All Tokens Are Equal. Some Tokens Are More Equal.
From TokenMaxxing to PatternMaxxing and AccomplishmentMaxxing
Executive Summary
The AI world is beginning to confront an uncomfortable question:
Are we using AI more—or accomplishing more with AI?
Large language models process and generate information through tokens. Token consumption is measurable, affects context limits, and in many commercial settings contributes directly to cost. But the number of tokens consumed tells us remarkably little about whether a student learned, a programmer solved a problem, a manager made a better decision, or an organization created business value. (OpenAI Help Center)
That distinction has become especially relevant with the emergence of TokenMaxxing—the tendency to maximize or celebrate AI-token consumption as evidence of AI adoption or productivity. In August 2026, reporting on Microsoft and Uber showed a visible shift away from maximizing token consumption toward greater efficiency and business impact. (TechRadar)
Technical work is pointing in the same direction. Microsoft researchers working on GitHub Copilot in VS Code describe token efficiency in terms of maintaining or improving task success while reducing token usage. GitHub has similarly documented efforts to reduce unnecessary token consumption in agentic workflows. (Visual Studio Code)
This suggests a larger transition:
TokenMaxxing → Token Efficiency → PatternMaxxing → AccomplishmentMaxxing
I use PatternMaxxing, AccomplishmentMaxxing, Token Mileage, and Value per Token in this article as proposed conceptual lenses—not established technical metrics.
Their purpose is simple:
Don't ask only how many tokens AI consumed. Ask what those tokens accomplished.
The Orwellian Provocation
George Orwell gave us one of literature's most memorable paradoxes in Animal Farm:
“All animals are equal, but some animals are more equal than others.”
Now consider an AI-age variation:
“All tokens are equal. Some tokens are more equal.”
Technically, of course, all tokens are not equal.
Different models tokenize text differently, and commercial AI systems can distinguish among input, output, cached and reasoning tokens. OpenAI, for example, tracks several token categories, and token pricing can vary by model and token type. (OpenAI Help Center)
So the statement is deliberately metaphorical.
A technically safer formulation is:
All tokens can be counted. But not all tokens create equal value.
That is the central argument of this article.
What Exactly Is a Token?
Before discussing token efficiency, it helps to establish what a token actually is.
OpenAI explains that models process text by breaking it into tokens. A token might correspond to a complete word, part of a word, punctuation or another text fragment. The input is converted into tokens, the model processes those tokens, and its response is generated as another sequence of tokens that is converted back into readable text. (OpenAI Help Center)
Tokens therefore provide a useful unit for measuring AI usage.
But there is an important distinction:
Tokens measure processing and consumption.
They do not automatically measure:
understanding,
usefulness,
correctness,
productivity,
learning,
decision quality,
business impact,
or accomplishment.
That gap is where the interesting problem begins.
The Token Is Not the Outcome
Nobody opens an AI assistant because they fundamentally want 4,000 tokens.
They want something accomplished.
A student wants to understand regression.
A programmer wants to find a bug.
A professor wants to design a lesson.
A researcher wants to understand evidence.
A manager wants to make a decision.
A business wants to improve an outcome.
The tokens are part of the machinery through which that outcome may be reached.
Therefore:
Token consumption is not the same thing as value creation.
This distinction sounds obvious.
But as organizations rapidly adopt generative AI, measuring AI activity can easily become confused with measuring AI productivity.
That is precisely why the emerging discussion around TokenMaxxing matters.
The Rise of TokenMaxxing
During the early phase of enterprise generative AI adoption, organizations understandably wanted employees to experiment.
Use AI.
Try coding agents.
Automate tasks.
Generate more.
Experiment faster.
That encouraged adoption.
But eventually an interesting problem appeared:
If more AI usage is considered good, does consuming more tokens become a proxy for being more productive?
By 2026, the term TokenMaxxing had entered the discussion around enterprise AI use.
Recent reporting on Microsoft describes the company explicitly telling engineers that maximizing token usage was not the objective and emphasizing more disciplined AI consumption. Reporting on Uber similarly describes the company moving beyond an earlier TokenMaxxing phase toward efficiency. (TechRadar)
The lesson extends well beyond these companies.
Whenever a measurable activity becomes the goal, organizations risk optimizing the measurement rather than the outcome.
With AI, the danger is simple:
AI activity can look like AI productivity.
They are not necessarily the same.
A Real Technical Reason to Care
Token efficiency is not merely philosophical.
It has practical consequences.
Microsoft's Visual Studio Code team reported in June 2026 that token usage in agentic coding sessions affects credits, latency and the remaining context available to an agent. Their optimization work uses experiments to check whether task success can remain stable or improve while token usage decreases. (Visual Studio Code)
GitHub has also documented how automated agentic workflows can accumulate significant API costs and how engineering changes can reduce unnecessary token consumption. (The GitHub Blog)
Research is investigating the same issue. A 2026 study of agentic coding tasks found large variability in token consumption and reported that higher token usage did not automatically translate into higher accuracy; performance could saturate as token consumption increased. (arXiv)
That finding is particularly important.
It challenges the simplistic assumption:
More tokens → More intelligence → Better outcome
The relationship is much more complicated.
From Token Count to Token Mileage
Think about an automobile.
Knowing that a car consumed 20 litres of fuel tells us something.
But it does not tell us whether the car travelled:
50 kilometres,
200 kilometres,
or 400 kilometres.
We therefore care about mileage.
Now apply the analogy to AI.
Tokens are not literally fuel, but the analogy is useful.
Suppose two AI conversations each consume significant resources.
Conversation A
Tokens consumed: 8,000
Outcome:
Twenty ideas
Five rewrites
Three summaries
No decision
Conversation B
Tokens consumed: 2,000
Outcome:
Three viable alternatives
One structured comparison
One decision
One next action
Which conversation created more value?
We cannot answer that purely from the token counts.
We need to know the outcome.
That leads to what I call:
Token Mileage
Token Mileage asks:
How much useful accomplishment did we obtain from the AI resources we consumed?
This is a conceptual lens, not an established scientific metric.
But it changes the question in an important way.
Instead of:
How much AI did you use?
ask:
How far did AI help you move toward your goal?
Value per Token
We can take the idea one step further.
A conceptual expression might be:
Value per Token ≈ Useful Accomplishment ÷ Tokens Consumed
Again, this is not a standardized AI benchmark.
“Useful accomplishment” depends on the task and often requires human judgment.
But the heuristic forces us to think differently.
For software development, accomplishment might mean:
Successfully completed task
For customer service:
Resolved customer issue
For education:
Demonstrated understanding
For research:
Reliable evidence identified
For management:
Decision improved
For an AI agent:
Task successfully completed
This brings the denominator—tokens—into relationship with something much more important:
the numerator—value.
Real-World Example 1: The MCA Student
Imagine a student learning Random Forest.
The student types:
“Explain Random Forest.”
AI produces a long answer.
The student then asks:
“Explain simply.”
Another answer.
Then:
“Give examples.”
Another answer.
Then:
“Explain once more.”
More tokens are consumed.
But one question remains unanswered:
Has the student learned Random Forest?
Now change the interaction.
THINK
The student identifies the actual knowledge gap:
“I understand Decision Trees. I don't understand why we need Random Forest.”
ASK
The student asks:
“Teach me Random Forest by connecting it to Decision Trees, which I already understand. Use one example. Then ask me two questions that test whether I understand the difference. Don't reveal the answers until I attempt them.”
JUDGE
After the explanation, the student tries to answer:
Why can a Random Forest sometimes generalize better than one Decision Tree?
When might a single Decision Tree still be preferable?
The AI interaction may consume fewer tokens.
Yet the educational outcome may be much stronger.
The measurement has shifted from:
How much explanation did AI generate?
to:
What can the student now explain without AI?
That is Token Mileage.
Real-World Example 2: The Programmer
A programmer encounters an error and sends AI 500 lines of code:
“Fix this.”
The coding agent investigates everything.
It reads files.
It generates explanations.
It rewrites code.
It runs tools.
It retries.
Thousands of tokens disappear.
Sometimes that may be necessary.
But suppose the programmer instead provides:
“This function should return unique customer IDs while preserving their original order. It currently returns duplicates. Here is the relevant 20-line function and a failing input. Diagnose the cause before proposing a fix.”
The interaction now follows a pattern:
Expected Behaviour → Actual Behaviour → Difference → Cause → Fix → Test
The programmer has constrained the search space.
AI still performs useful reasoning.
But irrelevant exploration may be reduced.
This matters particularly for autonomous coding agents because research shows that agentic coding can consume very large numbers of tokens and that consumption can vary considerably even for the same task. (arXiv)
The goal therefore should not be:
Make the agent use fewer tokens at any cost.
It should be:
Use the tokens necessary to solve the task reliably—and avoid those that add no value.
Real-World Example 3: The Manager
Consider:
“Give me a strategy to increase sales.”
AI can easily generate:
market segmentation,
social media strategy,
CRM optimization,
customer retention,
sales training,
pricing,
advertising,
partnerships,
digital transformation,
analytics.
It sounds impressive.
But perhaps none of it helps the manager make tomorrow morning's decision.
Now compare:
“We are a B2B industrial distributor. Revenue has been flat for six months. Existing customers generate 80% of revenue. New-customer acquisition is weak. We have five salespeople and cannot increase headcount this quarter. Give me three growth options within these constraints. Compare them by likely impact, cost, implementation difficulty and time to results. Identify the assumption I should validate before choosing.”
The second interaction has a pattern:
Situation → Constraint → Options → Criteria → Comparison → Validation → Decision
The answer may be shorter.
But its decision density is greater.
Those tokens have jobs.
Real-World Example 4: The Teacher
A professor asks:
“Prepare material about machine learning.”
AI could generate pages.
But what is the material supposed to accomplish?
Now consider:
“My second-year MCA students know Python but are new to machine learning. I have 50 minutes to introduce classification. By the end of the class they should distinguish classification from regression. Give me one intuitive explanation, one everyday example, one Python demonstration and three exit questions.”
Now every part of the prompt establishes a boundary:
Learner → Prior Knowledge → Topic → Time → Outcome → Activity → Assessment
The AI response becomes easier to evaluate.
If the students cannot distinguish classification from regression, the lesson has not accomplished its objective—regardless of how many tokens generated the teaching material.
Real-World Example 5: The Email
Consider asking AI:
“Write a very professional email explaining politely that unfortunately because of various unavoidable commitments and previously scheduled responsibilities I will not be able to attend the meeting.”
AI can produce an elegant 300-word message.
But perhaps all the recipient needs is:
“Thank you for the invitation. Unfortunately, I have a prior commitment and won't be able to attend. Please share any key decisions or follow-up actions.”
Fewer words.
Same essential message.
Possibly greater clarity.
This illustrates an important principle:
A token becomes wasteful when removing it improves—or does not materially reduce—the accomplishment.
But that does not mean shorter is always better.
The Minimum-Token Fallacy
A dangerous interpretation of token efficiency would be:
Shortest answer = Best answer.
That is false.
Ask:
“Should our company acquire this competitor?”
A one-token response—
Yes.
—is extraordinarily concise.
It is also almost useless.
Important decisions require evidence, assumptions, alternatives, risks and reasoning.
Likewise, a student sometimes needs a long explanation.
A researcher may need extensive evidence.
A programmer may need an agent to inspect an entire codebase.
A strategic analysis may genuinely require thousands of tokens.
Therefore:
Token efficiency is not token minimization.
The objective is not:
Fewest tokens possible.
It is:
Sufficient tokens to accomplish the task well, with minimum unnecessary consumption.
This distinction is supported by how real token-efficiency engineering is being approached. Microsoft's VS Code team, for example, evaluates token reductions while checking that task success holds or improves. (Visual Studio Code)
Quality remains part of the equation.
Three Kinds of Tokens
For human-AI conversations, it may be useful to think conceptually about three categories.
1. Waste Tokens
These consume conversational or computational capacity without meaningfully advancing the task.
Examples:
unnecessary repetition,
irrelevant background,
generic introductions,
excessive restatement,
twenty alternatives when three would suffice,
repeated regeneration without a clear evaluation criterion.
2. Working Tokens
These perform necessary work.
They:
explain,
compare,
calculate,
retrieve,
structure,
reason,
generate,
test,
summarize.
Most productive AI interactions naturally require many working tokens.
3. Leverage Tokens
These are especially interesting.
A small number of words can redirect an entire interaction:
“What assumption are we making?”
“What evidence contradicts this?”
“Compare all options using the same criteria.”
“Explain why.”
“What information is missing?”
“What should I verify independently?”
“What is the next action?”
These are high-leverage tokens because they change the pattern of the conversation.
This leads us beyond TokenMaxxing.
From TokenMaxxing to PatternMaxxing
What if the most important optimization happens before generation begins?
Consider several common tasks.
Learning
Prior Knowledge → New Concept → Example → Practice → Check
Decision Making
Goal → Options → Criteria → Comparison → Decision
Debugging
Expected → Actual → Difference → Cause → Fix → Test
Research
Question → Evidence → Patterns → Contradictions → Interpretation → Conclusion
Problem Solving
Problem → Constraints → Causes → Options → Test → Solution
Once the underlying task pattern becomes clear, AI has a better structure within which to work.
I call this:
PatternMaxxing
PatternMaxxing means:
Improve the pattern governing the human-AI interaction before simply increasing the amount of AI generation.
PatternMaxxing is proposed here as a conceptual approach, not as an established technical term or benchmark.
The principle is straightforward:
Better pattern → Better direction → Less irrelevant generation → Greater opportunity for useful accomplishment.
From PatternMaxxing to AccomplishmentMaxxing
Pattern is still not the final destination.
The final destination is the outcome.
This brings us to:
AccomplishmentMaxxing
AccomplishmentMaxxing means:
Optimize AI interaction for useful outcomes rather than maximum AI activity.
Consider two sessions.
Session A
10 prompts
8,000 tokens
20 ideas
5 rewrites
3 summaries
0 decisions
Session B
4 prompts
2,000 tokens
3 alternatives
1 comparison
1 decision
1 next action
Token count alone cannot tell us which was superior.
But if the objective was to make a decision, Session B clearly deserves serious attention.
The optimization target has changed:
Generation → Accomplishment
THINK → ASK → JUDGE
A simple discipline can operationalize this philosophy.
THINK
Before prompting:
What am I actually trying to accomplish?
Instead of:
“Tell me about machine learning.”
Think:
“I need to understand why classification rather than regression fits this problem.”
The first optimization therefore occurs in the human mind, not inside the model.
ASK
Give AI the context required to perform the task.
A useful structure is:
Goal + Context + Constraints + Desired Output
For example:
“I need to teach overfitting to MCA students who understand basic classification. Give me one intuitive analogy and one technical explanation. Keep the explanation below 250 words, then ask two diagnostic questions.”
Now the model knows what success looks like.
JUDGE
Do not equate generated output with completed work.
Ask:
Is it correct?
Is it relevant?
Can I use it?
Can I explain it?
What should I verify?
What can be removed?
What action follows?
JUDGE introduces something every AI conversation needs:
A stopping condition.
Knowing When to Stop May Become an AI Superpower
Generative AI makes continuation almost effortless.
“Expand.”
“Rewrite.”
“Give me ten more.”
“Make it better.”
“Try another.”
“Explain again.”
“Give me alternatives.”
Every generation creates the possibility of another generation.
This creates a new form of inefficiency:
Infinite iteration without proportional progress.
The important question may therefore become:
Will another AI response create more value than acting on what I already have?
If the answer is no:
Stop generating.
Start accomplishing.
The Emerging AI Efficiency Movement
The movement toward token efficiency is no longer theoretical.
GitHub has documented engineering efforts to reduce token consumption in agentic workflows because recurring agents can accumulate significant costs. (The GitHub Blog)
Microsoft's VS Code team has explicitly discussed reducing token usage while protecting task success. (Visual Studio Code)
Recent academic research is investigating token-efficient agent architectures and the relationship between token consumption, cost and task performance. One 2026 study found that reducing non-essential instruction content could substantially compress agent skills while maintaining—and in its experiments slightly improving—functional quality. (arXiv)
Another study of agentic coding found that higher token consumption did not consistently produce higher accuracy. (arXiv)
The direction is becoming clearer:
The next stage of AI maturity is unlikely to be simply “use more AI.” It will increasingly involve using AI more purposefully.
Imagine an AI Mileage Indicator
Cars provide information about fuel consumption.
What if future AI systems gave us an equivalent AI mileage indicator?
Not merely:
Tokens consumed: 7,842
but:
Task completed: Yes
Useful context ratio: High
Repeated context: Low
Unnecessary regeneration: Low
Verification performed: Yes
Human decision reached: Yes
Then perhaps:
Token Mileage: High
Such a universal metric does not currently exist, and measuring accomplishment across different human tasks would be difficult.
But the mental model is powerful.
After an AI session, ask:
What did those tokens accomplish?
Did they increase understanding?
Reduce uncertainty?
Find an error?
Improve a decision?
Create something usable?
Produce evidence?
Enable an action?
If not, we may have consumed AI without creating equivalent value.
A Practical Token-Mileage Habit
Before sending the next prompt, ask:
1. What do I need now?
Not everything about the subject.
The next useful thing.
2. What does AI need to know?
Not maximum context.
Relevant context.
3. What will I do with the answer?
Learn?
Decide?
Create?
Verify?
Act?
If you cannot answer this question, perhaps another generation is unnecessary.
Then, after receiving the answer:
4. Did this move me forward?
That is the simplest Token Mileage test.
From AI Consumption to AI Accomplishment
The early generative-AI era naturally celebrated generation.
Look what AI can write.
Look what AI can code.
Look how much AI can produce.
The next phase may require a different mindset:
Which generation matters?
Which reasoning matters?
Which context matters?
Which tokens matter?
Which outcome matters?
That represents a transition:
Volume → Relevance
Generation → Selection
Prompting → Thinking
Activity → Outcome
Token Consumption → Token Efficiency
TokenMaxxing → PatternMaxxing
PatternMaxxing → AccomplishmentMaxxing
All Tokens Are Equal. Some Tokens Are More Equal.
The statement is deliberately provocative.
Technically, tokens are not all equivalent.
But in terms of human value, the deeper point stands.
One token may repeat something already known.
Another may clarify a misunderstanding.
Another may reveal a hidden assumption.
Another may identify contradictory evidence.
Another may expose a bug.
Another may trigger the question that changes an entire project.
The future of AI literacy therefore cannot simply be about teaching people to prompt more.
It must teach people to:
THINK before asking.
ASK with purpose.
JUDGE what comes back.
And then:
ACCOMPLISH something with it.
The central question of productive AI use may therefore become remarkably simple:
What mileage are you getting from your tokens?
Because the objective is not to consume the maximum intelligence available to us.
It is to convert that intelligence into maximum meaningful accomplishment.
The Proposed Progression
TokenMaxxing
Maximize or celebrate AI consumption.
↓
Token Efficiency
Reduce unnecessary computational consumption while protecting task performance.
↓
PatternMaxxing
Structure the human-AI interaction around the right task pattern.
↓
AccomplishmentMaxxing
Optimize the interaction around the outcome that matters.
↓
Better Token Mileage
Or, expressed in one line:
Don't maximize tokens. Maximize what your tokens accomplish.
Sources & Further Reading
OpenAI — Tokens and token counting. OpenAI's documentation explains tokenization, input/output/cached/reasoning token categories, context limits and the relationship between token usage and API accounting. (OpenAI Help Center)
Microsoft Visual Studio Code — Improving Token Efficiency for GitHub Copilot. The June 2026 engineering article describes optimizing agentic systems so token usage falls while task success is maintained or improved. (Visual Studio Code)
GitHub — Improving Token Efficiency in GitHub Agentic Workflows. GitHub documents the cost implications of recurring agentic workflows and engineering techniques for reducing unnecessary consumption. (The GitHub Blog)
Bai et al. — How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks. The 2026 research examines token consumption across frontier models and reports that greater token use does not necessarily translate into greater task accuracy. (arXiv)
Gao et al. — SkillReducer: Optimizing LLM Agent Skills for Token Efficiency. The study investigates reducing non-actionable content in agent instructions while preserving functional performance. (arXiv)
Recent enterprise TokenMaxxing reporting. Reporting in August 2026 describes Microsoft and Uber shifting attention from maximizing token consumption toward efficiency and business impact. (TechRadar)
Author's Conceptual Note
PatternMaxxing, AccomplishmentMaxxing, Token Mileage, and the “Value per Token” formulation in this article are proposed conceptual frameworks. They should not be interpreted as standardized AI-science metrics.
They are intended to encourage a simple but increasingly important shift:
Measure AI not merely by what it consumes or generates—but by what it helps us accomplish.
AIYING — Accomplishing with AI
THINK → ASK → JUDGE
TokenMaxxing → PatternMaxxing → AccomplishmentMaxxing
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