本文以凯利准则（Kelly Criterion）为引子，探讨了布拉格AI实验室EquiLibre获得5亿美元估值的争议。EquiLibre由三位前DeepMind研究员创立，利用强化学习技术从扑克游戏转向金融交易，与高频交易公司Tower Research合作，声称在标普500和纳斯达克每日交易数十亿美元，且自成立以来每月均录得正收益。文章以文艺复兴科技公司（Renaissance Technologies）的传奇基金Medallion为对比，指出该基金刻意将资产管理规模控制在约100亿美元，甚至在2003年清退所有外部投资者，因为其创始人、数学家吉姆·西蒙斯深谙一个道理：下注规模应由你的优势决定，而非市场规模。

文章的核心论点在于，量化基金与科技公司在本质上截然相反。风险投资追求的是非线性增值——更多用户使产品更好、吸引更多用户、形成护城河；然而交易策略恰恰相反，使用的人越多，策略就越弱，因为其本质是发现错误定价并从中获利，当更多人复制同一策略时，套利机会便消失殆尽。此外，软体具有规模经济效应，而交易却具有规模不经济的特性——边际用户的成本为零，但边际资金却被「征税」，且税率随投入资金的增加而攀升，因为大额仓位会影响市场流动性，交易者的订单本身就会推动价格偏离预期方向。

EquiLibre的CEO施密德坦承风投投资对冲基金确实不合常理，并将公司定位为「AI技术公司」而非金融公司，将交易视为第一个垂直应用场景，长期目标是开发能在真实世界中行动的AI。然而文章指出多项隐忧：其「零负月」纪录诞生于2025年加密货币及股市普遍上涨的环境中，缺乏类似2007年「量化地震」般的极端压力测试；回测表现与实盘盈亏之间存在系统性交易中最昂贵的鸿沟；且即便对其优势做最慷慨的估计，按凯利准则计算，其价值也可能远低于5亿美元的估值。文章最终警告：AI并未将交易变成软体业务，量化基金不是科技公司，投资者不应混淆两者。

This article uses the Kelly Criterion as a framework to scrutinize the $500 million valuation of EquiLibre, a Prague-based AI lab founded by three former DeepMind researchers. The firm applies reinforcement learning—originally developed to master no-limit poker—to financial trading in partnership with Tower Research Capital, claiming billions in daily volume across the S&P 500 and Nasdaq with zero negative months since inception. The piece contrasts this with Jim Simons' Renaissance Technologies, whose legendary Medallion fund deliberately capped its assets at roughly $10 billion and expelled all outside investors in 2003, embodying the Kelly principle that bet size must match edge size, not market size.

The article's central argument is that quant funds and tech companies are structurally opposite businesses. Venture capitalists seek non-linear appreciation through network effects—more users improve the product, creating defensible moats. Trading strategies, however, degrade with scale and imitation: they profit from mispricings that close as more capital exploits them, and larger positions erode returns by moving the very prices the strategy targets. Software enjoys economies of scale where the marginal user is essentially free, while trading suffers diseconomies of scale where each marginal dollar is effectively taxed at an increasing rate. This fundamental asymmetry makes venture capital a poor structural fit for funding trading operations.

EquiLibre's CEO candidly acknowledges this tension, positioning the company as a technology lab that uses trading as its first revenue-generating vertical, with aspirations to build real-world AI agents. However, the article raises serious concerns: the firm's unblemished track record was established during a broadly rising market in 2025 crypto and equities, untested by stress events like the 2007 quant quake; back-tested improvements do not constitute evidence of edge replacement; and even generous estimates of the strategy's capacity, lifespan, and economics likely yield a value well below the $500 million valuation. The author concludes that AI has not transformed trading into a software business—quants have used machine learning for decades—and warns investors against confusing a business that compounds with one whose alpha inevitably depletes.