# Lighthouse or Landgrab?
**作者**: Joe Schmidt IV
**日期**: 2026-07-27T15:52:24.000Z
**来源**: [https://x.com/joeschmidtiv/status/2081769683066421522](https://x.com/joeschmidtiv/status/2081769683066421522)
---
That logo you’re chasing is costing you your market.
Founders with great AI products are spending months courting their first Fortune 100 customers, burning through funding rounds, and tying up their teams on deals that will never convert. They chase these logos not because the deals are big, but because a marquee name on a sales deck supposedly makes every deal after it easier. So founders focus all their attention on them, and often even offer steep discounts (or pay customers to use them!) to land them. The founders don’t care that the revenue barely registers. The logo is the whole point. Meanwhile the buyers who actually need the software, and would pay full price for it, have never heard the company's name.
It's not that there's no logic to this. AI is new, so founders assume they have to educate the market, and they default to the Lighthouse strategy: win a few marquee customers, build social proof, and reassure the buyer who's afraid of making the wrong call. But for a lot of AI companies that instinct is exactly backwards. Their buyers already understand the problem and aren't afraid of getting it wrong. They just need the math to work, and every week spent chasing prestigious logos is a week a competitor spends selling in Ohio. Those companies would be better off pursuing the Landgrab strategy: win on math, move fast, and sign the largest number of customers possible, logo be damned.
There are two GTM playbooks for AI companies selling into the enterprise: the Lighthouse and the Landgrab. The difference between them isn't about product quality or team strength. It's about what you're selling and who you're selling to.
## The Lighthouse
Category creation is when AI enables work that couldn't be done before. This kind of solution has no precedent inside the buyer's organization, no incumbent to displace, and no existing mental model in the buyer's head. You're not replacing Salesforce but inventing something new, and that often means you're asking buyers to take a leap. Social proof is everything so you need lighthouse customers whose adoption signals that this category is real and you're the one to bet on.
Harvey built AI for legal professionals, a category that now feels entrenched but only a few years ago had no players, let alone customers. Law firms were buying research tools from Thomson Reuters and LexisNexis, which surface information for an associate to interpret. Meanwhile, Harvey does all that work itself: drafting, research, and due diligence across thousands of documents. Lawyers are trained to avoid risk, so no firm wanted to go first, but when Allen & Overy signed in late 2022, followed by Paul Weiss in early 2023, every peer took notice. Today Harvey has hundreds of millions in ARR and an $11 billion valuation, but it first needed A&O and Paul Weiss to tell the legal industry AI-powered legal work was real.
Hebbia ran the same playbook in financial services, building an AI intelligence platform for firms whose teams spend 60+ hour weeks poring over high-stakes data rooms. In this world of highly confidential deals and guarded reputations, no fund wanted to go first either. Hebbia broke through with the world's largest private equity firms, hedge funds, and consultancies as early customers and then expanded to more than 40% of the largest asset managers by AUM, including KKR and BlackRock. Just as with Harvey, marquee names went first and the market followed.
Capturing lighthouses is a small-team, founder-led, and high-touch effort. Applied in the right markets, it lands large deals: six figure ACVs at least, often seven figures early on. Sales cycles can run three to six months (or longer) because you're navigating POCs, custom work, and buyers wary of costly mistakes. The team that closes the deal is often the same team that delivers the product, which is expensive and unscalable by design. Going first can come with outsized rewards for buyers, so the best lighthouse sellers make the leap feel like a chance to win big instead of a chance to be wrong.
The Landgrab
The game flips entirely when the buyer already knows the problem and a mistake won't cost them their job. They understand what you're selling, and the pitch is simple: "I replace Y at a lower cost or with a better outcome." You don't need Stripe's CTO vouching for you to get a meeting. You get the meeting by showing a VP of Support their current spend and saying "we cut this in half." Social proof still helps you close faster here, but the market already believes in what you’re selling.
In these markets, sacrificing speed is deadly because founders are not only competing with other startups, but also with incumbents building AI. Sure, Zendesk adding an AI copilot is fundamentally different from Decagon replacing the entire support function with agents. But the incumbent already owns the customer, and the more AI it adds each quarter (whether it’s make or buy), the harder it becomes to pull that customer away. That is why speed is the whole game. In the words of our colleague Alex Rampell: you need to get distribution before the incumbent gets innovation. The way you do that is with a landgrab.
Take Stuut, which automates accounts receivable: collections, payments, cash application, and dispute resolution. SAP and Oracle ship AR modules in their ERP suites, and players like HighRadius have sold point solutions since the early 2000s. Yet teams still lose countless hours chasing invoices. Stuut finally solves this: customers increase cash flow by 40%, cut manual tasks by 70%, and lower their collection period by 37%. The company went wide early, leaning into the lower middle market over Fortune 100 logos, and now serves manufacturers, distributors, and logistics companies across Michigan, Ohio, Texas, and beyond, deploying in under a week against the 6 to 18 months of a traditional rollout.
Decagon used a similar motion to win in customer support. The founders ran roughly a hundred customer conversations in a month before building their product, then sold on rapid deployment and immediate ROI, scaling from 0 to 8 figures in ARR in 18 months. In 2025 alone, the company signed more than 100 new enterprise customers across travel, finance, health, and retail, tripling its valuation to $4.5 billion in under six months.
Landgrab selling is demo-driven and relies on a larger team. The product needs to be standardized enough that a customer can onboard fast and see value quickly. Implementation is driven by forward-deployed teams specialized in delivery, rather than discovery. The unit economics have to work at volume because volume is the whole strategy.
Picking Your Strategy
Enterprise sales is fundamentally about risk and reward. Behind every deal is a person who has to sign their name to the decision, and who wants the same thing all of us want: for the thing they approved to work, and to still have their job next year. Instead of evaluating your product in the abstract, they're deciding how much personal exposure it creates, and what evidence would make that exposure bearable.
That's why two questions help you draw the map and decide which strategy you should pick.
How exposed is the buyer who signs? Not every mistake costs the same. In customer support or AR automation, a faulty reply or misstated invoice annoys a customer and gets fixed - the buyer's downside is a bad quarter, not a bad career. A buyer’s exposure climbs with three things: whether the industry is regulated enough that a vendor mistake becomes the buyer's compliance problem, whether you're replacing a system of record or adding a tool alongside one, and whether the output faces the outside world (a filed document or customer-facing answer vs. an internal draft someone reviews). In law and financial services all three run hot, and one fabricated figure can misprice a position or sink a deal. For those buyers, the ROI math is beside the point. They're managing personal downside no discount can offset.
Does social proof travel? In some markets, reputation carries well, while in others it does not. In financial services and law, firms watch each other obsessively, status is legible, and landing two marquee firms moves the whole market: whoever went first has effectively done the risk assessment for everyone behind them. In AR at mid-market firms, the controller in Des Moines does not care that a household-name brand uses your product and is never going to hear about it anyway. Buyers there don't watch each other closely, so each sale starts from zero and a marquee logo buys you less. Concentrated, status-driven markets carry proof while fragmented ones make you earn every deal on the math.
Put these two questions together and you have the map. When exposure is high and proof travels, you're in lighthouse territory: a few credible buyers going first unlock the whole market, the way it played out in legal work and financial research. When mistakes are recoverable and proof travels less, you're in landgrab territory: math closes the deal, and coverage wins the market, which is what is happening in customer support and AR automation. The other two corners matter less to enterprise sellers but are worth naming. When proof travels but isn't required, you may not initially need a large sales team; the product spreads itself, for instance engineer to engineer, the way dev tools do, and category creation happens bottom-up. Finally, when the buyer needs proof but the logos never reach them, you're in a hard market and you likely haven't heard of the companies stuck there.
You'll notice other patterns that seem to predict the motion: SMB skews landgrab, risk-averse industries skew lighthouse, additive tools move faster than system-of-record replacements. Trace any of these back far enough and you land on exposure or proof. The controller at a 200-person distributor is a landgrab buyer because her market is fragmented and her deal is small. The industry code on the account never entered into it.
Markets don't always fall cleanly into one bucket, and the two questions above lead to the clearest conclusion at the extremes. For the ambiguous cases, a few other factors are also worth weighing. For instance, an existing budget and napkin ROI usually confirm you're in landgrab territory, but only after the exposure question clears. A buyer can have the budget, see the math, and still refuse to move until someone credible goes first. When the two point in opposite directions, exposure wins every time.
Sales cycles can provide another gut check to identify which territory you're in. If they run longer than 60 days, if you're doing custom work to prove the concept, if the buyer asks "is this safe?" before "what does it cost?", your buyers need proof, and you need to run the lighthouse. The more common mistake right now is the opposite: assuming buyers need proof just because the technology is AI and therefore feels new. But the controller whose worst case is a misstated invoice isn't managing career risk. Show up with a marquee logo instead of a number and you've answered a question they never asked.
## The Traps
Lighthouse Traps
Becoming a hostage to the logo. Everyone wants the same marquee logos, and you end up in a brutal fight for the same 500 accounts that every other AI startup is pitching while the logos extract concessions because they know you're desperate. Lighthouse customers are a means to an end. Get a few trusted names, then run the table. The vast majority of revenue sits in companies no one's heard of.
Prestige without payback. The wrong customer won't collaborate on repeatable software (you become a consulting shop), won't pay recurring (unsustainable economics), or won't pay high enough ACVs (the math breaks). Worst case: a prestigious logo that teaches you nothing replicable and underpays, and you've traded time for a vanity metric.
Pilot purgatory. Big logos love pilots. Six-month POCs that never convert, burning your best people on deals that were never real. The fix is time-boxed pilots with clear milestones and auto-converting contracts.
A lighthouse for one. You over-rotate on your lighthouse customer's requests and build a product perfect for them and useless for everyone else. You win the lighthouse but no other ships follow the light in.
Landgrab Traps
Dying of indigestion. When you can sell to anyone the discipline is saying no. Some customers are poison: hard to onboard, hard to drive results for, low ACV. Without qualification discipline and a deal desk, you wake up with 200 customers and 50 underwater.
Grabbing land you can’t hold. You're selling at volume, and if you scale coverage before the product is ready you create detractors at scale. 50 unhappy customers means churn. 500 is a reputation problem.
Mistaking the valley for the market. Canvassing every bus stop in a major metro isn’t grabbing land. The real opportunity is figuring out how to show your ROI to the 50,000 companies outside of normal networks.
Sequencing: Lighthouse to Landgrab
The best companies don't stay in one mode forever. They sequence from lighthouse to landgrab deliberately, getting a bellwether in one vertical, dominating that vertical, and then finding adjacent verticals that look similar. At Affirm (a story worth its own post), the breakthrough was Casper. Once they had one mattress company they got every mattress company, and then they went to exercise equipment, and then to things that look like exercise equipment but aren't. Mattresses and Pelotons have nothing in common except that they're big-ticket items people want to pay for over time, and Affirm saw that before the market did.
Eventually, new categories turn into recognized ones because the lighthouse customers define them. But you have to earn the transition. Going wide before the category is established burns cash and credibility. The signal that you've earned it is buyers approaching you with allocated budgets and asking for a demo rather than asking who went first. When that happens, the lighthouse worked, and it's time to run the landgrab.
## Closing
Every AI founder believes they're inventing the future. And they are! But your buyer doesn't purchase the future; they purchase either proof or math. If they need proof, go win the logo that gives it to them. If they need math, get on a plane and show it to them before your competitor or the incumbent does.
The founders who get this wrong won't fail because they built the wrong product or picked the wrong strategy off a menu. They'll fail because they never asked which game they were in, and in a market moving this fast, you only get to ask it once.
Thank you to my coauthor @jhwmarx as well as Alex Rampell, Justin Kahl, Santi Rodriguez, Spencer Wiedeman, Frank Golden, and Elena Burger for their thoughts on this post.
## 相关链接
- [Joe Schmidt IV](https://x.com/joeschmidtiv)
- [@joeschmidtiv](https://x.com/joeschmidtiv)
- [326K](https://x.com/joeschmidtiv/status/2081769683066421522/analytics)
- [spend 60+ hour weeks poring over high-stakes data rooms](https://www.hebbia.com/newsroom/hebbia-raises-usd30-million-led-by-index-ventures-to-launch-the-future-of)
- [the world's largest private equity firms, hedge funds, and consultancies](https://www.hebbia.com/newsroom/hebbia-raises-usd30-million-led-by-index-ventures-to-launch-the-future-of)
- [more than 40% of the largest asset managers by AUM](https://www.hebbia.com/newsroom/hebbia-and-intercontinental-exchange-bring-institutional-pricing-data-into-ai-workflows)
- [Alex Rampell](https://a16z.com/distribution-vs-innovation/)
- [increase cash flow by 40%, cut manual tasks by 70%, and lower their collection period by 37%](https://www.prnewswire.com/news-releases/stuut-technologies-raises-29-5-million-series-a-led-by-andreessen-horowitz-to-automate-accounts-receivable-work-302621866.html)
- [lower middle market](https://youtu.be/P-Nse7c9gS4?t=1382)
- [deploying in under a week](https://www.prnewswire.com/news-releases/stuut-technologies-raises-29-5-million-series-a-led-by-andreessen-horowitz-to-automate-accounts-receivable-work-302621866.html)
- [hundred customer conversations](https://youtu.be/OatHFsqPr2c?t=326)
- [0 to 8 figures in ARR](https://www.saastr.com/from-zero-to-eight-figures-in-18-months-decagon-ceos-playbook-for-ai-native-saas-growth-and-why-they-partnered-with-accel/)
- [100 new enterprise customers](https://decagon.ai/blog/series-d-announcement)
- [valuation to $4.5 billion](https://www.bloomberg.com/news/articles/2026-01-28/ai-customer-support-startup-decagon-valued-at-4-5-billion)
- [@jhwmarx](https://x.com/@jhwmarx)
- [Upgrade to Premium](https://x.com/i/premium_sign_up)
- [11:52 PM · Jul 27, 2026](https://x.com/joeschmidtiv/status/2081769683066421522)
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---
*导出时间: 2026/7/28 22:07:34*
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## 中文翻译
# 灯塔还是圈地？
**作者**: Joe Schmidt IV
**日期**: 2026-07-27T15:52:24.000Z
**来源**: [https://x.com/joeschmidtiv/status/2081769683066421522](https://x.com/joeschmidtiv/status/2081769683066421522)
---
你苦苦追逐的那个大客户Logo，正在让你赔掉整个市场。
拥有出色AI产品的创始人们花费数月时间去迎合他们的第一批财富100强客户，烧完一轮又一轮的融资，把团队困在那些永远无法成交的交易上。他们追逐这些Logo，不是因为交易规模大，而是因为销售演示材料上有一个响亮的名字，据说会让之后的每一笔交易都更容易谈成。于是创始人将全部精力都集中在他们身上，甚至经常提供大幅折扣（或者甚至倒贴钱请客户使用！）以求签下订单。创始人们不在乎收入微乎其微。Logo才是全部重点。与此同时，那些真正需要这款软件并且愿意全价购买的买家，却从未听说过这家公司的名字。
这并非没有道理。AI是新鲜事物，所以创始人认为他们必须教育市场，他们默认采用“灯塔”策略：赢得几个标志性客户，建立社会认同，并安抚那些害怕做出错误决定的买家。但对于许多AI公司来说，这种本能恰恰是背道而驰的。他们的买家已经理解了问题，并不担心出错。他们只需要账算得通，而每一周花在追逐知名Logo上的时间，都是竞争对手在俄亥俄州进行销售的一周。这些公司最好采用“圈地”策略：靠算账赢，快速行动，尽可能签下最多的客户，去他的Logo。
向企业销售的AI公司有两套GTM（进入市场）剧本：灯塔模式和圈地模式。它们之间的区别不在于产品质量或团队实力。而在于你在卖什么，以及你在卖给谁。
## 灯塔
品类创造是指AI使以前无法完成的工作成为可能。这种解决方案在买家组织内部没有先例，没有需要取代的 incumbents（现有巨头），买家脑海中也没有现成的心理模型。你不是在取代Salesforce，而是在发明某种新东西，这通常意味着你要求买家跨越思维的鸿沟。社会认同就是一切，所以你需要灯塔客户，他们的采用向市场发出信号：这个品类是真实的，而你才是值得押注的对象。
Harvey为法律专业人士构建了AI工具，这个品类现在看起来似乎根深蒂固，但在仅仅几年前还没有任何参与者，更不用说客户了。律师事务所以前是从汤森路透和律商联讯购买研究工具，这些工具负责整理信息供律师解读。与此同时，Harvey自己完成了所有这些工作：起草、研究以及针对数千份文件的尽职调查。律师受训时要规避风险，所以没有哪家律所愿意做第一个吃螃蟹的人，但当Allen & Overy在2022年底签约，随后Paul Weiss在2023年初跟进时，同行都注意到了。今天Harvey拥有数亿美元的ARR（年度经常性收入）和110亿美元的估值，但它首先需要A&O和Paul Weiss来告诉法律界，AI驱动的法律工作是真实存在的。
Hebbia在金融服务领域也采用了同样的剧本，为公司构建AI情报平台，这些公司的团队每周要花费60多个小时仔细审查高风险数据室。在这个高度保密交易和严守声誉的世界里，也没有基金愿意做第一个。Hebbia通过让世界上最大的私募股权公司、对冲基金和咨询公司成为早期客户而取得了突破，随后扩展到按资产管理规模（AUM）计算最大的资产管理公司中超过40%的客户，包括KKR和贝莱德。就像Harvey一样，知名名字先行，市场随后跟进。
拿下灯塔客户是一个小团队、创始人主导且高接触度的努力。在正确的市场中应用，它能带来大额交易：至少六位数的ACV（年度合同价值），通常早期就能达到七位数。销售周期可能长达三到六个月（或更长），因为你要应对概念验证（POC）、定制工作以及担心付出昂贵代价的买家。完成交易的产品交付团队往往是同一拨人，这成本高昂且在设计上就是不可扩展的。做先行者可能会给买家带来超额回报，所以最好的灯塔销售者会让这一步跨越感觉像是一次大赢的机会，而不是一次可能出错的机会。
### 圈地
当买家已经了解问题，而且一个错误不会让他们丢掉工作时，游戏规则就完全改变了。他们理解你在卖什么，推销辞令很简单：“我以更低的成本或更好的结果取代Y。”你不需要Stripe的首席技术官为你背书就能获得会议机会。你获得会议机会的方法是向一位支持副总裁展示他们目前的支出，并说“我们可以把这笔费用减半”。社会认同在这里仍然有助于你更快成交，但市场已经相信你卖的东西了。
在这些市场中，牺牲速度是致命的，因为创始人不仅要与其他初创公司竞争，还要与正在构建AI的现有巨头竞争。当然，Zendesk添加一个AI副驾驶与Decagon用智能体完全取代整个支持功能有着根本的不同。但巨头已经拥有客户，而且他们每季度增加的AI功能（无论是自建还是购买）越多，要把那个客户挖走就越难。这就是为什么速度就是整个游戏。用我们同事Alex Rampell的话说：你需要在巨头实现创新之前先获得分销渠道。要做到这一点，就需要圈地。
以Stuut为例，它自动化应收账款（AR）：催收、付款、现金核销和争议解决。SAP和Oracle在其ERP套件中提供AR模块，而像HighRadius这样的玩家自2000年代初以来一直在销售点解决方案。然而，团队仍然在追赶发票上浪费无数时间。Stuut最终解决了这个问题：客户现金流增加了40%，手动任务减少了70%，收款周期缩短了37%。该公司很早就铺开市场，倾向于关注中低端市场而不是财富100强的Logo，现在为密歇根州、俄亥俄州、德克萨斯州及其他地区的制造商、分销商和物流公司提供服务，部署时间不到一周，而传统 rollout 需要6到18个月。
Decagon使用了类似的动作在客户支持领域获胜。创始人在构建产品前大约一个月内进行了大约100次客户对话，然后销售快速部署和即时ROI，在18个月内将ARR从0扩展到八位数。仅2025年一年，该公司就在旅游、金融、健康和零售领域签约了100多家新的企业客户，在不到六个月的时间内将其估值翻了三倍，达到45亿美元。
圈地销售是演示驱动的，依赖更大的团队。产品需要足够标准化，以便客户能够快速上手并迅速看到价值。实施由专门负责交付的前置团队推动，而不是探索团队。单体经济模型必须在批量下成立，因为批量就是整个策略。
## 选择你的策略
企业销售从根本上说是关于风险和回报。每笔交易背后都有一个必须签字决策的人，他们和我们所有人想要的一样：希望他们批准的东西能奏效，并且明年还能保住工作。他们不是在抽象地评估你的产品，而是在决定这会给他们带来多大的个人风险敞口，以及什么证据能让这种风险变得可以承受。
这就是为什么有两个问题可以帮助你绘制地图并决定你应该选择哪种策略。
### 签字的买家风险敞口有多大？并非每个错误的代价都一样。在客户支持或AR自动化中，错误的回复或错误的发票会惹恼客户并得到修正——买家的不利之处只是一个糟糕的季度，而不是糟糕的职业生涯。买家的风险敞口随着三件事而增加：行业是否受到足够监管，以至于供应商的错误会成为买家的合规问题；你是在取代记录系统还是在旁边添加一个工具；以及输出结果是否面向外部世界（提交的文件或面向客户的答案，还是某人审查的内部草稿）。在法律和金融服务领域，这三点都非常敏感，一个编造的数字就可能导致定价错误或搞砸一笔交易。对于这些买家来说，ROI数学并不重要。他们是在管理个人下行风险，这是任何折扣都无法抵消的。
### 社会认同能传播吗？在有些市场中，声誉传播得很好，而在其他市场则不然。在金融服务和法律领域，公司互相密切关注，地位清晰可见，拿下两家知名公司就能推动整个市场：谁先走一步，实际上就为后面所有人做了风险评估。在中端市场的AR领域，得梅因的会计主管并不在乎某个家喻户晓的品牌在使用你的产品，而且反正也听不到这方面的消息。那里的买家不会密切互相关注，所以每一笔销售都从零开始，知名Logo能给你带来的帮助就很少。集中的、地位驱动的市场承载着证明，而分散的市场则让你必须靠算账去赢得每一笔交易。
把这两个问题放在一起，你就有了地图。当风险敞口高且证明能传播时，你就处于灯塔领域：少数几个可信的买家先行就能解锁整个市场，就像法律工作和金融研究那样。当错误是可以挽回的且证明传播较少时，你就处于圈地领域：算账促成交易，覆盖面赢得市场，这正是客户支持和AR自动化正在发生的情况。另外两个角对企业销售者来说不那么重要，但值得提一下。当证明能传播但并非必要时，你最初可能不需要庞大的销售团队；产品自我传播，例如工程师对工程师，就像开发工具那样，品类创造是自下而上发生的。最后，当买家需要证明但Logo永远传达不到他们那里时，你就处于一个艰难的市场，你可能也没听说过那些被困在那里的公司。
你会注意到其他似乎能预测行动模式的规律：SMB（中小企业）倾向于圈地，规避风险的行业倾向于灯塔，附加工具比记录系统替代品移动得更快。追溯其中任何一个规律足够远，你都会落脚于风险敞口或证明。一家200人分销商的会计主管是圈地买家，因为她的市场是分散的，而且她的交易规模很小。账户上的行业代码根本与此无关。
市场并不总是干净利落地落入一个桶中，上述两个问题在极端情况下能得出最清晰的结论。对于模糊的情况，其他一些因素也值得权衡。例如，现有的预算和餐巾纸上的ROI通常确认你处于圈地领域，但这只是在风险敞口问题通过之后。买家可能有预算，看懂数学，但仍然拒绝行动，直到某个可信的人先行。当两者指向相反方向时，风险敞口每次都赢。
销售周期可以提供另一种直觉检查，以识别你处于哪个领域。如果周期超过60天，如果你需要做定制工作来证明概念，如果买家在问“这要花多少钱？”之前先问“这安全吗？”，那么你的买家需要证明，你需要运行灯塔策略。现在更常见的错误恰恰相反：仅仅因为技术是AI且感觉新鲜，就假设买家需要证明。但是，对于最坏情况只是一张错误发票的会计主管来说，她并不是在管理职业风险。如果你拿着一个知名Logo而不是一个数字出现，你就回答了一个他们从未问过的问题。
## 陷阱
### 灯塔陷阱
**成为Logo的人质**。每个人都想要同样的知名Logo，你最终陷入了一场残酷的争夺战，争夺每家其他AI初创公司都在推销的那500个账户，而Logo方会榨取让步，因为他们知道你很绝望。灯塔客户只是达到目的的手段。获得几个受信任的名字，然后横扫全场。绝大多数收入在于那些没人听说过的公司。
**有声望无回报**。错误的客户不会合作开发可重复的软件（你会变成一家咨询公司），不会支付经常性费用（不可持续的经济模式），或者不会支付足够高的ACV（数学逻辑崩塌）。最坏的情况：一个享有声望的Logo，没教给你任何可复制的东西，还给钱太少，你用时间换取了一个虚荣指标。
**试点炼狱**。大公司喜欢试点。六个月的POC永远不会转化，把你最好的人员浪费在从未成真的交易上。解决方法是有时间限制的试点，有明确的里程碑和自动转化的合同。
**孤家寡人灯塔**。你过度迎合灯塔客户的要求，构建了一个对他们来说完美但对其他人无用的产品。你赢得了灯塔，但没有其他船只跟随这束光驶入。
### 圈地陷阱
**死于消化不良**。当你能卖给任何人时，纪律就在于说“不”。有些客户是有毒的：难以上手，很难为其带来成果，ACV低。如果没有资格筛选纪律和交易审核台，你会醒来发现拥有200个客户，其中50个在水下挣扎。
**抢了你守不住的地**。你在批量销售，如果在产品准备好之前扩大覆盖面，你就会批量制造贬低者。50个不满意的客户意味着流失。500个就是声誉问题。
**把山谷当成了市场**。在大都市的每个公交站 canvassing（拉票/推销）并不是圈地。真正的机会是弄清楚如何向正常网络之外的50,000家公司展示你的ROI。
## 顺序：从灯塔到圈地
最好的公司不会永远停留在一种模式。他们有意地按顺序从灯塔转移到圈地，在一个垂直领域获得一个风向标，主导该垂直领域，然后寻找看起来相似的相邻垂直领域。在Affirm（一个值得单独写一篇帖子的故事），突破点是Casper。一旦他们有了一家床垫公司，他们就拿下了每一家床垫公司，然后他们去了健身器材领域，然后是那些看起来像健身器材但其实不是的东西。床垫和Peloton除了都是人们希望分期付款的大件商品外没有任何共同点，而Affirm在市场意识到这一点之前就看到了。
最终，新品类会变成公认的品类，因为灯塔客户定义了它们。但你必须赢得这种转变。在品类建立之前就铺开市场会烧掉现金和信誉。你已经赢得这一转变的信号是，买家带着已分配的预算主动找上门，要求演示，而不是问“谁是第一个”。当这种情况发生时，灯塔起作用了，是时候运行圈地策略了。
## 结语
每位AI创始人都相信他们在发明未来。他们确实是在发明未来！但你的买家不购买未来；他们购买的是证明或者算账。如果他们需要证明，就去赢得那个能提供证明的Logo。如果他们需要算账，就坐上飞机，在你的竞争对手或巨头之前把它展示给他们看。
犯错的创始人之所以失败，不是因为构建了错误的产品或从菜单上挑选了错误的策略。他们失败是因为他们从未问过自己处于哪种游戏中，而在一个如此快速变化的市场中，你只有一次提问的机会。
感谢我的合著者 @jhwmarx 以及 Alex Rampell、Justin Kahl、Santi Rodriguez、Spencer Wiedeman、Frank Golden 和 Elena Burger 对本文的想法。
## 相关链接
- [Joe Schmidt IV](https://x.com/joeschmidtiv)
- [@joeschmidtiv](https://x.com/joeschmidtiv)
- [326K](https://x.com/joeschmidtiv/status/2081769683066421522/analytics)
- [spend 60+ hour weeks poring over high-stakes data rooms](https://www.hebbia.com/newsroom/hebbia-raises-usd30-million-led-by-index-ventures-to-launch-the-future-of)
- [the world's largest private equity firms, hedge funds, and consultancies](https://www.hebbia.com/newsroom/hebbia-raises-usd30-million-led-by-index-ventures-to-launch-the-future-of)
- [more than 40% of the largest asset managers by AUM](https://www.hebbia.com/newsroom/hebbia-and-intercontinental-exchange-bring-institutional-pricing-data-into-ai-workflows)
- [Alex Rampell](https://a16z.com/distribution-vs-innovation/)
- [increase cash flow by 40%, cut manual tasks by 70%, and lower their collection period by 37%](https://www.prnewswire.com/news-releases/stuut-technologies-raises-29-5-million-series-a-led-by-andreessen-horowitz-to-automate-accounts-receivable-work-302621866.html)
- [lower middle market](https://youtu.be/P-Nse7c9gS4?t=1382)
- [deploying in under a week](https://www.prnewswire.com/news-releases/stuut-technologies-raises-29-5-million-series-a-led-by-andreessen-horowitz-to-automate-accounts-receivable-work-302621866.html)
- [hundred customer conversations](https://youtu.be/OatHFsqPr2c?t=326)
- [0 to 8 figures in ARR](https://www.saastr.com/from-zero-to-eight-figures-in-18-months-decagon-ceos-playbook-for-ai-native-saas-growth-and-why-they-partnered-with-accel/)
- [100 new enterprise customers](https://decagon.ai/blog/series-d-announcement)
- [valuation to $4.5 billion](https://www.bloomberg.com/news/articles/2026-01-28/ai-customer-support-startup-decagon-valued-at-4-5-billion)
- [@jhwmarx](https://x.com/@jhwmarx)
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- [11:52 PM · Jul 27, 2026](https://x.com/joeschmidtiv/status/2081769683066421522)
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*导出时间: 2026/7/28 22:07:34*