Token-Fat: Age of AI Word of the Day
Treating AI like a person doesn't make you polite; it makes you token-fat
The AI market is hitting record highs, and profits are expected to be “mahoosive”.
Mahoosive! Our old vocabulary needs quirky words to capture the strangeness of our new machine age.
Today’s word is TOKEN-FAT
Recently, StrictQuality.AI wrote about the fierce tokenmaxxing debate.
Tokens are the information units processed by AI models. Their cost and scarcity has forced some firms to cancel projects or limit token availability. One enterprise AI startup paid over $50,000 for tokens.
We can’t all afford to be tokenmaxxers. In the Age of AI, most of us should be token-fat aware.
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The Micro-Dose on Token-Fat
Token-fat enables discussion of the consequences from the Conversational Friction vs. Instructional Density trade-off in a user’s prompting style.
In Personal Use-Cases: Token-fat introduces uncertainty to the AI and “sluggish” outcomes that break your flow.
In Work Use-Cases: Token-fat across the organization is significant leakage in the AI budget and a source of slop in workflows.
The “Toaster” Rule: “Nobody says ‘Please’ to a toaster. Fattening up on tokens to treat AI like a person is a “verbosity tax” you pay.
Strict Quality Tip: Treat every prompt like a mile driven on a 1/4 tank. Use labels like Goal or Task to separate your data from your intent and instructions. Every word in a prompt should provide context, a constraint, a data point, or a formatting instruction.
Use-Case Examples
Personal scenario: [User Uploads Photo of Fridge Contents]
Token-Fat: “Here’s a photo of what’s in my fridge. Please look at what I have and give me 3 ideas for a quick, healthy dinner I could make tonight.” (32 tokens)
Token-Lean: “Suggest 3 high-protein recipes based on photo. 15-min cooking time tops.” (14 tokens)
The Result of Slimming Down: 55% lower cost in tokens and near-instant response.
Work scenario: [User Uploads Transcript]
Token-Fat: “I uploaded the transcript from our meeting. Please look it carefully and provide a short summary of the main points and action items.” (26 tokens)
Token-Lean: “Context: Uploaded transcript. Output: Bulleted main points + Action Items table.” (12 tokens)
The Result of Slimming Down: Over 50% reduction in token waste and higher instructional adherence.
Takeaway
Start talking about token-fat.
It is a hidden tax on your time, focus, and budget; an obstacle to productivity and a mechanical lag that disconnects the AI’s output from your train of thought.
Work on getting token-lean. Every word in your prompts should provide context, a constraint, a data point, or a formatting instruction.