AI投资全景图：把握大模型时代的财富机遇

AI企业估值框架

# AI企业估值模型
def calculate_ai_company_valuation(company_metrics):
    """
    AI企业估值计算框架
    """
    # 1. 收入倍数法
    revenue_multiple = get_revenue_multiple(
        growth_rate=company_metrics['revenue_growth'],
        market_position=company_metrics['market_position'],
        tech_moat=company_metrics['technology_advantage']
    )
    
    # 行业基准倍数
    if company_metrics['stage'] == 'growth':
        base_multiple = 15  # 15-25x ARR
    elif company_metrics['stage'] == 'mature':
        base_multiple = 8   # 8-12x ARR
    else:  # early
        base_multiple = 25  # 25-40x ARR
    
    # 2. 技术资产评估
    tech_value = evaluate_technology_assets({
        'model_performance': company_metrics['model_metrics'],
        'data_assets': company_metrics['proprietary_data'],
        'patents': company_metrics['ip_portfolio'],
        'team_quality': company_metrics['team_score']
    })
    
    # 3. 市场机会评估
    market_factor = assess_market_opportunity({
        'tam': company_metrics['total_addressable_market'],
        'market_growth': company_metrics['market_cagr'],
        'competitive_position': company_metrics['market_share']
    })
    
    # 4. 风险调整
    risk_discount = calculate_risk_discount({
        'regulatory_risk': company_metrics['regulatory_exposure'],
        'technology_risk': company_metrics['tech_obsolescence_risk'],
        'execution_risk': company_metrics['team_track_record'],
        'market_risk': company_metrics['customer_concentration']
    })
    
    # 综合估值
    valuation = (
        company_metrics['arr'] * base_multiple * revenue_multiple +
        tech_value
    ) * market_factor * (1 - risk_discount)
    
    return {
        'valuation': valuation,
        'price_per_share': valuation / company_metrics['shares_outstanding'],
        'key_drivers': {
            'revenue_multiple': revenue_multiple,
            'tech_premium': tech_value / valuation,
            'risk_discount': risk_discount
        }
    }