Chapter 12: Token as a Quantitative Measure of Project Scale

What 1M Token Means:

• A medium-sized codebase (tens of thousands to over a hundred thousand lines of code)
• Plus necessary documentation and tests
• Can be fully loaded in a "large context model" (like Gemini's 1 million token)
• AI can understand the entire project's full picture in one session

What 10M Token Means:

• A large project, or a collection of multiple related projects
• Already cannot be fully loaded in one session
• Must rely on context selection strategies, letting AI only focus on "relevant parts"
• But within this scale, as long as context selection is done well, AI can still bring significant productivity improvements

After Exceeding 10M Token:

• Context selection complexity will rise sharply
• AI accuracy will noticeably decline (because it can always only see "partial context")
• May need to consider "project splitting" or "architecture refactoring," splitting large projects into multiple 1–10M token sub-projects

3. How to Measure a Project's Token Scale

A rough estimation method is:

1. Code Part

   • Add up all text from the entire codebase
   • Estimate by "1 token ≈ 4 English characters" or "1 token ≈ 1.5 Chinese characters"
   • Or directly use tools (like tiktoken) for precise calculation

2. Documentation Part

   • Add up all markdown, text documents
   • Similarly estimate by token ratio

3. Test Part

   • Test code and test data
   • If there are many test cases, this part might account for a large proportion

4. Other Assets

   • Configuration files, SQL schemas, API documentation, etc.
   • If the project has "meeting recordings converted to text" or "design drafts converted to text," these should also be included

Practical Measurement Recommendations:

• Can use scripts to regularly scan the entire codebase and calculate total token count
• Focus on token count's growth trend, not absolute numbers
• If you find token count growing too fast, think: Are documents/tests written too much? Is code duplication too high?

4. Impact of Token Scale on Engineering Practice

Within 1M Token:

• Can relatively "luxuriously" use AI
• Can have AI read the entire codebase, do global refactoring and optimization
• Documentation can be slightly "redundant," because context space is still sufficient

Between 1–10M Token:

• Must start paying attention to context selection
• Documentation must be more concise, only keep "snapshot-style" high-level documentation
• Code must be more modular, so AI can work by "only looking at relevant modules"

Exceeding 10M Token:

• Context selection becomes the core bottleneck
• May need to introduce "project indexing," "symbol indexing," and other more complex mechanisms
• Or consider "splitting projects," splitting large projects into multiple independent small projects

5. Practical Decision Recommendations

In a two-person small team, strongly dependent on AI, you can use the token metric like this:

1. Regularly Measure Project Token Scale

   • Measure once per month or quarter
   • Focus on growth trends. If growth is too fast, analyze the reasons

2. Adjust Strategy Based on Token Scale

   • Within 1M: Can be "extensive," let AI read more context
   • 1–10M: Start paying attention to context selection, simplify documentation
   • Exceeding 10M: Consider project splitting or architecture refactoring

3. Use Token Scale to Evaluate Tool/Model Selection

   • If the project is within 1M token, can directly use Gemini Web (1 million token context)
   • If exceeding 1M, may need tools like Codex/Cloud Code that use "symbol indexing + intelligent selection"

6. Summary

Token, as a quantitative measure of project scale, has unique value in the AI-Native era:

• It directly reflects AI's "understanding cost"
• It uniformly measures all types of project assets
• It's directly tied to AI usage costs

1M Token is a 'comfort zone,' 10M Token is a 'critical point.'
Within this range, AI-Native solutions can continuously bring at least 3× productivity improvement.
Beyond this range, more complex context selection strategies are needed, or consider project splitting.

Regularly measuring and paying attention to token scale is an important means for AI-Native teams to manage project complexity.