Predictive Token Accounting: Forecasting the Bill

For a CFO, the scariest part of AI is the Variable Cost. Unlike a traditional SaaS database that costs $500/month regardless of traffic, an AI app that goes "Viral" can cost $50,000 in a weekend.

Predictive Accounting is the practice of using historical data to forecast future costs. If you know that your average user consumes 5,000 tokens per session, you can mathematically predict your bill based on your User Growth.

In this final lesson of Module 17, we learn how to calculate your Unit Economics and build a manual or automated Cost Predictor.

---

1. The Metric: TPD (Tokens Per Daily Active User)

This is the cornerstone of AI unit economics.

• Formula: Total tokens per month / Total DAU per month.

If your TPD is 10k, and you expect to add 1,000 users next month, your predicted bill increase is 10,000,000 tokens.

---

2. Tracking the "Token/Action" Benchmark

Every task in your system has an "Average Price."

• Drafting a Resume: 2,500 tokens ($0.03).
• Correcting Code: 1,200 tokens ($0.015).
• Enterprise Search: 5,000 tokens ($0.08).

By tracking these benchmarks, you can see if your Efficiency Engineering is actually move the needle. If "Drafting a Resume" moves from 2,500 to 2,000 tokens, you have a 20% efficiency gain that will scale linearly with your company's growth.

---

3. Implementation: Linear Cost Regression (Python)

Python Code: The Budget Prophet

import numpy as np

# Historical Data: [DAU counts], [Total Cost]
users = np.array([100, 200, 500, 800, 1200]).reshape(-1, 1)
costs = np.array([5, 12, 28, 45, 68]) # Dollars

# Simple Linear Model
from sklearn.linear_model import LinearRegression
model = LinearRegression().fit(users, costs)

# Predict cost for 10,000 users
future_users = np.array([[10000]])
predicted_bill = model.predict(future_users)

print(f"Predicted monthly bill for 10k users: ${predicted_bill[0]:,.2f}")

---

4. The "Scenario" Forecaster

You should build a spreadsheet (or a dashboard) with 3 scenarios:

• Baseline: Current efficiency settings.
• Optimized: If we implement Semantic Caching (Module 5).
• Pessimistic: If users start sending 2x longer prompts.

Business Value: This allows you to say to your board: "If we invest 2 weeks into caching, we will save $12,000 over the next 6 months based on our current TPD growth."

---

5. Summary and Key Takeaways

1. Know your TPD: Tokens Per DAU is your most important financial metric.
2. Action Benchmarks: Track the cost of individual features, not just the total bill.
3. Linear Scaling: AI costs usually scale linearly with users, making simple regression highly accurate.
4. Efficiency as ROI: Use your forecast to "Fund" your next efficiency project.

---

Exercise: The Growth Forecast

1. You have 5,000 users today. Your bill is $1,000/month.
2. Current TPD: $1,000 / 5,000 = $0.20 per user.
3. The Task: You are planning a marketing campaign that will bring in 50,000 new users.
4. Calculate the bill for the new user base. (Result: $11,000).
5. The Efficiency Challenge: If you can reduce the TPD by 25% (through pruning and minification), what is the new predicted bill?

• (Result: $8,250).
• The Question: Is a $2,750/month saving worth 1 week of engineering work? (Usually, Yes).

---

Congratulations on completing Module 17! You are now a financial strategist for the AI age.