2026 AI 融资全景：从百亿到千亿的投资狂潮

# 2026 H1 基础模型公司融资对比（亿美元）
companies = {
    "OpenAI": {"round": "G轮", "amount": 400, "valuation": 5000, "investors": ["SoftBank", "微软", "NVIDIA"]},
    "xAI": {"round": "F轮", "amount": 250, "valuation": 3500, "investors": ["红杉", "a16z", "Sequoia Capital"]},
    "Anthropic": {"round": "E轮", "amount": 150, "valuation": 2200, "investors": ["亚马逊", "Google", "Thrive"]},
    "Mistral AI": {"round": "D轮", "amount": 60, "valuation": 800, "investors": ["泛大西洋投资", "Lightspeed"]},
    "智谱AI": {"round": "D轮", "amount": 45, "valuation": 600, "investors": ["阿里", "腾讯", "高瓴"]},
}

for name, data in companies.items():
    print(f"{name}: {data['round']} 融资 ${data['amount']}亿, 估值 ${data['valuation']}亿")

def ai_valuation(base_arr, benchmark_scores, compute_resources, data_moat_score):
    """
    AI 公司估值新模型
    - base_arr: 基础年化收入
    - benchmark_scores: 关键基准测试得分 dict
    - compute_resources: 算力资源 (GPU-hours/月)
    - data_moat_score: 数据护城河评分 (1-10)
    """
    # 能力溢价系数
    capability_premium = sum(benchmark_scores.values()) / len(benchmark_scores)
    
    # 算力乘数
    compute_multiplier = min(compute_resources / 1e6, 5.0)  # 上限 5x
    
    # 数据护城河乘数
    moat_multiplier = 1.0 + (data_moat_score - 5) * 0.15
    
    # 综合估值
    valuation = base_arr * 30 * capability_premium * compute_multiplier * moat_multiplier
    
    return round(valuation / 1e8, 2)  # 以亿美元为单位

# 示例：某 AI 编程公司
valuation = ai_valuation(
    base_arr=2e8,  # 2亿美元 ARR
    benchmark_scores={"SWE-Bench": 0.85, "HumanEval": 0.95, "CodeContests": 0.72},
    compute_resources=5e6,  # 500万 GPU-hours/月
    data_moat_score=8
)
print(f"估值: ${valuation}亿美元")