And if you're thinking like a person who likes money, you're going to be asking yourself, who's powering the power?
如果你像個愛錢的人一樣思考，你會問自己，是誰在供應電力？
00:33
We're looking at utilities, generators, grid plays, or even buying or starting businesses that service energy infrastructure.
我們關注公用事業、發電機、電網布局，甚至是購買或創立服務能源基礎設施的企業。
00:40
Because look at this consumption level, actually wild.
因為看看這個消耗量，真的很驚人。
00:45
Companies like Hanley Energy, which builds data centers.
像 Hanley Energy 這樣建造資料中心的公司。
00:48
The play, if you're not going to build the data center, come to specialists like the contractor for emergency 24-7 data center services in a specific region.
I would copy the homework of these guys, Hanley Energy Electric.
我會抄 Hanley Energy Electric 這些人的作業。
01:02
So they're smaller contractors.
他們是規模較小的承包商。
01:04
They build expertise around data centers in particular.
他們專門建立關於資料中心的專業知識。
01:07
And if I was you, I might be looking to become a specialist, a contractor for them, knowledgeable about the area.
如果我是你，我可能會考慮成為一名專家，成為他們的承包商，並對該地區非常了解。
01:13
And I really think this next tier you're going to see, chips?
而我真的認為你接下來會看到的下一個層級，是晶片？
01:17
This is the arms race.
這是一場軍備競賽。
01:19
NVIDIA, AMD, and whoever controls chip manufacturing now controls global innovation.
NVIDIA、AMD，以及現在誰控制了晶片製造，誰就控制了全球創新。
01:23
But I'm not just interested in the stock.
但我感興趣的不只是股票。
01:25
I want to know who's building the fabs?
我想知道是誰在興建晶圓廠？
01:27
Who's maintaining the clean rooms?
是誰在維護無塵室？
01:29
Who's doing the HVAC and installation?
是誰在負責空調系統與安裝工程？
01:31
I think most people think, hey, this is just for the big guys.
我想大多數人都覺得，嘿，這只是大公司的遊戲。
01:34
I'm out.
我不玩了。
01:35
You know what?
你知道嗎？
01:35
I guess you're right.
我想你是對的。
01:37
But the contractors building these $10 billion facilities, they've been around for 20 years.
但興建這些百億美元設施的承包商，已經存在20年了。
01:41
They have certifications and engineering teams that speak in acronyms.
他們擁有認證，以及一群滿口專業術語的工程團隊。
01:45
We can just be the guys who build the things those guys need in the real world.
我們可以專門做這些大公司在現實世界中需要的那些事。
01:48
And I think those billion dollar fabs, guess what?
而我認為那些價值十億美元的晶圓廠，你猜怎麼著？
01:51
They also still get dirty.
它們還是會變髒。
01:52
They need HEPA filters that clog, and the big contractors.
它們需要更換堵塞的HEPA濾網，而那些大承包商。
01:56
They don't want to send their $200 an hour engineers to change filters.
他們不想派每小時200美元的工程師來換濾網。
02:00
That's where we come in.
這就是我們切入的點。
02:02
And we just charge them $150 an hour.
而我們只收他們每小時150美元。
02:04
These are the specialized service companies nobody's talking about.
這些就是沒人討論的專業服務公司。
02:07
Talk about like Primera, formally data clean.
比如說 Primera，前身是 Data Clean。
02:09
They started cleaning these data centers and clean rooms.
他們開始清理這些數據中心和無塵室。
02:12
Now they're the go-to across the entire industry.
現在他們成了整個產業的首選。
02:15
So like do one thing obsessively well, keep mission critical environments, particle free.
所以，要專注做好一件事，就是讓關鍵任務環境保持無塵。
02:20
You don't have to know a ton.
你不需要懂很多。
02:22
You just have to show up with a shop back and say I clean good.
你只需要帶著工具箱出現，然後說「我很會清」。
02:25
Then we've got tier two.
接著是第二層級。
02:26
Tier two is the data centers.
第二層級就是數據中心。
02:28
Four hundred and fifty five billion dollars went into data centers last year.
去年有四千五百五十億美元投入數據中心。
02:31
That's up 51%.
成長了 51%。
02:32
That's like so much money I can't even quite understand it.
這金額大到我難以理解。
02:35
Microsoft, Meta, Amazon, all the boys spending billions here.
Microsoft、Meta、Amazon，這些巨頭都在這裡砸下數十億美元。
02:38
What about the plumbing, roofing, wiring, insulation, the local businesses that these AI empires can't function without?
那水電工程、屋頂、線路、絕緣材料呢？這些 AI 帝國不可或缺的在地企業怎麼辦？
02:45
This is AI translated for blue collar domination.
這就是 AI 帶來的藍領階級商機。
02:47
Then look at how much money these companies are making today.
再看看這些公司現在賺了多少錢。
02:50
I think this is the kind of unsexy business that makes people rich while everyone else chases AI unicorns.
我認為這就是那種不性感的生意，當大家都在追逐 AI 獨角獸時，這種生意能讓人致富。
02:56
So JEM Tech Group, they clean data centers thrilling, right?
所以，JEM Tech Group，他們在清理數據中心，很刺激吧？
02:59
No, they're not just cleaning.
不，他們不只是在清理。
03:00
They're actually looking at fancy detection systems that are missed, fire hazards, disconnected cables.
他們其實是在檢視那些被忽略的高階偵測系統、火災隱患和斷開的線纜。
03:06
The play here is really simple.
這裡的策略很簡單。
03:09
You show up to clean, you show up to inspect, you show up to fix.
你出現是為了清潔，你出現是為了檢查，你出現是為了維修。
03:12
Suddenly you're not the janitor.
突然間你不再是清潔工。
03:14
You're the risk management consultant.
你變成了風險管理顧問。
03:15
And guess who gets paid a lot more?
猜猜看誰的收入高很多？
03:17
The risk management consultant.
風險管理顧問。
03:18
I think people in this space are going to make an ungodly amount of money.
我認為這個領域的人將會賺到天文數字般的財富。
03:21
This is the picks and shovels of the AI industry, and nobody is talking about it.
這是 AI 產業的鏟子和鋤頭（關鍵工具），卻沒有人在討論它。
03:25
Tier three of AI, we've got foundation models.
AI 的第三層級，我們有基礎模型。
03:27
This is where the big dogs play.
這是大玩家博弈的地方。
03:29
Open AI, X, Google, anthropic.
Open AI、X（原推特）、Google、Anthropic。
03:31
These are like the oil rigs, big money, long timelines, uncertain returns.
這些就像是石油鑽井平台，資金龐大、時程長、回報不確定。
03:35
You and I aren't going to invest there.
你和我不會投資在那裡。
03:36
Most people watching, we can't compete, but you don't need to build the oil rig.
大多數觀眾，我們無法競爭，但你不需要建造石油鑽井平台。
03:40
You just need to sell the fuel, tools, or whatever.
你只需要販售燃料、工具，或任何相關的東西。
03:43
Talkos to the guys on it.
向那些身處其中的人致敬。
03:44
I agree with what another investor Elizabeth Yen said.
我同意另一位投資者 Elizabeth Yen 所說的。
03:47
The typical early AI startup, they're burning AKA spending $500,000 a month.
典型的早期 AI 新創公司，他們每個月燒掉（也就是花掉）50 萬美元。
03:53
A lot of this is on GPUs.
這其中很大一部分花在 GPU 上。
03:54
If you don't know what that is, it's basically like a super fast calculator built to do thousands of tiny tasks at the same time.
如果你不知道那是什麼，它基本上就像是一個超級快速的計算器，設計用來同時執行數千個小任務。
04:00
You need that in order to power your AI.
你需要這個來驅動你的 AI。
04:02
At this pace, if you even had $10 million as an AI startup, it only buys you 20 months of runway, AKA ability to live and still survive as a company.
以這個速度，如果你身為一家 AI 新創公司只有 1000 萬美元，這只能買你 20 個月的跑道（Runway），也就是維持公司生存的能力。
04:12
But during that period, that's no profit.
但在那段期間，這並沒有獲利。
04:15
Because the problem is, you have too many users, and each one costs you money.
因為問題在於，你有太多用戶，而每個用戶都讓你花錢。
04:20
So OpenAI hit a $300 billion valuation, with revenue growing from $3.7 billion to $12.7 billion.
所以 OpenAI 的估值達到 3000 億美元，營收從 37 億美元增長到 127 億美元。
04:27
But the caveat here is that the training costs for these companies, they grow exponentially.
但這裡的警告是，這些公司的訓練成本呈指數級增長。
04:32
And so future models are going to cost them billions of dollars to train.
因此，未來的模型將花費他們數十億美元來訓練。
04:35
Think of this like the plumbing layer of AI, orchestration tools, APIs, deployments, frameworks.
把這想像成 AI 的管線層、編排工具、API、部署和框架。
04:40
It's like the AWS, the cloud of AI.
這就像 AI 的 AWS，也就是雲端服務。
04:43
It's not sexy, but neither is Stripe, or Datadog, or MongoDB.
這聽起來不性感，但 Stripe、Datadog 或 MongoDB 也一樣。
04:47
And yet, look at how much money those companies are worth.
然而，看看這些公司的市值有多高。
04:49
Multi-billion dollar businesses, as they make everything else work.
數十億美元的企業，因為他們讓其他一切得以運作。
04:52
I think some people are worried that bad things will happen here, that the big tech and AI companies are all well sleeping with each other.
我認為有些人擔心會發生不好的事，擔心大型科技和 AI 公司之間關係過於緊密。
04:59
Take a look at this chart from Bloomberg.
看看這張來自彭博社的圖表。
05:00
I think it means they actually are all sleeping together.
我認為這意味著他們確實都「睡在一起」。
05:04
You know, these companies are servicing one another.
你知道，這些公司互相服務。
05:06
Navidia and AMD are investing in companies they sell to.
Nvidia 和 AMD 投資於他們銷售的對象。
05:10
Anthropic is using Amazon's web services, and Amazon is an investor, an anthropic, and Microsoft is investing in OpenAI and counts OpenAI as a customer.
This is actually called circular financing, where dollars trade between firms, clouding actual demand.
這實際上被稱為循環融資，金錢在公司之間流動，掩蓋了實際需求。
05:26
So are they making all that revenue?
所以，他們真的賺了那麼多營收嗎？
05:28
Or are they just paying each other?
還是他們只是在互相付錢？
05:29
Then we've got tier five, the AI native applications.
接著我們來到第五層，也就是 AI 原生應用程式。
05:32
This is like the shiny stuff.
這就像那些閃亮的東西。
05:34
Most people playing here are building without a business model.
在這裡玩的大多數人，都是在沒有商業模式的情況下進行開發。
05:36
But if you focus on apps that replaced cost centers and drive real productivity, you got a shot.
但如果你專注於那些取代成本中心並推動實際生產力的應用，你就有機會。
05:41
That's where we're hunting.
這就是我們尋找目標的地方。
05:42
Now the question becomes, how do you not go alone here?
現在的問題變成了，你如何不獨自完成這件事？
05:45
Tools like Replet and Cursor will let you play startup founder for a weekend.
像 Replet 和 Cursor 這樣的工具，能讓你利用一個週末扮演創辦人。
05:49
You'll build something that looks legit, feel like a genius, maybe even get a few users.
你會打造出看起來很專業的東西，讓你感覺自己是個天才，甚至可能獲得一些用戶。
05:52
Cool story.
聽起來很酷。
05:53
But when it's time to actually scale, when the demo breaks at 2 a.m. and customers are screaming, when you need to connect payment processors, databases, APIs, and all the backend plumbing that makes real businesses work, you're how do the kids say, cooked.
So if you're going to play in this space on this tier, here's the kicker nobody talks about.
所以，如果你打算在這個領域、這個層級上競爭，這裡有個沒人談論的關鍵點。
06:09
If you actually win in this space, big tech becomes your biggest threat.
如果你真的在這個領域獲勝，大型科技公司將成為你最大的威脅。
06:12
Not because they'll copy you, they might, because they'll steal your team.
不是因為他們會抄襲你（雖然他們可能會），而是因為他們會挖走你的團隊。
06:16
If you build the millions, tens of millions, maybe even unicorn status and suddenly meta slides into your senior engineer's DMs with a $2 million package, an unlimited liqueur, who can say none of that, you know?
如果你建立了百萬、甚至千萬級別的事業，甚至達到獨角獸地位，突然 Meta 滑進你資深工程師的私訊，開出兩百萬美元的薪資包和無限額的福利，誰能拒絕得了，你說是吧？
06:28
This is called key person risk on steroids.
這就叫作被放大版的關鍵人物風險。
06:30
So if you want a real example, Google, meta, and windsurf.
所以如果你想要一個真實案例，看看 Google、Meta 和 Windsurf。
06:33
It's like this nerd soap opera from Silicon Valley that'll make you want to lock your engineers in the basement with golden handcuffs and NDA's written in blood.
這就像一齣來自矽谷的書呆子肥皂劇，讓你想用黃金手銬和血書寫的保密協議，把你的工程師鎖在地下室裡。
06:42
Don't do that though, that's not good.
但別這麼做，那是不好的。
06:43
So the play here is, if you're serious about tier five, you need a technical co-founder with equity, not just salary.
所以這裡的策略是，如果你對第五層級是認真的，你需要一位持有股權的技術共同創辦人，而不僅僅是領薪水。
06:49
You got to have something called vesting schedules that actually means something.
你需要一個真正有意義的所謂「歸屬時間表」。
06:52
You need retention bonuses that kick in before the big guys come calling, and a culture so strong, your team turns down Zuckerberg and everything he can offer.
Or you accept that you're building a nice little one to $5 million lifestyle business, which I think is awesome.
或者你接受自己正在建立一個價值一百萬到五百萬美元的美好小型生活方式企業，我認為這很棒。
07:05
Either way, know what game you're playing because in AI apps, your engineers aren't just employees, they're the entire moat, and Silicon Valley knows it.
Now the scary part in some ways, is as the financial times put it, America is now one big bet on AI.
現在，從某些方面來看，可怕的是，正如《金融時報》所言，美國現在是一場對人工智慧的巨大賭注。
07:18
AI better deliver for the U.S.
人工智慧最好能為美國帶來回報。
07:20
or its economy and markets will lose the one leg they are standing on now.
否則，其經濟和市場將失去目前所依賴的唯一支柱。
07:24
Look at this chart, it's kind of horrifying.
看看這張圖表，有點嚇人。
07:26
So the next question is, is this a bubble?
所以下一個問題是，這是一個泡沫嗎？
07:28
And the answer is like, probably.
答案是，很有可能。
07:30
400 billion being spent on AI infrastructure.
四千億美元正被投入到人工智慧基礎設施中。
07:33
Some AI companies raised $2 billion.
有些人工智慧公司籌集了二十億美元。
07:35
The S&P almost 40% of its gains this year are tied to 10 AI heavy stocks.
標普指數今年近40%的漲幅與10檔人工智慧權值股掛鉤。
07:41
So if it looks like a duck, if it talks like a duck, and it quacks like a duck, it's probably a bubble.
所以如果它看起來像鴨子，叫起來像鴨子，那它很可能就是個泡沫。
07:45
But here's what makes this one different.
但這次有個不同之處。
07:47
Unlike the tulip craze or even parts of the dot-com bust, AI is already in the economy.
不同於鬱金香狂熱，甚至不同於網路泡沫破滅的部分時期，人工智慧已經融入經濟之中。
07:52
Like this is kind of crazy.
這有點瘋狂。
07:53
90% of GDP growth, which is basically a fancy way of saying like, all the money the economy makes, AI related.
90%的GDP成長，基本上可以說是經濟體創造的所有錢財，都與人工智慧相關。
07:58
The biggest companies are trading at kind of reasonable multiples compared to the big bubble back in 99.
與1999年的大泡沫相比，最大型公司的交易倍數顯得相對合理。
08:05
So what do you do?
那你該怎麼做？
08:06
I'm not telling you to go buy tech stocks.
我不是在叫你去買科技股。
08:08
In fact, I don't think you should.
事實上，我認為你不該買。
08:09
You don't short it.
不要放空它。
08:10
You don't blindly ape in either.
也不要盲目地跟風進場。
08:12
You build a moat.
你要建立一條護城河。
08:13
Because you don't actually need to code.
因為你其實不需要會寫程式。
08:15
You don't need B.C.
你也不需要大學學位。
08:16
You just need cash flow, which brings us to the fun part.
你只需要現金流，這就帶我們來到了有趣的部分。
08:18
One of the fastest way to build cash flow in the digital economy is launching a digital storefront.
在數位經濟中建立現金流最快的方法之一，就是開設一個數位店面。
08:23
And I've been experimenting with a new tool called Build Your Store.
而我一直在試用一個名為「Build Your Store」的新工具。
08:26
And it's basically like having an AI store builder.
它基本上就像擁有一個 AI 商店建構器。
08:28
Here's what it does.
以下是它的功能。
08:29
You tell BYS what niche you want to target and let's say pet grooming or home office upgrades.
你告訴 BYS 你想鎖定的利基市場，比如說寵物美容或家庭辦公室升級。
08:34
The AI instantly builds your entire Shopify store products, design everything.
AI 會立即為你建立整個 Shopify 商店，包含產品和所有設計。
08:38
You get ready to sell business in minutes with zero tech skills and zero inventory to start.
幾分鐘內，你就能得到一個準備好銷售的事業，不需要任何技術技能，也不需要庫存就能開始。
08:43
And as a part of this setup, Build Your Store also walks you through a quick auto DS registration, which basically what it does is automatically add 10 products to your store and allow you to automate shipping and order fulfillment.
作為設定的一部分，Build Your Store 也會引導你完成快速的自動代發（Auto DS）註冊，基本上它會自動為你的商店添加 10 項產品，並讓你自動化運輸和訂單履行。
08:54
And this auto DS 30-day trial is just a buck, $1.
而這個自動代發的 30 天試用期只需要一美元。
08:58
And the best part, pro level design you normally pay $200 for already included.
最棒的是，通常要付 200 美元的專業等級設計已經包含在內了。
09:02
So if you're looking for an online side hustle with lower risk and potential for lots of upside, this could be one of the funnest plays I've seen.
所以，如果你正在尋找一個風險較低且潛力龐大的線上副業，這可能是我看過最有趣的玩法之一。
09:09
So thanks to Build Your Store for sponsoring this video.
因此，感謝 Build Your Store 對本影片的贊助。
09:11
Head to the link in the description to your Shopify store with Build Your Store AI today.
點擊說明欄位中的連結，立即使用 Build Your Store AI 建立你的 Shopify 商店。
09:16
And now in section three.
現在進入第三部分。
09:18
How to prop it either way.
如何在任何情況下佈局。
09:19
So whether this thing pops or rockets, here's where the money's made.
所以無論這個東西是暴漲還是暴跌，真正的財富就在這裡誕生。
09:23
One, you could leverage AI for margin expansion.
第一，你可以利用 AI 來擴大利潤率。
09:26
This is a fancy way of saying make more money in your business.
這是一種比較好聽的說法，意思就是讓你的公司賺更多錢。
09:28
AI is a tool, not a business.
AI 是一種工具，而不是一門生意。
09:30
So in our business right now, and in the 14,000 people we teach how to build more profitable businesses at contrarian growth boardroom, we use agents to automate customer service and admin, pre-qualify leads, build SOPs faster, cuts, costs, like for example, this furniture company, Temple and Webster.
就以我們現在的業務，以及我們在「逆向增長董事會」教導的 14,000 名學員如何建立更有利可圖的企業為例，我們使用 AI 代理來自動化客戶服務和行政工作、預篩選潛在客戶、更快地建立標準作業程序（SOP）、降低成本，例如像家具公司 Temple and Webster。
09:46
They didn't build a new GPT app.
他們並沒有開發新的 GPT 應用程式。
09:48
They simply layered AI across support, product descriptions, shipping calculations, customer service.
XAI, Google, all racing to make chat box that each sound a little smarter than the last.
XAI、Google，都在競相打造一個比上一個稍微聰明一點的聊天機器人。
10:09
When every product looks and acts the same, what happens? Price is strong.
當每個產品看起來和運作都一樣時，會發生什麼事？價格競爭會很激烈。
10:12
Amazing for users, tough for companies.
對用戶來說很棒，但對公司來說很艱難。
10:14
So with open source and Chinese models giving kind of this commoditized market, it's not good.
所以隨著開源模型和中國模型讓市場趨於商品化，這對它們並不有利。
10:20
And what I think Goldman says perfectly is competition is often underestimated and the returns on capital invested overstated.
我認為高盛說得非常到位：競爭往往被低估，而資本投資的回報則被高估。
10:27
Fancy way of saying real winners in the coming years won't be model makers, but scrappy So I want to buy businesses that wire the data servers, cool down server farms, clean semiconductor labs, install solar.
It plugged into things that were already happening, like document review, contract drafting, and it cut the time to do that work in half.
它整合了既有的工作流程，像是文件審查、合約起草，並將執行這些工作的時間砍半。
11:06
So an AI strategy like this will make you twice as much money.
所以像這樣的 AI 策略能讓你賺加倍的錢。
11:09
That's your sales pitch.
這就是你的銷售說詞。
11:11
Four, stop waiting, start owning.
第四，停止等待，開始擁有。
11:13
By the time regular people feel safe, the money's already been made, you guys.
等到一般大眾覺得安全時，錢早就被賺走了，各位。
11:16
You got to use AI to speed up ownership now, because it doesn't eliminate opportunity.
你必須現在就用 AI 加速擁有權，因為它並沒有消除機會。
11:21
It actually multiplies it if you move fast enough.
只要你夠快，它其實會倍增機會。
11:23
I think one of the biggest opportunities in this space is using AI before everybody else does.
我認為這個領域最大的機會之一，就是在其他人之前率先使用 AI。
11:29
People take their time to figure shit out.
人們會花時間去搞懂那些鳥事。
11:31
An underrated bottleneck in AI isn't computing power, but human creativity.
AI 領域一個被低估的瓶頸不是運算能力，而是人類的創造力。
11:35
Companies can sell you endless new models, but if you don't really know how to wield them, then what good are they to you?
公司可以賣給你無窮無盡的新模型，但如果你真的不知道如何駕馭它們，那它們對你有什麼用？
11:39
The real opportunity, learn how to apply these new tools in ways that actually make you more money.
真正的機會在於，學會如何實際運用這些新工具來幫你賺更多錢。
11:44
Harvard and Stanford found out that some workers are spending more time correcting AI output than doing actual work.
哈佛和史丹佛發現，有些員工花在修正 AI 輸出的時間，比做實際工作還多。
11:50
But Kinsey found two-thirds of respondents say their organizations have not even started using AI.
但肯錫發現，三分之二的受訪者表示他們的組織甚至還沒開始使用 AI。
11:55
So if you can get ahead of these trends, you're just going to make more money.
所以如果你能搶先掌握這些趨勢，你就會賺更多錢。
11:58
The play?
這個策略是？
11:59
The biggest benefits will likely come in the form of AI agents.
最大的好處很可能會以 AI 代理的形式出現。
12:02
This is the last one.
這是最後一個了。
12:03
These are the tools to help you.
這些都是能協助你的工具。
12:05
Many of them are okay today, but digital specialists will change everything.
目前其中許多工具都還不錯，但數位專家將會改變一切。
12:10
Agentic AI understands goals and can actually plan for you.
具代理性的 AI 能理解目標，並實際為你制定計畫。
12:13
They can make decisions, take initiative without constant human direction.
它們能做決策，在沒有持續的人為指示下主動採取行動。
12:17
This is all from VISTA Equity, where one of the smartest software companies in the world.
這些都來自 VISTA Equity，它是世界上最聰明的軟體公司之一。
12:21
So for you, that will eventually mean having a little team of workers for you in AI.
所以對你來說，這最終意味著擁有一支 AI 工作小團隊。
12:28
And I think one of the reasons that's so powerful is the key to making money in the AI age is not letting AI think for you, but making it think with you.
我認為這之所以強大的原因之一是，在 AI 時代賺錢的關鍵，不是讓 AI 替你思考，而是讓它與你一起思考。
12:35
Most people let AI copy what's already out there.
大多數人讓 AI 複製現有的東西。
12:38
You be original.
你要保持原創。
12:39
You move fast.
你要快速行動。
12:40
AI can only generate, but you can direct.
AI 只能生成，但你能指導方向。
12:44
So I don't actually think you should try to time the market.
所以我其實認為你不應該試圖預測市場時機。
12:46
It's time in the market, but it's more important over the long term.
重點是投入市場的時間，長期來看這更重要。
12:49
Here's the thing.
情況是這樣的。
12:50
Nobody, not us, not Sam Altman, Nadielon Musk knows exactly what's going to happen with AI.
沒有人，不是我們，不是 Sam Altman，也不是 Nadine Dorries 或 Elon Musk，確切知道 AI 未來會發生什麼事。
12:54
So you don't have to predict it.
所以你不需要去預測它。
12:56
But what if you built the picks and shovels?
但如果你打造了「鎬與鏟」（關鍵工具）呢？
12:58
The way you win is by owning the things nobody else thinks is sexy.
你致勝的關鍵在於持有那些沒人覺得性感的東西。
13:02
So bubble or boom, that's how you stay rich.
所以無論是泡沫還是繁榮，這就是你保持富有的方法。
13:05
You learn how to use the tool instead of being used by it.
你要學會如何使用這個工具，而不是被它利用。
13:09
And if you want to learn exactly how to do this, I'm teaching a free workshop on how to use AI to buy cash flowing businesses.
如果你想深入了解具體做法，我將開設一場免費工作坊，教你如何運用 AI 來收購現金流穩健的事業。
13:17
So let's do this together.
讓我們一起行動吧。
13:18
Let's be part of the picks and shovels that last way beyond the gold rush.
讓我們成為那種在淘金熱過後依然長久存在的鏟子與鎬頭。
13:23
I'll see you there.
我們到時見。
The AI Bubble is Bursting… Here’s How to Profit From It
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📝 影片摘要
本單元深入剖析AI產業的泡沫現象，並提供非技術背景者也能參與的獲利策略。影片將AI經濟分為六大層級，從能源、基礎建設到應用程式，強調真正的商機不在於追逐獨角獸，而是投入「鏟子與鋤頭」(Picks and Shovels) 式的關鍵服務，如數據中心維護、能源供應與專業勞動。此外，也分享如何利用AI工具擴大既有業務利潤，以及如何透過建立數位店面來創造現金流，以應對未來的市場波動。
1Accordingtothevideo, whatistheestimatedtimeframethatmostAIstartupswillsurvive?根據影片，大多數 AI 新創公司預計能存活多久？Accordingtothevideo, whatistheestimatedtimeframethatmostAIstartupswillsurvive?
