内容来源：https://www.geekwire.com/2026/etzioni-on-ai-ten-commandments-for-ai-startups/

内容总结：

人工智能创业的十大“戒律”：从经典到AI时代

在《埃齐奥尼谈AI》系列专栏的第十期中，作者结合自身从1996年创立Netbot到2024年创立Vercept的亲身经历，以及在AI2孵化器和Madrona风投与众多创始人共事的经验，为AI创业者提炼出十则全新“戒律”。这些戒律不仅涵盖传统创业智慧，更直指AI时代的独特挑战。

经典创业戒律（仍不过时）

1. 真正的风险是遗憾：别等到60岁时才后悔从未尝试。
2. 慎重选择联合创始人：如同选择伴侣，合伙破裂代价巨大。
3. 追求成功概率而非股权比例：别最终拥有99%的零。
4. 用故事融资：投资者买的是你未来可能成为的公司。
5. 解决真实痛点：做“止痛药”而非“维生素”。
6. 尽早频繁接触客户：一盎司数据胜过一磅直觉。
7. 专注再专注：学会对好点子说“不”。
8. 边飞边造火箭：先起飞再迭代完善。
9. 做好颠簸准备：创业像一连串“濒死体验”，赢家是拒绝放弃的人。
10. 警惕咨询顾问：你需要的是“猪”一样的承诺，而非“鸡”一样的参与。

AI时代十大新戒律

1. “我们是AI公司”不再是优势：这是入场券。要讲清楚痛点、客户、盈利模式和时机。
2. AI技术远远不够：最重要的模型是商业模式。没有执行的愿景只是空想。
3. 别给模型“涂脂抹粉”：如果只是给别人的API贴金，前沿实验室会轻松碾压你。去它们不愿或不能去的地方。
4. 拥有数据，但别把它当护城河：真正重要的是“飞轮效应”——如Waymo每跑一英里都让下一英里更智能。
5. 速度是新护城河：谁更快变聪明，谁就赢。
6. 嵌入工作流：成为客户上班打开、下班关闭的那个应用，或悄悄接管他们已有的工具。
7. 分发是稀缺资源：建AI产品从未如此便宜，让买家看到从未如此艰难。从第一天起，分发就要和产品同等重要。
8. 别“绑定”某个模型：今天赢你的前沿模型，半年后可能沦为第三。构建可快速切换的架构。
9. 推理成本是新的销售成本：每笔查询都花钱。如果10个客户时算不过来账，1000个客户时也一样。
10. 人际关系依然最重要：AI不会为你凌晨接电话、不会在董事会上为你辩护、不会在你需要过桥融资时出现。

作者最后指出，西雅图正成为践行这些戒律的绝佳之地。AI时代已来，创业者们，出发吧。

中文翻译：

在我撰写“人工智能领域的艾齐奥尼”系列专栏的第十篇之际，我想为人工智能初创企业分享十条戒律，在此之前，先列出那些依然适用的永恒经典。这些内容汲取了我在AI2孵化器、Madrona与创始人合作的经验，以及我本人从Netbot（1996年）到Vercept（2024年）作为人工智能创始人的亲身经历。

为了看清人工智能初创企业的独特之处，我们先以适用于所有初创企业的十条戒律热身，这些戒律融入了维诺德·科斯拉、里德·霍夫曼和埃里克·里斯等初创企业元老的智慧。如果你已熟知这些经典，可直接跳至人工智能戒律部分。

1. 真正的风险是遗憾。到了六十岁坐在看台上，才意识到自己从未踏上过赛场。
2. 像选择伴侣一样慎重选择联合创始人。创业初期你与联合创始人相处的时间可能比伴侣还长。创始人分道扬镳同样痛苦万分。
3. 最大化成功的概率，而非你的股权比例。别到头来拥有百分之九十九的虚无。合适的投资人、孵化器或员工，其创造的价值将远超成本。
4. 靠故事融资。投资人购买的是你公司未来可能成为的样子。正如科斯拉所言：“不要为了逻辑顺序而扭曲你的故事。”
5. 解决真实存在的问题。它是止痛药还是维生素？初创企业卖的是止痛药。
6. 尽早并频繁与客户交流。在客户证明之前，你的假设都是错误的；一盎司的数据胜过一磅的直觉。一个与用户互动的精悍实验，能解决可能耗时数月才能解决的争论。
7. 专注、专注、再专注。对某些创始人来说，最难说出口的词是“不”。要对好点子说“不”，才能执行那个绝佳的主意。如果你的产品既是甜品顶料又是地板蜡，那它什么都不是。
8. 边飞边造火箭。你不可能在万事俱备后才起飞，需要快速迭代来摸索出其余部分。
9. 为颠簸之旅做好准备。初创企业就像一连串九死一生的经历。胜出者是那些拒绝放弃的人。
10. 提防顾问。记住那个火腿蛋的寓言：鸡只是参与，猪才是真正的投入。你需要一个全心投入的团队。任何按小时计费的人，其利益与你并不一致。

以下是一些最终未能入选的戒律：雇佣木匠而非建筑师；慢招人，快裁人；对自己的数字了如指掌，尤其是烧钱率。

其他值得一读的十诫清单包括：里德·霍夫曼的、霍华德·图尔曼的，以及由格利洛特资本更新的什洛莫·卡利什的。

这些经典依然有效，但现在是时候增加十条人工智能戒律了。

1. “我们是一家人工智能公司”已不再是差异化优势。这只是入局门槛。讲好故事：痛点是什么？客户是谁？如何赚钱？为何是现在？
2. 人工智能技术本身不够。正如Madrona的马特·麦克伊尔温所言：“最重要的人工智能模型是商业模式。”你必须兑现这个模式——没有执行的愿景只是空想。
3. 不要给模型涂脂抹粉。如果你的公司只是别人API上的一层薄薄粉饰，前沿实验室会让你死无葬身之地。他们拥有模型和分销渠道，而你两者皆无。去他们不会（或不能）涉足的领域建立优势。
4. 拥有自己的数据，但别将其误认为是护城河。专有数据有帮助，但并非应许之地。关键在于飞轮效应：Waymo从其汽车行驶的每一英里中学习，而学习让下一英里更出色。建立这个循环。
5. 速度是新的护城河。人工智能已经降低了“变聪明”的成本。优势属于谁能更快变聪明。正如a16z的布莱恩·金所说：“势头就是护城河。”
6. 嵌入工作流程。客户早上九点打开、下午六点关闭的应用，是他们无法轻易切换的。成为那样的应用。或者悄悄接管他们已经在用的应用。人工智能编码让从头重建应用层变得比以往任何时候都便宜。
7. 分销是稀缺资源。构建人工智能产品从未如此便宜。将其呈现在买家面前从未如此困难。那竞争你客户的一千家公司，不会在功能上输掉，而会在触达范围上落败。从第一天起，分销就应与产品享有同等地位。
8. 不要对单一模型从一而终。今天在你的演示中胜出的前沿模型，六个月后可能沦为第三梯队。构建灵活的技术栈，以便在“chemistry”不再时轻松更换。
9. 推理是新的销售成本。每次查询都消耗真金白银，且成本随用户数增长。贝塞麦直言不讳：“如果10个客户时数学算不过来，1000个客户时也一样。”在增长之前，先清楚你的产品成本。
10. 人际关系仍然最重要。人工智能无法赢得信任。它不会在午夜接听你的电话，不会在董事会上为你辩护，也不会在你需要过桥融资时出现。

西雅图是践行这些戒律的绝佳之地：人工智能之家已在海滨开业；新基金已筹集；数百家初创企业正在蓬勃发展。

人工智能初创企业的时机就是现在。去创造吧。

英文来源：

For my tenth column in the “Etzioni on AI” series, I want to share ten commandments for AI startups, preceded by timeless classics that still apply. They draw on my work with founders at the AI2 Incubator, Madrona, and my own experience as an AI founder from Netbot (1996) to Vercept (2024).
To see what’s different for AI startups, let’s warm up with ten commandments for startups in general, which fold in the wisdom of startup stalwarts such as Vinod Khosla, Reid Hoffman, and Eric Ries. If you already know the classics, jump straight to the AI commandments.

1. The real risk is regret. It’s sitting in the stands at 60 and realizing you never stepped up to the plate.
2. Choose your co-founder as carefully as a spouse. You may end up spending more time with your co-founder in the early years. And founder breakups are also very painful.
3. Maximize your odds of success, not your ownership stake. Don’t end up owning 99% of nothing. The right investor, incubator, or hire will add far more value than cost.
4. Raise on the story. Investors are buying the company you could become. As Khosla puts it: “Don’t subvert your story in service of logical order.”
5. Solve a real problem. Is it a painkiller or a vitamin? Startups sell painkillers.
6. Talk to your customers early and often. Your assumptions are wrong until a customer proves otherwise; an ounce of data is worth a pound of intuition. A scrappy experiment engaging users settles arguments that could otherwise take months to resolve.
7. Focus, focus, focus. The hardest word for some founders is “no.” Say it to good ideas so you can execute the one great one. If your product is both a dessert topping and a floor wax, it’s neither.
8. Build the rocket while you’re flying it. You launch without every answer in place and iterate quickly to figure the rest out.
9. Be ready for a rough ride. A startup can seem like a series of near-death experiences. The winners are the ones who refused to quit.
10. Beware of consultants. Remember the ham-and-eggs adage: the chicken is involved, but the pig is committed. You want a committed team. Anyone running a meter has incentives misaligned with yours.
    Here are a few commandments that didn’t make the cut: hire carpenters, not architects; hire slowly, fire fast; know your numbers cold, especially your burn rate.
    Other ten-commandment lists worth perusing include: Reid Hoffman’s, Howard Tullman’s, and Shlomo Kalish’s, as updated by Glilot Capital.
    These classics still rule, but it’s time to add ten AI commandments.
11. “We’re an AI company” is no longer a differentiator. It’s table stakes. Tell the story: what’s the pain point? Who’s the customer? How do you make money? Why now?
12. AI technology is not enough. As Madrona’s Matt McIlwain puts it, “the most important AI model is the business model.” And you have to deliver against that model — vision without execution is hallucination.
13. Don’t put lipstick on a model. If your company is a skin-deep gloss over someone else’s API, the frontier labs will eat you alive. They have the model and the distribution. You have neither. Build where they won’t (or can’t) go.
14. Own your data, but don’t mistake it for the moat. Proprietary data helps, but it’s not the promised land. What matters is the flywheel: Waymo learns from every mile its cars drive, and the learning makes the next mile better. Build that loop.
15. Velocity is the new moat. AI has collapsed the cost of being smart. The edge belongs to whoever is smart faster. As a16z’s Bryan Kim writes, “momentum is the moat.”
16. Embed in the workflow. The application your customer opens at 9am and closes at 6pm is the one they can’t switch off. Become that. Or quietly take over the one they already use. AI coding makes it cheaper than ever to recreate the application layer from scratch.
17. Distribution is the scarce resource. Building an AI product has never been cheaper. Getting it in front of buyers has never been harder. The thousand companies competing for your customer won’t lose on features. They’ll lose on reach. Distribution deserves equal billing with product from day one.
18. Don’t marry a model. The frontier model that wins your demo today will be third-best in six months. Build the stack so you can make an easy change if the chemistry fades.
19. Inference is the new COGS. Every query costs real money, and the cost scales with every user. Bessemer puts it bluntly: “If the math doesn’t work at 10 customers, it won’t at 1,000.” Know what your product costs you before you grow.
20. Personal relationships still matter most. AI doesn’t earn trust. It won’t take your call at midnight or defend you to the board, and it won’t be there when you need a bridge round.
    Seattle is a phenomenal place to live by these commandments: AI House has opened on the waterfront; new funds have been raised; and hundreds of startups are thriving.
    The time for AI startups is now. Go build.