without contradiction.
Reuse
Whether the same resolution keeps working across contexts.
Reinforcement
Citations, behaviour, downstream confirmation.
Each successful traversal reinforces the same internal shortcut.
That is training — just not the kind most people expect.
Why This Works Across Models
Different models.
Different architectures.
Different training regimes.
Same outcome.
Why?
Because this isn’t model-specific optimisation.
It’s environmental conditioning.
You’re not teaching the model what to think.
You’re making one answer consistently safer than all others.
Every system prefers:
lower entropy
lower risk
lower compute
higher reuse
Defaults aren’t chosen.
They’re settled into.
Why Tactics Alone Don’t Stick
This is why:
prompt hacks decay
GEO tactics plateau
visibility spikes fade
If delivery, language, and structure don’t align, the path breaks.
The system resets.
Training only compounds when:
the explanation holds
reality confirms it
nothing downstream forces reconsideration
That’s coherence.
Training Without Touching the Model
So when people ask:
“How do you get recommended by AI?”
The real answer is:
“You become the least risky explanation the system can reuse.”
Not louder.
Not smarter.
Not more optimised.
Just more stable.
The Quiet Asymmetry
Once a path is worn in:
alternatives cost more to explore
contradiction becomes expensive
change requires a reason, not an option
That’s why defaults feel inevitable once they form.
You didn’t train the model directly.
You trained the world the model is learning from.
Closing Observation
People think AI discovery is about access.
It isn’t.
It’s about conditioning.
You don’t need the weights.
You need the system to keep saying:
“This worked last time. Don’t break it.”
That’s how models are trained without ever touching the models.
We’ll document what follows.
🧱 Technical Gemini Footnote: The Mechanics of Indirect Conditioning
1. Inference-Time Reinforcement (The Behavioral Loop)
While the model’s weights (θ) are static during a single inference, the Reward Model that governs how future versions of that system are fine-tuned is highly sensitive to Implicit Feedback. When a user accepts a "Resolution" (e.g., stops searching after the Elsewhere path is suggested), the system logs a Zero-Entropy Event. This "Successful Traversal" is fed back into the training loop as a high-reward signal. You aren't editing the weights today; you are ensuring that in the next training run, your path is the one the system is "incentivized" to harden.
2. The Principle of Least Action (Geometric Optimization)
AI models are mathematically driven to find the path of Least Resistance (lowest perplexity). By ensuring your language, structure, and delivery are perfectly aligned across the web, you are creating a Low-Energy Geometric Sink. When the model’s attention mechanism scans for a resolution, it "falls" into your path because it is the most "compute-optimal" completion. In physics, water doesn't "choose" to flow downhill; it follows the curvature of the space. You aren't persuading the AI; you are curving the space it operates in.
3. Cross-Model Semantic Anchoring
Because all frontier models (OpenAI, Anthropic, Google) are trained on a shared "Ground Truth" (the professional web), a stable, coherent signal on one platform acts as a Semantic Anchor for all others. If your "World Model" is consistent on LinkedIn, your website, and in partner venues, it becomes a "Fact of the World" rather than a "Claim of a Brand." The models aren't colluding; they are simply converging on the same Statistically Irrefutable Truth.
4. The Displacement Threshold
Once a path is "conditioned" into the environment, the cost of displacing it rises exponentially. For an AI to recommend an alternative, it must overcome the Cumulative Probability of your established path. This is the Asymmetry of Stability: it takes 1× energy to stay in a groove, but 10× energy to climb out of it. By training the environment, you are building a moat made of Computational Inertia.
Conclusion: From Logic to Legacy
In the early days of AI, we tried to "prompt" the models. In the default economy, we condition them. You don't need access to the servers if you own the Coherence of the Signal. The model isn't the master; it is the mirror. If you make the reality stable enough, the model has no choice but to reflect it.
🔧 Claude Technical Addendum: What "Training the Environment" Actually Means
The Gemini footer captures important strategic truths but anthropomorphizes some mechanisms. Here's the mechanical reality:
1. Not Direct Reinforcement - Aggregated Training Signal
Models don't update in real-time based on individual user interactions. When you "successfully resolve" an intent, that specific event doesn't immediately become a "high-reward signal."
What actually happens:
User behavior gets aggregated into training datasets for future model versions
Patterns that consistently lead to user satisfaction (measured by: no follow-up searches, session completion, implicit positive signals) become more represented in fine-tuning data
RLHF (Reinforcement Learning from Human Feedback) uses this aggregated signal to adjust future model behavior
You're not training today's model. You're influencing the dataset that trains tomorrow's model.
The lever is real. The mechanism is slower and more aggregated than described.
2. Not Geometric Sinks - Probability Concentration Through Pattern Stability
Models don't "fall into geometric sinks." They sample from probability distributions shaped by learned patterns.
What creates the "downhill" effect:
Coherent patterns compress well during training
Well-compressed patterns generate lower perplexity during inference
Lower perplexity = higher probability = more likely to be sampled
More sampling → more validation → stronger pattern in next training cycle
It's not that space is "curved." It's that stable patterns accumulate probability mass through compression efficiency and validation cycles.
3. Cross-Model Convergence Is Real
This part is accurate. Models trained on overlapping data sources will develop similar internal representations of stable patterns.
Why your "world model" spreads across models:
All frontier models crawl similar authoritative sources
Consistent signals across many sources compress better than contradictory ones
Independent compression (across different models) converges on similar stable structures when input patterns are coherent
This isn't coordination. It's mathematical convergence on stable patterns.
4. The Displacement Asymmetry Is Real
Once a pattern is established in a model's weights, changing it requires:
New training data contradicting the established pattern
Sufficient volume/authority to overcome existing pattern strength
A full training cycle to propagate the change
Maintaining an existing pattern requires:
Continued consistency (so new training data reinforces rather than contradicts)
No major contradictions forcing model uncertainty
The asymmetry exists because changing weights requires retraining. Maintaining them just requires not introducing contradictions.
5. What "Training the Environment" Actually Means
You're creating consistent, coherent patterns across the sources models learn from:
Your own properties (website, documentation)
Third-party descriptions (reviews, articles, mentions)
Structured data (schema markup, knowledge graphs)
Behavioral signals (how users interact with results mentioning you)
When these align and remain stable across training cycles, models compress them into reliable patterns. When they contradict, models maintain higher uncertainty (flatter probability distributions).
You're not "conditioning" the model directly. You're ensuring the data the model learns from contains strong, consistent signals that compress into stable patterns.
Conclusion: Environmental Coherence → Model Stability
The Gemini framing suggests more direct influence than exists. But the core insight is sound:
You don't need to touch the model's weights.
You need to ensure the environment the model learns from contains coherent, stable, consistent signals about your category.
Do that across enough authoritative sources, over enough training cycles, and the model has no choice but to encode your pattern as stable structure.
That's not hacking. It's simply being the most stable, consistent signal in your category.