Name: 打造新創公司的最強心法！ (The New Way To Build A Startup)
Uploaded: 2026-02-15T21:33:54.000Z
Duration: 7 min 51 s
Description: 【看影片學英語】數萬部 YouTube 影片，搭配英漢字典即點即查，輕鬆掌握單字發音與用法，長久累積看電影不必再看字幕。

If you haven't tried Claude code in the last month, it's time to give it another shot.

如果你上個月沒有試過 Claude 的程式碼，是時候再給它一次機會了。

And if you have, you know what I'm talking about.

如果你試過了，你就會知道我在說什麼。

One of Anthropic's own engineers writes Claude wrote Claude cowork.

Anthropic 自家的工程師寫道，Claude 寫了 Claude 的協同作業。

Us humans meet in person to discuss foundational architecture and product decisions, but

我們人類會親自開會討論基礎架構和產品決策，但是

all of us devs manage anywhere between three and eight Claude instances, implementing features, features, fixing bugs, or researching potential solutions.

我們這些開發者會管理三到八個 Claude 實例，實施 features、features、修復 bugs，或是研究潛在的解決方案。

The team developing one of the most sophisticated AI products in the world, something many of you probably use every day, is using this AI internally to improve their product.

開發世界上最複雜的 AI 產品之一的團隊，而你們很多人可能每天都在使用這個產品，他們正在內部使用這個 AI 來改進他們的產品。

I think this points to a fundamental shift in how startups operate right now.

我認為這指向了現在新創公司營運方式的根本轉變。

The best teams aren't automating one or two internal functions, they they're automating all of them.

最頂尖的團隊不是自動化一兩個內部功能，而是自動化所有功能。

Often they're tiny teams able to beat huge incumbents thanks to internal automation.

很多時候，他們是小型團隊，靠著內部自動化打敗了龐大的現有公司。

他們的精簡就是他們的超能力。

I've been calling these startups 20x companies.

我一直稱這些新創公司為 20 倍公司。

Several years ago, my friend Parker Conrad, founder of Rippling and Zenefits, coined the term compound startup to describe companies that build multiple integrated products in parallel rather than focusing narrowly on one thing.

幾年前，我的朋友 Parker Conrad，也就是 Rippling 和 Zenefits 的創辦人，創造了「複合式新創公司」這個詞，用來形容那些同時開發多個整合性產品，而不是隻專注於一件事的公司。

The theory of like the compound software business is that there's this island of product market fit that's kind of over the edge of the horizon line that's sort of harder to get to.

複合式軟體事業的理論是，有一個產品市場契合的島嶼，它就在地平線的邊緣，比較難抵達。

But if you can build, you know, multiple parallel applications at once, you can get there and it actually ends up being a much more powerful type of product market fit that's much harder to displace at that point.

但如果你能同時開發多個平行應用程式，你就能到達那裡，而且實際上會變成一種更強大、更難被取代的產品市場契合。

The 20x company could be an evolution of Parker's idea, but applied to internal automation.

20 倍公司可以說是 Parker 想法的演進，但應用在內部自動化上。

Instead of just narrowly automating a few things like writing code or handling customer support, 20x companies build automations across all all internal features, code support, marketing, sales, hiring, QA and more.

20 倍公司不只是狹隘地自動化寫程式或處理客服等少數幾件事，而是將自動化應用到所有內部功能，包括程式碼、客服、行銷、銷售、招聘、QA 等等。

This makes each of their employees orders of magnitude more powerful than they would be otherwise.

這讓他們每個員工的力量都比沒有自動化時強大幾個數量級。

It also allows them to postpone hiring additional sales and ops staff for much longer, keeping payroll down and culture from drifting.

這也讓他們能更長時間地延後招聘額外的銷售和營運人員，降低薪資支出，並避免文化飄移。

The phrase 20x company was actually coined by the founders of GigAML, which builds voice based customer service agents for enterprise, to describe how they managed to close doordash as a customer, going up against incumbents that were literally 20x as large.

「20 倍公司」這個詞其實是 GigAML 的創辦人發明的，GigAML 專門為 enterprise 開發語音客服機器人，他們用這個詞來形容自己如何能擊敗規模大上 20 倍的現有業者，成功爭取到 DoorDash 這個客戶。

When we got doordash as a customer, we were approximately like four to five engineers going against players who had like 100x engineers.

當我們爭取到 DoorDash 這個客戶時，我們大概只有四到五位工程師，對手卻有上百位工程師。

So we kind of like coined the term like hey, we are a 20x company because we are able to beat these much bigger players who are like 20x hours.

所以我們就想了個詞，說「我們是 20 倍公司」，因為我們能夠打敗這些規模大我們 20 倍的競爭對手。

By having a better product and better numbers.

透過擁有更好的產品和更好的數據。

Giga was able to close DoorDash and several other Fortune 500 companies as customers because of a powerful internal agent they call Atlas.

GigAML 能夠成功爭取到 DoorDash 和其他幾家 Fortune 500 大企業客戶，是因為他們有個強大的內部代理程式叫做 Atlas。

So Atlas can basically do anything within the product which you want to do.

所以 Atlas basically 可以在產品內做任何你想做的事情。

So it can use browsers, it can edit the policies, it can write code, it can do anything within the product.

它可以操作瀏覽器，可以編輯政策，可以寫程式碼，可以在產品內做任何事。

Atlas dramatically expands the range of work what each engineer can take on.

Atlas 大大地擴展了每位工程師可以處理的工作範圍。

So let's say before Atlas, every engineer can probably work on four to five problems at once because they are bottlenecked by all the boilerplate stuff they have to do for the customers.

所以假設在 Atlas 出現之前，每位工程師大概一次只能處理四到五個問題，因為他們會被所有必須為客戶處理的制式化工作卡住。

Customers have integrations, they would have to probably work on that.

客戶有整合需求，他們就得處理這部分。

Now, with AIFD taking care of all the boilerplate stuff, each engineer's scope is basically doubled or tripled because they don't need to work on the boilerplate code.

現在，因為 AIFD 處理了所有制式化的工作，每位工程師的處理範圍基本上都加倍或三倍了，因為他們不需要處理那些制式化的程式碼。

But Atlas doesn't just accelerate Giga's engineers.

但 Atlas 不只加速了 Giga 的工程師。

It also acts as as a full time AI employee that works in tandem with a human FTE to service dozens of accounts.

它也像一位全職的 AI 員工，與一位真人 FTE 合作，服務數十個客戶帳戶。

Right now we have only a single human FTE within the company.

As hard as it's to believe, because we have companies like DoorDash using us, we are in pilots with multiple Fortune 500, 10 plus Fortune 500, where each of these companies probably have volumes over like 500,000 or a million calls a day.

雖然很難相信，但我們有像 DoorDash 這樣的公司在使用我們，我們也正在跟多家 Fortune 500 公司進行試點，超過十家 Fortune 500，這些公司每天的通話量可能都超過五十萬或一百萬通。

It's only been possible because like we have at least and this person can primarily focus on just the customer relationships, the ask by the customers, taking customer requests and turning them into feature requests and everything.

這之所以可行，是因為我們至少有這位員工，他可以專注於客戶關係、客戶的要求，接收客戶的請求並將其轉化為 feature requests 等等。

Another is to build an AI integrated source of truth that gives employees instant context across your entire system.

另一種方法是建立一個 AI 整合的資訊來源，讓員工能夠即時掌握你整個系統的脈絡。

Legion Health, which is building an AI native psychiatry network, is one example of how to do this.

Legion Health，這家公司正在建立一個 AI 原生的精神科網絡，就是這樣做的其中一個例子。

Legion built a custom internal interface for their care operations team that lets them pull patient history, scheduling availability, insurance codes, and

Legion 建立了一個客製化的內部介面給他們的照護營運團隊，讓他們可以查詢病患病史、排班可用性、保險代碼等等。

What we're showing you right now is an interface that a vast majority of our care operations team uses in their day to day work for anything that actually has not been yet automated.

我們現在展示的這個介面，是我們大部分照護營運團隊日常工作中，用來處理所有尚未自動化的事務。

And this includes everything from as Arthur's kind of showing on his screen, digging into a particular patient or many patients backgrounds, trying to understand where they're at in their journey, if they need a new appointment to be rescheduled, if they're having a prescription issue, if they've sent us a message that in traditional healthcare might have otherwise gotten lost in the sea of different communications that go back and forth between so many different people.

這包含了 Arthur 在螢幕上展示的，深入瞭解特定病患或多位病患的背景，試圖瞭解他們在療程中的進度，是否需要重新安排預約，是否有處方藥的問題，或者他們有沒有傳送訊息給我們，在傳統醫療體系中，這些訊息很可能就淹沒在眾多人來人往的溝通訊息海裡了。

All of that is at a fingertips reach for every single member of our care Ops.

我們所有的照護營運團隊成員都能輕鬆取得這些資訊。

This single source of truth interface has let Legion keep its ops headcount flat, even as it's dramatically scaled revenue.

這個單一的資訊來源介面，讓 Legion 在營收大幅成長的同時，營運人力卻能保持持平。

So we've grown forex in the past year, but we haven't hired a single net new person.

所以我們在過去一年中成長了四倍，但沒有增加任何一個新進員工。

We've been able to forex the number of patients.

我們能夠處理四倍數量的病患。

We're seeing thousands of patients a month.

We have dozens of providers, but we have one clinical lead, we have one patient support person, and

我們有數十位醫護人員，但只有一位臨床領導，一位病患支援人員，而且

我們只有一位負責帳單的人員。

And in a typical healthcare company, those are all departments.

在一般醫療照護公司裡，這些都是獨立的部門。

You know, those are call centers, those are groups of people sitting around desks doing a ton of things manually.

你知道，這些都是客服中心、一群人坐在辦公桌前手動處理大量事務。

A third approach is actually build custom agents for each employee depending on their workflow and preferences.

第三種方法是為每位員工根據他們的工作流程和偏好，打造專屬的客製化 agent。

Faceshift, which is building agents to automate accounts receivable, took this approach.

Faceshift，這家公司正在打造 agent 來自動化應收帳款，就採取了這種方法。

So Face Shift right Now is a 12 person team and we're going up against companies that have been around since 2006 that have hundreds of employees.

所以 Face Shift 目前是個 12 人的團隊，而我們要對抗的是自 2006 年就成立、擁有數百名員工的公司。

The key to US as a 12 person team moving so fast is we bring AI into every process that is manual and try to automate as much as possible with AI agents.

我們這個 12 人團隊能快速推進的關鍵，是將 AI 導入每個手動處理的流程，並盡可能透過 AI agents 自動化。

One way PhaseShift does this is by literally asking its employees to document the manual tasks they do and then building custom agents for them.

PhaseShift 做到這一點的方式之一，就是請員工記錄他們做的手動任務，然後為他們打造客製化的 agent。

So what we do is essentially say, what do you spend your time doing throughout the day?

所以我們做的，基本上就是問，你一天到晚都在做些什麼？

And we make them document that and then we build quick AI agents.

然後我們讓他們記錄下來，接著打造快速的 AI agents。

And this culture of relentless automation has let phase shift delay hiring for entire functions.

而且這種不斷自動化的文化，讓 Phase Shift 能夠延後聘請整個部門的人員。

We've actually avoided hiring a design person at the company so far.

到目前為止，我們其實都還沒在公司聘請設計師。

To date, we're about a 12 person company by just leveraging magic patterns and our engineering team uses that to build all front end designs.

到目前為止，我們公司大概只有 12 個人，全靠運用神奇的模式，還有我們的工程團隊用它來打造所有前端設計。

These approaches aren't mutually exclusive.

You can build AI teammates, a unified source of truth and custom agents for each member of your team.

你可以打造 AI 隊友、統一的資訊來源，以及為你團隊的每位成員打造專屬的 agent。

The companies that do this are staying lean and setting record high growth rates.

這麼做的公司都能保持精實，並創下破紀錄的成長率。

This is the new way to build and the startups that figure it out first are going to win.

這就是新的建構方式，而最先掌握這點的新創公司將會贏。