Be AI-Native and AI-Proof

Julie Yoo 2026-08-20 6.7 KB 106.8K 386 𝕏 文章列表

本文探讨了创始人如何构建既充分利用前沿模型优势（AI-Native）又不被其颠覆（AI-Proof）的企业。作者以医疗健康行业为例，指出随着通用模型进步，单纯依靠智能和自动化难以维持竞争优势，价值正转向“智能+自动化+问责制”。文章提出三种具备问责制属性的产品形态：提供临床服务、承担财务风险、开发FDA监管产品，并分别分析了其AI原生和AI免疫的方面。最后指出，最强AI原生企业正积极承担更多问责，以增强抗颠覆能力。

AI-Native AI-Proof 医疗健康问责制创业投资前沿模型监管风险承担

# Be AI-Native and AI-Proof **作者**: Julie Yoo **日期**: 2026-08-18T16:29:34.000Z **来源**: [https://x.com/julesyoo/status/2089751569063706784](https://x.com/julesyoo/status/2089751569063706784) --- ![Image](/doc/4814/image/img_2e549ad186a1.jpg) A top-of-mind question for founders and investors these days is how to build a business that gets stronger as frontier models improve, without becoming vulnerable to them. How do you build a business that is both AI-Native (taking full advantage of frontier models in a way that shows up in the P&L) and AI-Proof (unlikely to be disrupted by frontier models)? Recent adoption of AI in healthcare has been[ faster and more organic](https://healthtechbuilders.substack.com/p/no-longer-a-mythical-beast-market) than in any prior era, and the initial products have largely been focused on 1) better intelligence and 2) automation of administrative work. But healthcare now reflects a broader AI-era theme: as generalist frontier models keep improving, those value props become progressively less defensible, so value moves from intelligence and automation alone to intelligence and automation plus accountability. ## Accountability is the Product Accountability requires capabilities that sit outside the model layer. Even as models improve, they won’t automatically simplify infrastructure and real-world operations, solve regulatory complexity, or facilitate human relationships, let alone make companies more willing and able to bear risk. When the CEO of a frontier model company wakes up in the morning (if they’ve even slept to begin with!), the priorities on their mind are: lawsuits, staying on the good side of the government, anticipating what China will do, raising enough capital to cement access to compute and top talent, competing with the most formidable companies of all time, and the list goes on… What's the likelihood that the CEO wants to take on even more regulatory risk and liability by owning the underlying healthcare itself? Therein lies the opportunity: in vertically integrating into the healthcare accountability layer - where a licensed, contracted, or regulator-approved entity has to deliver an outcome, stand by a claim, and accept the consequences of being wrong (or right). So which healthcare product form factors own enough of the accountability to be both highly AI-Native and AI-Proof? Here are three: 1. Delivering care and IRL services What’s AI-Native: An AI-native clinical services business can operate with a) disruptively low cost structure (e.g. you're more likely to have run towards the CMS ACCESS program than away from it) and b) faster and more scalable growth and product expansion (e.g. operating nationally, with higher panel leverage, and more specialty coverage, sooner that prior generations of services companies). What’s AI-Proof: A medical practice still has to obtain state-by-state licensure, credential with payors, get malpractice coverage, contract and integrate with referral partners, and implement IRL care delivery operations. Even non-regulated, sub-clinical services are subject to rigorous safety validation, clinician oversight, and last-mile integration into care workflows that represent a level of liability that goes far beyond the intelligence layer. 2. Bearing financial risk What’s AI-Native: AI can arguably automate the majority of the admin work that comes with being a risk-bearing entity, and meaningfully changing the cost structure of managing a population at scale. What’s AI-Proof: A full-stack risk-bearing entity may have to raise reserve capital, get state licensure (if fully insured), secure provider network contracts, engage with members and doctors to manage the total cost of care, and win high-touch sales. And even software-only businesses can bear financial risk, e.g. by only getting paid when multi-party transactions are completed or when the customer gets paid. 3. Developing an FDA-regulated product What’s AI-Native: AI can increasingly automate the operational and cognitive parts of the diagnostic or drug discovery and development process. What’s AI-Proof: A company ultimately still has to manufacture the product, put it through years of human clinical trials, and get FDA approval to make a definitive claim about the diagnostic or therapeutic benefit. Not to mention [we have only scratched the surface of understanding human biology](https://www.a16z.news/p/drug-discovery-has-no-magic-wands), so we still need to generate new data and build validation loops at scale to expand the search space over which AI can effectively operate. ![Image](/doc/4814/image/img_e6dc68fdfbce.jpg) Of course, the pace of evolution in the space means this framework could have a shorter half-life than we might think. Deregulation could lower the bar for autonomous AI products to do more without licensure or approval. Agentic provider-network contracting could lower the barriers to fully autonomous health plans in the employer-sponsored market. Frontier models could mature to a point that those existential risks on the minds of their CEOs every morning become far more manageable, freeing them up to explore taking on more risk, liability, and ultimately, accountability. That said, many of the [best-performing healthtech companies today](https://www.youtube.com/watch?v=3h8e8648d-U&t=621s) are already implementing one or more of the models above: delivering outcomes, bearing financial risk, and/or developing a regulated diagnostic or therapeutic. And the strongest AI-native performers whose centers of gravity are still “better intelligence” or “administrative automation” are actively figuring out how to take on more accountability - and hence become more AI-proof. [Originally posted here](https://healthtechbuilders.substack.com/p/be-ai-native-and-ai-proof) ## 相关链接 - [Julie Yoo](https://x.com/julesyoo) - [@julesyoo](https://x.com/julesyoo) - [37K](https://x.com/julesyoo/status/2089751569063706784/analytics) - [faster and more organic](https://healthtechbuilders.substack.com/p/no-longer-a-mythical-beast-market) - [we have only scratched the surface of understanding human biology](https://www.a16z.news/p/drug-discovery-has-no-magic-wands) - [best-performing healthtech companies today](https://www.youtube.com/watch?v=3h8e8648d-U&t=621s) - [Originally posted here](https://healthtechbuilders.substack.com/p/be-ai-native-and-ai-proof) - [Upgrade to Premium](https://x.com/i/premium_sign_up) - [12:29 AM · Aug 19, 2026](https://x.com/julesyoo/status/2089751569063706784) - [37.2K Views](https://x.com/julesyoo/status/2089751569063706784/analytics) - [View quotes](https://x.com/julesyoo/status/2089751569063706784/quotes) --- *导出时间: 2026/8/20 21:27:02* --- ## 中文翻译 # 成为AI原生且AI免疫的企业 **作者**: Julie Yoo **日期**: 2026-08-18T16:29:34.000Z **来源**: [https://x.com/julesyoo/status/2089751569063706784](https://x.com/julesyoo/status/2089751569063706784) --- ![Image](/doc/4814/image/img_2e549ad186a1.jpg) 如今，创始人和投资者最关心的问题之一是如何构建一家能够随着前沿模型进步而变得更强大、同时又不会因这些模型而变得脆弱的企业。如何打造一家既具备AI原生性（充分利用前沿模型并在损益表中体现其价值）又具备AI免疫性（不易被前沿模型颠覆）的企业？近年来，医疗健康领域对AI的采用比以往任何时代都[更快、更自然](https://healthtechbuilders.substack.com/p/no-longer-a-mythical-beast-market)，最初的产品主要集中在1）更智能的决策和2）行政工作的自动化。但医疗健康现在反映了一个更广泛的AI时代主题：随着通用型前沿模型的不断进步，这些价值主张的防御性逐渐减弱，因此价值从单纯的智能和自动化转向智能、自动化加问责制。 ## 问责制就是产品问责制需要模型层之外的能力。即使模型不断改进，它们也不会自动简化基础设施和现实世界的运营、解决监管复杂性或促进人际关系，更不用说让企业更愿意且更有能力承担风险了。当一家前沿模型公司的CEO早上醒来时（如果他们真的睡了的话！），他们脑海中优先考虑的事情是：诉讼、与政府保持良好关系、预测中国会做什么、筹集足够资本以确保算力和顶尖人才的获取、与有史以来最强大的公司竞争，等等……这位CEO有多大可能愿意通过自己拥有底层医疗业务来承担更多的监管风险和法律责任？机会就在于此：垂直整合到医疗健康的问责层——在这个层面，一个获得许可、签约或监管机构批准的实体必须交付结果、为其主张负责，并接受错误（或正确）的后果。那么，哪些医疗健康产品形态拥有足够的问责权，既能高度AI原生又具备AI免疫性？以下是三种： 1. 提供护理和线下（IRL）服务 AI原生性体现在：一个AI原生的临床服务企业可以以a）颠覆性的低成本结构运营（例如，你更可能主动拥抱CMS ACCESS计划而非回避它），以及b）更快、更具可扩展性的增长和产品扩张（例如，在全国范围内运营，拥有更高的医生管理患者比例，以及比前几代服务公司更早地覆盖更多专科领域）。 AI免疫性体现在：医疗诊所仍然必须逐州获得执业许可、与支付方签订合同、购买医疗事故保险、与转诊合作伙伴签约并整合，以及实施线下护理交付运营。即使是受监管较少的亚临床服务，也受到严格的安全验证、临床医生监督和护理工作流程的最后一公里整合的约束，这些代表了远超智能层的责任水平。 2. 承担财务风险 AI原生性体现在：AI可以说能够自动化承担风险实体所涉及的大部分行政工作，并显著改变大规模管理人群的成本结构。 AI免疫性体现在：一个全栈式风险承担实体可能需要筹集储备资本、获得州级许可（如果是完全保险的话）、签订医疗服务提供者网络合同、与会员和医生互动以管理总护理成本，并赢得高接触度的销售。即使是纯软件企业也可以承担财务风险，例如，只有在多方交易完成或客户收到付款时才获得报酬。 3. 开发受FDA监管的产品 AI原生性体现在：AI可以越来越多地自动化诊断或药物发现和开发过程中的操作性和认知性部分。 AI免疫性体现在：公司最终仍然必须制造产品，经过多年的人体临床试验，并获得FDA批准才能对诊断或治疗益处做出明确声明。更不用说[我们对人类生物学的理解还只是触及了皮毛](https://www.a16z.news/p/drug-discovery-has-no-magic-wands)，因此我们仍然需要生成新数据并建立大规模验证循环，以扩大AI可以有效运作的搜索空间。 ![Image](/doc/4814/image/img_e6dc68fdfbce.jpg) 当然，该领域的发展速度意味着这个框架的半衰期可能比我们想象的要短。放松监管可能会降低自主AI产品在没有许可或批准的情况下做更多事情的门槛。代理式医疗服务提供者网络签约可能会降低雇主赞助市场中完全自主健康计划的进入壁垒。前沿模型可能会成熟到其CEO每天早上担心的那些生存风险变得更容易管理的程度，从而让他们有余力去探索承担更多风险、责任，最终是问责。话虽如此，许多[当今表现最好的医疗科技公司](https://www.youtube.com/watch?v=3h8e8648d-U&t=621s)已经在实施上述一种或多种模式：交付结果、承担财务风险和/或开发受监管的诊断或治疗产品。而那些重心仍停留在“更智能”或“行政自动化”的最强AI原生企业，正在积极探索如何承担更多问责——从而变得更AI免疫。 [原文发布于此](https://healthtechbuilders.substack.com/p/be-ai-native-and-ai-proof) ## 相关链接 - [Julie Yoo](https://x.com/julesyoo) - [@julesyoo](https://x.com/julesyoo) - [37K](https://x.com/julesyoo/status/2089751569063706784/analytics) - [更快、更自然](https://healthtechbuilders.substack.com/p/no-longer-a-mythical-beast-market) - [我们对人类生物学的理解还只是触及了皮毛](https://www.a16z.news/p/drug-discovery-has-no-magic-wands) - [当今表现最好的医疗科技公司](https://www.youtube.com/watch?v=3h8e8648d-U&t=621s) - [原文发布于此](https://healthtechbuilders.substack.com/p/be-ai-native-and-ai-proof) - [升级至Premium](https://x.com/i/premium_sign_up) - [2026年8月19日上午12:29](https://x.com/julesyoo/status/2089751569063706784) - [37.2K次浏览](https://x.com/julesyoo/status/2089751569063706784/analytics) - [查看引用](https://x.com/julesyoo/status/2089751569063706784/quotes) --- *导出时间: 2026/8/20 21:27:02*