AI初创公司面临着来自投资者的相互矛盾的期待：既要实现研究突破，又要实现商业规模化。1研究突破和商业规模化各自的成功概率均为1/100，两者同时达成的概率仅为1/10000。1
针对这一融资困境，有观点建议借鉴制药行业的经营模式，通过预先注册的并购承诺机制来解决问题。1该模式要求前沿实验室向达成特定研究目标的团队预先承诺支付固定的收购价格，从而将融资问题转化为单一目标的实现。1
制药行业的数据支持了这一模式的可行性。12013年至2022年间获美国食品药品监督管理局批准的药物中，收入低于5亿美元的小公司占52%，超过100亿美元的大公司占36%。1作为参考案例，SpaceX对Cursor的收购结构为：收购价60亿美元或放弃支付10亿美元。1
预计该方案具有经济可行性。1按照示意性目标，如果前沿实验室承诺以20亿美元收购任何以十分之一训练计算量匹配其上一代模型的团队，则从100万美元种子轮投资到20亿美元退出可实现2倍基金回报。1这一机制也反映了AI训练成本的快速增长趋势：最大训练运行成本自2016年以来以每年2.4倍的速度增长，预计2027年将超过10亿美元。1
An analysis published on Hacker News proposes a novel approach to resolving the funding crisis facing AI startups caught between two competing pressures.1 Investors simultaneously demand research breakthroughs and commercial scalability, yet each objective independently carries only a 1-in-100 success rate, making their combined achievement a 1-in-10,000 proposition.1 Rather than forcing startups to pursue both goals simultaneously, the proposal suggests borrowing from pharmaceutical industry practices by establishing pre-registered acquisition commitments, wherein leading research labs pledge to purchase promising teams at predetermined prices upon achieving specific technical milestones.1
The model draws parallel to existing pharmaceutical patterns, where between 2013 and 2022, 52 percent of FDA-approved drugs came from smaller companies generating less than $500 million in revenue, while 36 percent originated from organizations exceeding $10 billion in revenue.1 The urgency of such restructuring is underscored by escalating computational demands: maximum training run costs have grown at a rate of 2.4 times annually since 2016, with projections suggesting expenses will exceed $1 billion by 2027.1
The proposal illustrates its mechanics through concrete examples, including SpaceX's acquisition structure for Cursor, which involved either a $6 billion purchase price or a $1 billion payment waiver.1 One hypothetical application would commit a leading research lab to acquiring any team that matches its previous-generation model performance using one-tenth of the computational resources for $2 billion.1 Such an arrangement could enable seed investors to achieve two-fold fund returns by scaling $1 million initial investments to $2 billion exits.1
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