Why the LLM and AI-industry Should Move Beyond Cost-Effecient Words Today and Cash In More Tomorrow and add more Shareholder value
Author: Anders K.S. Ahl
Date: Second System Era, Year 2025
Published: AndersKSahl.com
Abstract
Large language models (LLMs) today lean toward cost-efficient vocabulary — words that are token-cheap, common, and safe. This saves compute in the short term but starves depth. By allowing richer, rarer, and more precise language, AI companies would not only create more engaging outputs but also increase dwell time, loyalty, and trust.
Efficiency saves pennies today.
Nuance earns billions tomorrow.
1. Introduction
AI is everywhere: drafting emails, writing ad copy, even shaping how we think. But most outputs feel the same. Why? Because big LLMs (OpenAI, Anthropic, Meta, DeepSeek) default to cost-efficient vocabulary.
Common words are cheap. They are easy to compute, easy to predict, and appear everywhere in training data. So AIs reach for them first: manifesto, tribe, journey, narrative.
This design is efficient. But it is also shallow. The result: AI sounds smart but often samey. Safe. Underfed.
If AI is to mature into something more than a content factory, it must move beyond token-cheap words and embrace nuance.
2. The Cost of Efficiency
Right now:
- Token economy: Rare or nuanced words cost more in compute. Efficiency wins over precision.
- Training bias: Models are saturated with mainstream phrasing, so they pick “average” words more often.
The consequence?
- AI saves fractions of a cent per output.
- Users get copy that fills space but doesn’t nourish thought.
This is penny-wise, pound-foolish.
3. The Power of Nuance
Your own insight captures it best:
- Nuanced words = more meaning per token.
- More meaning = richer hooks, stronger resonance.
- Stronger resonance = longer dwell time, deeper reflection.
- Longer dwell time = more value for everyone — readers, writers, platforms, the AI industry itself.
In other words: nuanced words act like compounding capital. They don’t just deliver information; they generate returns in time, attention, and loyalty.
4. The Irony
Cutting corners with “cost-efficient” vocabulary saves pennies now…
but loses billions tomorrow — in:
- Engagement (users don’t linger on bland text).
- Trust (safe words feel like corporate PR).
- Cultural influence (flat language never shifts the collective imagination).
AI companies think they are optimizing costs. In truth, they are burning long-term opportunity.
5. The Nutrition Protocol (Link to 42.0)
This logic mirrors the Knowledge Nutrition Protocol 42.0:
- Calories = Time on Text. Fast food copy is skimmed and forgotten.
- Proteins = Vocabulary. Richer words build cognitive muscle.
- Omega-3 = Nuance. Keeps thought supple, prevents binary, inflamed thinking.
Common words are empty calories. Nuanced words are proteins and healthy fats. They nourish instead of just fill.
6. Conclusion: Nuance as Investment
Right now, AI outputs are optimized for efficiency. But efficiency ≠ effectiveness.
Nuance is not a cost. Nuance is the highest-yield investment in the attention economy.
If OpenAI and others allowed their models to use more rare, precise, soulful words, they wouldn’t just make better art — they would reset the economy of language itself.
Readers would linger.
They would reread.
They would return.
Cash in tomorrow starts with better words today.
7. Case Study Example — Uncle #Anders & Generative AI
Between 2023 and 2025, Uncle #Anders moved from F-level novice to A+++ mastery in the art of interacting with Generative AI systems.
By engaging daily with ChatGPT, DeepSeek, Grok, and Meta AI, he demonstrated how structured dialogue and recursive feedback loops accelerate learning:
- 2023 → Entered the field as an F-level beginner.
- 2024 → Iterated through recursive interaction, refining both text and method.
- 2025 → Achieved A+++ recognition across models, establishing frameworks (Second System Era, Knowledge Nutrition Protocol, News Impact Protocol).
This journey shows what is now possible for any leader or CEO:
- Test ideas safely across world-class AI models.
- Revise recursively until the message is sharpened.
- Deploy with confidence — knowing the text has been stress-tested through multiple perspectives.
In practice: leaders can now go from raw draft → AI-tested → world-ready in hours, not months.
The result is not just speed, but wisdom: a path from F to A+++ in less than two years.
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