SoftBank reportedly slashes OpenAI-backed loan from $10 billion to $6 billion as lenders balk at private AI valuations

Introduction

In the world of private AI companies like OpenAI, valuations can be highly speculative and difficult to assess. This tutorial demonstrates how to build a simple valuation model that can help estimate the value of private AI companies using comparable public company data. We'll create a Python-based tool that calculates valuation multiples and applies them to private company metrics, which is similar to what lenders might use when evaluating loans backed by private company shares.

Prerequisites

• Python 3.7 or higher installed
• Basic understanding of financial metrics and valuation methods
• Experience with Python data analysis libraries (pandas, numpy)
• Access to financial data APIs or local CSV files with company data

Step-by-Step Instructions

Step 1: Set Up Your Development Environment

First, we need to install the required Python packages. Open your terminal or command prompt and run:

pip install pandas numpy yfinance

This installs the necessary libraries for data manipulation and financial data retrieval. pandas is essential for data handling, numpy for numerical operations, and yfinance for retrieving stock market data.

Step 2: Import Required Libraries

Start by creating a new Python file and importing the necessary modules:

import pandas as pd
import numpy as np
import yfinance as yf

# For visualization (optional)
import matplotlib.pyplot as plt

These imports give us access to data manipulation tools and financial data retrieval capabilities.

Step 3: Define the Valuation Model Class

Next, we'll create a class that encapsulates our valuation model:

class PrivateCompanyValuation:
    def __init__(self, company_name):
        self.company_name = company_name
        self.comparable_companies = []
        self.valuation_metrics = {}
        
    def add_comparable(self, company_symbol, revenue, market_cap, pe_ratio, ev_revenue_multiple):
        """Add a comparable public company to our analysis"""
        self.comparable_companies.append({
            'symbol': company_symbol,
            'revenue': revenue,
            'market_cap': market_cap,
            'pe_ratio': pe_ratio,
            'ev_revenue_multiple': ev_revenue_multiple
        })
        
    def calculate_valuation(self, private_company_revenue, private_company_metrics):
        """Calculate valuation using comparable company analysis"""
        # Convert to pandas DataFrame for easier manipulation
        df = pd.DataFrame(self.comparable_companies)
        
        # Calculate average multiples
        avg_ev_revenue = df['ev_revenue_multiple'].mean()
        avg_pe = df['pe_ratio'].mean()
        
        # Apply multiples to private company
        estimated_ev = private_company_revenue * avg_ev_revenue
        estimated_market_cap = estimated_ev + (private_company_metrics.get('debt', 0) - private_company_metrics.get('cash', 0))
        
        return {
            'estimated_ev': estimated_ev,
            'estimated_market_cap': estimated_market_cap,
            'avg_ev_revenue_multiple': avg_ev_revenue,
            'avg_pe_ratio': avg_pe
        }

This class provides a framework for comparing private companies against public ones using financial multiples, which is exactly what lenders would consider when evaluating loan amounts.

Step 4: Collect Data for Comparable Companies

Now we'll fetch data for some public AI and tech companies to use as comparables:

# Define our comparable companies
comparables = ['NVDA', 'AMD', 'MSFT', 'GOOGL', 'META']

# Create our valuation model
valuation_model = PrivateCompanyValuation('OpenAI')

# Fetch data for each comparable company
for symbol in comparables:
    try:
        company = yf.Ticker(symbol)
        info = company.info
        
        # Get financial data
        market_cap = info.get('marketCap', 0)
        revenue = info.get('totalRevenue', 0)
        pe_ratio = info.get('trailingPE', 0)
        
        # Calculate EV/Revenue multiple (simplified)
        enterprise_value = info.get('enterpriseValue', 0)
        ev_revenue_multiple = enterprise_value / revenue if revenue > 0 else 0
        
        # Add to our model
        valuation_model.add_comparable(symbol, revenue, market_cap, pe_ratio, ev_revenue_multiple)
        
        print(f"Added {symbol}: EV/Revenue = {ev_revenue_multiple:.2f}")
        
    except Exception as e:
        print(f"Error fetching data for {symbol}: {e}")

This step retrieves real market data from Yahoo Finance, which is what lenders would use to determine reasonable valuations for private companies.

Step 5: Apply the Model to a Private Company

With our comparable data loaded, we can now estimate the value of a private AI company:

# Private company metrics (these would be estimated for OpenAI)
private_company_metrics = {
    'revenue': 10000000000,  # $10 billion
    'debt': 0,
    'cash': 2000000000,      # $2 billion
    'market_cap': 0
}

# Calculate valuation
result = valuation_model.calculate_valuation(private_company_metrics['revenue'], private_company_metrics)

print("\nValuation Results:")
print(f"Average EV/Revenue Multiple: {result['avg_ev_revenue_multiple']:.2f}")
print(f"Estimated Enterprise Value: ${result['estimated_ev']:,.0f}")
print(f"Estimated Market Cap: ${result['estimated_market_cap']:,.0f}")

This simulates how a lender would evaluate the value of a private company like OpenAI, using the multiples derived from public comparables.

Step 6: Visualize the Results

For better understanding, let's create a simple visualization of our multiples:

# Create a simple visualization
if len(valuation_model.comparable_companies) > 0:
    df = pd.DataFrame(valuation_model.comparable_companies)
    
    plt.figure(figsize=(10, 6))
    plt.scatter(df['ev_revenue_multiple'], df['pe_ratio'], alpha=0.7)
    plt.xlabel('EV/Revenue Multiple')
    plt.ylabel('P/E Ratio')
    plt.title(f'Comparable Companies Analysis for {valuation_model.company_name}')
    
    # Add labels for each company
    for i, row in df.iterrows():
        plt.annotate(row['symbol'], (row['ev_revenue_multiple'], row['pe_ratio']),
                    xytext=(5, 5), textcoords='offset points')
    
    plt.grid(True)
    plt.tight_layout()
    plt.show()

This visualization helps us understand the relationship between different valuation metrics, which is crucial for lenders when assessing risk.

Summary

In this tutorial, we've built a practical valuation model that demonstrates how lenders might assess the value of private AI companies like OpenAI. The model uses comparable public companies to derive valuation multiples, which are then applied to private company metrics. This approach mirrors the process used by financial institutions when evaluating loan amounts secured by private company shares. While this is a simplified model, it demonstrates the core principles behind valuation assessments that were likely behind SoftBank's decision to reduce their loan amount from $10 billion to $6 billion due to the difficulty in reliably assessing OpenAI's private valuation.