家教、學英文/中文、翻譯、文化、旅行... Learning English/Chinese, Translation, Culture, Traveling....

從以前看邦喬飛這支MV，就看到裡面那個新娘一直跑跑跑，不知要跑去兜位~ 我最近也一直跑，很驚訝自己竟然可以從家裡跑到公車站(用普通速度從我家走到公車站約21-24分鐘，回程約28-33分鐘)，最近我竟然可以用11-12分鐘從我家跑到公車站(不過中間有多次停頓休息一下再繼續跑啦)
不是多厲害的數字，我跑的原因也不是因為我要特意訓練自己或我喜歡跑步，就只是因為我太慢出門、怕搭不到公車，但意外發現我現在變得還蠻喜歡(適當的)跑步的~
我過往很討厭跑步，本來天生就不擅長跑步了，加上我國三讀烏眉國中時，學校規定全校學生每天跑五圈操場，實在很討厭跑步時那種上氣不接下氣的感覺~
不過，之前看某個醫學什麼的說“跑步的那種感覺會活化大腦細胞”，近來我的確感覺跑步有種能讓我感覺到整個頭腦的細胞都呼吸進新鮮空氣、真的感覺得到自己腦袋裡的細胞的感覺~
而且，這次跑步，我還很高興自己體能恢復不少，過往長期在家工作導致體能變差，本來我還很擔心自己的體能到底何時能恢復成以前在台師大跟競技體操隊一起練習時那樣~
台師大競技體操隊也規定每次練習時要跑好幾圈操場，我當時也討厭跑步，就"時不時"偷偷打混摸魚用走的交差了事~沒事兒啦~ 因為我不是競技體操隊的選手，大小教練們的心全都在選手和正式隊員身上💔沒人在乎我💔沒人有心思來關注我這個連翻筋斗都做不標準的門外漢😭😆
我在台北教一對一英文時，也很少跑著去教課的地點~ 大多數時候我會提早到，多數的家長和學生也是體諒我是特地去上課，所以叫我不要急，因此，我基本上都是自然速度的走路~
沒想到去餐飲業打工幾個月就自然而然地恢復了不少、還有點喜歡跑步，我真的很驚訝自己竟然可以連續跑那麼久，所以，我現在去很多地方都“喜歡”用跑的:
「覺得自己好像能體會<阿甘正傳>裡的阿甘喜歡去哪裡都用跑的感覺了。」
#今天看這支MV終於完整看到結束_原來新娘是跑去他的新郎身邊要結婚~
#一直以來都覺得這支MV拍得很漂亮

(影片連結設定在圖片，點擊圖片即可觀看影片)分享酒精成癮的郭艾珊有著世人眼中非常成功和亮眼的表現: 台灣大學經濟系畢業、中國星巴克品類資深總監、中國麥當勞資深總監、交通大學經營管理研究所碩士畢業、寶僑大中華區護膚品牌經理、台灣萊雅行銷經理、台灣嬌生嬰兒及身體護膚行銷經理、台北101企業傳播處長、英國藥商、港商...... 另外，他還跨領域就讀台北大學文學藝術跨域研究所畢業，也就讀淡江大學教育與心理諮商研究所。
郭艾珊在這訪談中，表示後來發現自己原來是所謂的“高功能酒精成癮者”--- 也就是工作表現很好、也會去健身，但這一切都是為了讓自己能繼續喝酒。

從我寫文章以來，儘管我是一直保持著幫助別人的善心，也將定義寫得明確，但有時候就是會出現斷章取義，不看清楚我寫的內容和意思，明明不了解很多事情、還充滿著許多顯而易見的錯誤觀念、卻很敢批評的人; 最重要的是: 網路上不友善的人發表那些言論的本質並"非"真的想討論事情。
事實上，這樣不友善的人一直都存在著，而且，也不是只有在網路上有，我在學術圈，在實際教那麼多社會人士學生/在學生/跟家長溝通的這至今21年多的過程中，在日常生活的各種人際關係中，也都多少有遇到過這樣不友善的人，連日常生活遇到我都不在意，又為何要回應網路上的不友善？
其實我若有回應網路上不友善的原因是“因為我想幫助這些不友善的人了解更多關於學習和教育的事情”，但其實也不清楚不友善的人的真實動機，你對他們友善，他們會承認自己錯嗎？不會，唯一可以確認的是：
“就算他們真有不懂，可以像很多其他網友一樣禮貌詢問、可以虛心學習，而非貶低或批評創作者。"
既然我創作文章和影片的目的“之一”是為了幫助人，那當網路上有不友善的人時，也就給他們時間學習和成長成一棵棵大樹🌳
【網路上不友善的人發表那些言論的本質並"非"真的想討論事情】
時不時看到很多公眾人物回應被網路上不友善的人批評的事情，我都會覺得這些公眾人物其實沒有必要回應，尤其對我喜歡的公眾人物，我更希望我喜歡的公眾人物根本不需要回應，因為: "網路上不友善的人發表那些留言的本質並非真的想討論事情。"
而且，當我身為觀眾時，我根本不會被這些不友善的留言影響，我也不在乎這些留言說了什麼，因為我求真的意願和能力，能讓我去找出真相、讓我知道我支持的人說的是對是錯。
就跟我希望我喜歡的公眾人物不要回應網路上不友善的言論一樣，我相信追蹤我的網友也不希望我回應不友善的言論，同時，也祝福不友善的網友變成友善的網友🙂祝福生活中不友善的人變成友善的人🙂

Mr. Beast是全世界Youtube粉絲數最多的國際大網紅，但他的Youtube頻道竟然也花了好幾年才達到1000位粉絲/訂閱者，讓我們來看看他是怎麼說的吧:

Two Americans and one Italian perform a popular Min閩-Nan南 song, <Sound of Rainfall落雨聲> ,which is also the background music on my last post. This song is in connection with filial piety, and sung in Min閩-Nan南 the language, not Chinese.
🌳Min閩-Nan南 is one of seven main dialects in China and being used as a main language by about 30.4% to 31.7% of Taiwanese population(note: 86% Taiwanese population possess some level of Min-Nan speaking and listening skills.)
It is admirable for these foreign friends to speak such exceptional Chinese and dialects
.
Of these four performers:
(1) the female American singer, Tristan Hilderbrand(Chinese name: 崔璀璨): https://www.youtube.com/@yakitorisutan )
(2) the male American singer, Matthew Eric Candler(Chinese name: 杜力Dooley): ‪ https://www.youtube.com/@dooleytw‬
(3) the male Italian singer, Giovanni Voneki (Chinese name: 吳子龍): ‪ https://www.youtube.com/@GiovanniVoneki‬
(4) the Taiwanese guitar player, 黃丞(Bruce Huang ‪ https://www.youtube.com/@huangchen0721‬ )
This song is originally performed by Jiang江-Hue蕙, a household name and a popular singer who sings in Min-Nan the language. Following is the lyric of this song, <Sound of Rainfall落雨聲>:
Note:
(1) Words in both first and second lines are all written in Chinese characters, but the first line is mimicking the sounds of Min-Nan the language, not how contemporary Chinese meaning would say.
(2) The exact contemporary Chinese meaning and how contemporary Chinese would say is in second lines.
(3) The first Chinese lines and the third lines of English translation are copied from the introduction of this Youtube link, https://www.youtube.com/watch?v=y1yEIgpq7GY&t=1s
(4) I just translate the second lines of contemporary Chinese from the first lines.
【Sound of Rainfall•落雨聲】
<主歌•Verse>
落雨聲 若親像一條歌
落雨聲 如果像是一首歌
The sound of rainfall is like a song
誰知影 阮越頭毋敢聽
誰知道 我轉頭不敢聽
Little do they know I can't turn to listen
異鄉的我 一個人起畏寒
異鄉的我 一個人感覺寒冷
Far from home I suddenly feel the cold
寂寞的雨聲 捶阮心肝
寂寞的雨聲 重擊我的內心
The lonely sound of rain is knocking at my heart
人孤單 像斷翅的鳥隻
人孤單 就像斷了翅膀的鳥
Lonely people are like birds with broken wings
飛袂行 敢講是阮的命
飛不動 難道是我的命
Is it my fate to not be able to take off and fly?
故鄉的山 永遠攏徛佇遐
故鄉的山 永遠都站在那裡
The mountains of my hometown stand as they always have
阮的心情只有講予山來聽
我的心情只能講給山聽
I can only tell these feelings to the mountains
<銜接主歌與副歌•Pre-Chorus>
來到故鄉的海岸
來到故鄉的海岸
Here at the beaches of my hometown
景色猶原攏總無變化
景色還是完全沒有變化
The scenery stands unaltered
當初離開是為啥
當初離開是為了什麼?
Why did I leave to begin with?
你若問阮阮心肝就疼
你若問我，我的心就痛
It pains me to ask that question
<副歌•Chorus>
你若欲有孝序大毋免等好野
你如果想孝順，不用等到有錢
Don't wait until you have money to show your parents gratitude
世間有阿母惜的囡仔上好命
世界上有媽媽疼的孩子最好命
The luckiest children in the world are those with their mother's love
毋通等成功欲來接阿母住
別等成功想接媽媽來一起住時
Don't wait until you're successful to take your mother into your care
阿母啊 已經無佇遐
媽媽可能已經不在了
Your mother may not be there anymore
~謝謝閱讀•Thanks for Reading~

這演講是知名搞笑主持人Conan今年2026年在哈佛大學的畢業典禮上的演說，比起黃仁勳今年在卡內基美隆大學的畢業典禮演講 https://reurl.cc/YDLNDa ，Conan的這演講難許多，以下是這兩者的比較:
1️⃣Conan的這篇從頭到尾充滿笑話、文化點，要知道文化才會一起笑:
如果將黃仁勳今年在卡內基美隆大學畢業典禮的演講稿印出來，只要查好不懂的單詞的意思，讀起來的感覺其實只像台灣高中課本的文章; 但Conan的這篇因為參雜了大量的笑話、文化點，要將這些文化全弄懂、並能從頭到尾跟著笑，則沒那麼容易。
還記得當我的英文沒那麼好時，聽Conan這類型演講就會覺得自己很不懂，其實不了解這些笑點和文化沒關係，只要想學的話，只要盡心盡力了解、有學到東西即可，沒辦法了解全部笑點沒關係。
還有，這些文化、笑點不是只從英文口說聽力就可以學到的，很大部分也可以從閱讀中涉略，所以， “長期閱讀”也能增強我們理解此類的英語聽力和口說。
2️⃣Conan的這篇字詞也較難，有GRE程度的詞彙: tercentenary, quarrellous, denigrade, bemoan… 黃仁勳的那篇完全沒有GRE程度的字詞。
3️⃣Conan這篇就算不是GRE詞彙的難度，也有些生活字詞必須要查才懂: mimosas, Church of England Ziti, Lutheran Lambada, an infinitely packed clown car of multitudes.
4️⃣Conan這篇也有很多道地的生活用語: Don’t push it. I’ll see your ass in court. things go south, a death knell, tool(傀儡、傻瓜), shout-out, … 黃仁勳的那篇完全沒有。
5️⃣口說時的句型大致不會太難，黃仁勳的更加簡單，Conan這篇有出現英語母語者時不時會用到的 "if not more... 這個對中文母語者需要多理解的用法( “托福”出現和考過N次這種用法，不過這裡的比較簡單，托福出現的則大多有較多概念要理解)":
I made something I love just as much, if not more than my late night show.
6️⃣黃仁勳那篇完全沒有倒裝句，Conan這篇有一個倒裝句:
Not only am I not against these lawsuits, I’m here to announce that I’m joining them.
7️⃣每個人不會的字詞不盡相同，雖然我在這篇新學的字詞和用法在20個以下，但我會將自己覺得有任何聯想、或自己雖然能理解但想更確定的字詞/用法，都重查一次，也會特別練習想特別加強的地方;
還有，也會將我聯想到的東西一起查，因為只要會讓你聯想到、卻分不清的事情，就代表目前會混淆:
🎂以下是我在這演講中，查過是否有其他特殊意思，及特別額外練習的字詞、用法:
honorands, mimosas, stewardship, less than flattering, potion, druids, head start, bunch, tercentenary, patch, veritable, quarrellous, denigrate, tool, don’t push it, veneer, shout-out, provost, intel, on all your concerns, bemoan, complimentary, sicken, potty, chancellor, Ms. Pacman, leaderboard, lean cuisine, an erase cartridge, Smith Corona typewriter, be bound by, creeps, confiscate, an instrument of divine cruelty, Soldier’s field, sake, less than spectacular, three-way, krokodiloes, rendition, splish splash, to this day, government-issued cod, gum up, Calvinist Reggae, savory, Church of England Ziti, sexually charged, Lutheran Lambada, toss off, alma mater, bedridden, commencement, how much hard work it took for all of you to get to this point, diss, disdain, in any way, a death knell, scream, car, things go south, pivot, zigs and zags(zigzag), gummy, spontaneity, renounce, degrade, eject, roundly mocked, patrons, pathetic display, etiquette, kick out of, in style, add to that, siloed, antidote, an exercise in virtue, pretensions, a cash component, not despite it, an infinitely packed clown car of multitudes; boundless.
*一秒都不能想、要立馬區分 humanity, humility, humiliation.

就在這個月(2026年8月)，史丹佛大學主導之研究團隊宣布用AI首次設計出病毒，引起大眾關切，也因此，美國資深聯邦參議員Bernie Sanders在這影片中，呼籲三大科技巨頭的領導人(Altman, Amodei和祖克伯) 遵守這三家科技巨頭曾說過的承諾，也就是: “到一個重大的點後，即刻停止AI的發展，而這個點就是現在。”
1️⃣｜分析這篇演說的英文難度｜ 算簡單的，沒有什麼特別難的地方、沒有俚語、沒有需要特別去理解的文化層面的東西。
對“平常有好好學英文的台灣公立高中生來“自學”這影片”而言，只要查完不會的單詞、分析完句型架構，也把整支影片講稿看懂聽懂的話，依不同人不同程度，能理解約七成到九成。
2️⃣｜這篇可以特別注意的用法｜
(1) 此為口語用法: 引用別人的話時，也就是當有 “” 此符號出現時，前面的 “ 唸成 quote，後面的 ” 則唸成 end quote. 這就是為什麼你會在這篇聽到、看到許多這個用法。
(2) verbatim 一字不差、逐字逐句地。在 “I’m going to read it verbatim.”這句中出現，意思為: “我將一字不差、逐字逐句地唸出來”。
3️⃣｜以下為這支影片的字幕轉錄稿，是我從此YT頁面上複製下來並且修改過的---應該是除了標點符號外，幾乎都修改到了｜
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Thank you very much for joining me. I wanted to take a moment to share with you a letter uh that I've just sent to
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the three major leaders of the AI industry. And here's what the letter says. I'm going to read it verbatim.
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Dear Mr. Altman, Mr. Amodei, and Mr. Zuckerberg.
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Almost every day there is a new story about how your companies are losing control of the AI technology you are
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developing with potentially cataclysmic results. This week we learned frighteningly that AI has been used for
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the first time ever to create new viruses. As you know, this type of development in the wrong hands could
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lead to new bioweapons that result in the deaths of tens of millions of people. Last month, the world found out
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that Open AI lost control of an AI model. The result, the model hacked into another company's computers, a clear
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violation of federal law. After conducting internal reviews, Anthropic and Meta reported their models similarly
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escaped their control. One of the targeted companies called the AI hack quote an unprecedented event end quote that deserves an unprecedented response.
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Yosua Benjio, the most cited living scientists in the world, said these incidents quote should serve as a wakeup
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call end quote. I agree. So do the top scientists at the companies you lead the
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very people building this technology. As you know, these technology leaders recently called for the international
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community to create a safety mechanism, a pause button to avoid catastrophe.
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They warned there is quote a real risk that capability development rapidly accelerates beyond our ability to
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understand or control the resulting systems. End quote. And yet, at a moment when we have seen human loss of control
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and the creation of potentially dangerous viruses, your companies are still racing ahead, investing tens of
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billions of dollars into a technology that nobody can fully understand, predict, or control. That is absurd, irresponsible, and extremely dangerous.
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It is also a betrayal of your own stated commitments. In 2023, Anthropic said it
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would quote commit to pause the scaling and/or delay the deployment of new models whenever our scaling ability
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outstrips our ability to comply with safety procedures. End quote. In 2025, Meta said, quote, "If a frontier AI is
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assessed to have reached the critical risk threshold and cannot be mitigated, we will stop development end quote."
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That same year, Open AI said it would quote halt further development end quote
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until strong safeguards were in place if AI capabilities ever reached a critical threshold.
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That moment is here.
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AI capabilities have reached a critical threshold. There is a reason why the head of the CIA says that AI models are
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quote akin to digital nuclear weapons and almost like a doomsday device.
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Mr. Altman, Mr. Amodei, and Mr.
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Zuckerberg, in the interest of humanity, stand by your words. Pause AI development. It is not too late to avoid disaster.
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Stop building machines that humans cannot control.
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Let me be very clear. If you do not take appropriate action now, my colleagues and I in the US Senate will.
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That is the letter that I sent to these three leaders in the AI industry. And thank you very much for joining me. Take care.
~謝謝閱讀，共約1,160字~

這位目前649萬訂閱的Youtuber，英文很好、印度口音非常重，但還是非常厲害: 遣詞用字道地、文法句法也幾乎都正確(雖然有很少數地方有小錯，但瑕不掩瑜)，最重要的是他說的內容不錯，所以，他YT的訂閱數很高。
由於他的印度口音咬字很重，整支聽時需要很專注，專注久了就累，所以聽完後覺得鬆了一口氣，也覺得聽完很值得，因為他說的內容很棒，讓我思考和注意到一些科技發展和創業的事情，難怪他至今會有649萬訂閱者。
在此之前，我也聽過不少印度母語者說一長串的英文，說得最長最多的是我邊做家事邊聽的印度新聞台WION的英文新聞影片(YOUTUBE搜尋WION即可收看)，他們的口音都沒有這位Youtuber的口音重，所以，在這位之前，我並不覺得印度母語者說英文有多難懂~
直到這位印度裔的YOUTUBER，他的名字是Ganeshprasad Sridharan，真的是讓我見識到了，他說的很多地方如果沒有字幕的話，我會聽不懂--- 但這也不是怪他，就是自己的印度腔英文的經驗值不夠，如果把他所有影片通通都拿來聽懂的話，印度腔經驗值應該會上升不少~
想想看我們很多台灣人、應該說整個東亞很多人都是在說英文時，經常會自己綁手綁腳的; 也有部分人會批評別人的口音，但看看人家印度人都不會因為口音腔調很重而膽怯說英文~
更何況台灣人就算說英文有口音，大部分人的口音也都比印度人的輕得多，所以，儘管追求如同英語母語者的口音是好事、雖然說長期而言糾正口音成更趨近母語者也是一個學英文的動力，但卻不該因此而被綁手綁腳的:
平日就要常開口練英文，需要說英文的時候更要大膽說出口、不要被發音、口音、文法…等等的綁手綁腳喔~
｜以下為他這支影片的字幕轉錄稿，是我從YT頁面上複製下來的，我修改了一些轉錄稿上的錯誤，但目前沒有全部地方都修改到｜
0:02
How do you think about this bubble talk
0:03
that has been going on for the last few
0:05
months especially?
0:09
I mean I I think it's quite possible.
0:12
Ladies and gentlemen, on 25th of June
0:14
2026, Apple did something that it has
0:16
never done in its history. In the middle
0:19
of the year for no new product, it just
0:22
raised prices. MacBook Air is up by 18%,
0:25
iPad Pro is up by 20% and Apple TV is up
0:29
by 54%.
0:33
Apple said yesterday it is immediately
0:35
raising prices on the products.
0:36
The company says soaring memory chip
0:38
prices are driving up costs and the
0:40
[music] AI boom is a major factor behind
0:42
the surge.
0:42
The AI trade is leading to real
0:45
near-term inflation.
0:48
And when asked why, Apple said something
0:50
remarkable. They said, "We have never
0:53
seen a competent price increase this
0:55
much this quickly." And the reason your
0:57
laptop got more expensive is because of
1:00
a war being fought over tiny memory
1:02
chips thousands of kilometers away. And
1:04
look at this graph. In 2020, before Chad
1:07
GBD existed, the four biggest US tech
1:10
companies spent combined $90 billion on
1:13
capeex. In 2023, they spent $147
1:16
billion. In 2025 that number went up to
1:20
$410 billion and then in 2026 it is up
1:25
to $725 billion. So in 6 years the capex
1:30
has grown 8x and all of this is coming
1:33
just from Amazon, Meta, Google and
1:36
Microsoft. At the same time, the stock
1:38
market was going so crazy over the AI
1:40
wave that on 2nd of June 2026, Nvidia
1:43
was worth $5 trillion and analysts were
1:46
screaming to buy AI stocks.
1:48
The AI will be a pretty [music] good
1:50
thing to invest in.
1:51
It is going to be, I think, just a
1:53
booming year for AI and tech stocks,
1:55
[music] especially in the first half of
1:57
the year.
1:57
But just 3 days later, something started
2:00
cracking. Nvidia lost $320 billion in
2:04
market cap. By 24th June, Micron was
2:06
down by 13%, SanDisk was down by 10.59%,
2:11
Apple fell by 6.1% and Soft Bank tanked
2:14
12%. On top of that, OpenAI delayed its
2:17
IPO and slowly warnings are coming from
2:20
the smartest people on earth. We are
2:22
right now rising close to the same level
2:25
in 2010.
2:26
So what's the end? Is it a bubble that
2:28
bursts eventually?
2:29
I think it is. Yes. The problem with the
2:31
AI capex boom is not only is it immense
2:34
but a big chunk of it is funded with
2:36
debt and that pain doesn't stay
2:38
restricted. It spills over into the rest
2:42
of society. The second thing that
2:43
happens when people get very excited as
2:46
they are today about artificial
2:47
intelligence for example is every
2:50
experiment gets funded. This is a kind
2:53
of industrial bubble as opposed to
2:55
financial bubbles. Now looking at this
2:57
madness, I went back to understand all
2:59
the bubbles in history. I read the Wall
3:01
Street Journal, the Financial Times, the
3:03
CNBC transcripts and even the JP Morgan
3:05
gap analysis to understand why does Ray
3:08
Dalio call this a textbook example of a
3:10
bubble. And by the end of this video,
3:11
you will understand better than 99% of
3:13
Indian investors, whether this is the
3:15
greatest business bet in human history
3:16
or the greatest bubble ever inflated.
3:18
Why are Michael Bur, Ray Dalio, and Jeff
3:20
Bezos implying that this is a bubble?
3:23
And what happens when this bubble bursts?
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--------------------正文開始-------------------------------------------------
4:44
[music]
4:46
This is the story of one of the greatest
4:48
bets in human history. Before we go
4:50
anywhere, let me install a mental model
4:52
in your head. Because if you don't
4:53
understand what a data center actually
4:54
is, none of the numbers will make sense.
4:56
Imagine your phone. When you type a
4:58
question into Chad Gupty, your phone
5:00
doesn't just answer by [music] itself.
5:02
Your phone is just a screen with Wi-Fi.
5:04
The actual thinking happens somewhere
5:06
else in a warehouse. A giant windowless
5:09
industrial warehouse which is filled
5:11
with metal racks. And each rack is
5:13
installed with thousands of these
5:15
[music] chips. That warehouse is called
5:17
a data center. Each one of these
5:19
buildings can hold 100,000 Nvidia GPUs.
5:22
Each Nvidia GPU cost 30 to $40,000.
5:26
So one building holds 3 to4 billion
5:29
worth of chips. One building just holds
5:32
[music]
5:33
3 to4 billion worth of chips. This 3 to4
5:37
billion is [music] just for chips. On
5:40
top of that, you have to power them,
5:42
cool them, connect them with [music]
5:43
high-speed cables and build them with
5:46
concrete, security, and fire
5:47
suppression. All in all, a single large
5:50
AI data center cost 10 to 25 billion to
5:53
build. [music] And this is where the
5:55
race is happening. Like I told you in
5:57
the data center case study, if you look
5:58
at this graph, in 2010, the world
6:01
created or replicated two zettabytes of
6:03
data. That's roughly 2 trillion GB. But
6:06
fast forward to today, something
6:08
terrifying is happening. By 2026, the
6:11
[music] world is projected to generate
6:13
221 zetabytes of data. That's over 100x
6:16
more than in 2010. So, the world is
6:20
producing more data in a month than it
6:22
did in all of history until 2010. This
6:25
is the reason why the investment in data
6:27
centers has shot up from $90 billion to
6:29
$725 billion in just the last 6 years.
6:33
Now, here's a number that made me
6:35
question everything that is happening.
6:36
The PIMCO report says that big tech
6:39
capeex will consume 94% of operating
6:42
cash flows. I repeat 94% of operating
6:46
cash flows over the next 2 years. You
6:49
know what that means? If big tech earns
6:52
$100, they will spend $94 back into
6:55
building AI infrastructure. Only $6 will
6:58
be left for dividends, buybacks, salary
7:01
hikes, innovation, and everything else.
7:04
In 2023, that same ratio was just 40%.
7:07
And now it stands at 94%. So do you
7:10
realize big tech is betting 94% of all
7:14
its money into just one assumption. And
7:16
the assumption says that in just 5
7:18
years, the world will need so much AI
7:21
compute that every dollar being spent
7:23
right now will practically look like a
7:25
bargain. Sounds unstoppable, right?
7:27
After all, we are producing so much
7:29
data. Well, here's where it gets
7:31
dangerous. Now, let's forget economics
7:33
for a second and just imagine that you
7:34
are a business owner. Let's say you
7:36
spend $10 million building a coffee
7:38
machine factory. Now, imagine that after
7:40
all that, your factory only sells
7:42
$400,000 worth of coffee machines in a
7:45
year. So, you just make $400,000 from a
7:48
factory that cost you $10 million. Is
7:51
that good, bad, or terrible? You tell
7:53
me. It's terrible, right? Why would you
7:56
build another factory if your current
7:57
factory doesn't make any money? Now take
8:00
that exact same example and apply it to
8:02
AI. Now let me show you the math. JP
8:05
Morgan sat down and did this calculation
8:07
and the logic is pretty simple. If
8:09
you're an investor and you put money
8:10
into something, you would at least
8:12
expect a 10% return. That's bare minimum
8:14
any serious investor demands on a risky
8:16
bet like this. So JP Morgan said for AI
8:18
giants to justify all the money that
8:20
they're spending, how much money does AI
8:22
actually need to bring in every year?
8:24
The answer was $650 billion every single
8:28
year. Okay, now remember this figure,
8:31
$650 billion. Now, do you know how much
8:34
AI is actually earning right now? Let's
8:36
add it up. OpenAI, the makers of Chad
8:38
GBT, make $25 billion a year, and
8:40
they're losing $14 billion a year.
8:43
Anthropic is set to make $47 billion at
8:45
best if their current run rate goes on
8:47
for one year. As of now, the target for
8:50
Anthropic is about $26 billion by the
8:52
end of this year. And let's say Gemini
8:54
also makes $25 billion. So every major
8:57
AI model company combined make around
9:00
$75 billion with OpenAI losing 14
9:03
billion and Anthropic losing 3 billion
9:06
in 2025 alone. Now put these three
9:08
numbers side by side. Money that AI
9:10
needs to earn to make sense $650
9:12
billion. Money AI is actually earning
9:16
$75 billion. Money that AI is losing is
9:19
minimum $17 billion. But the money that
9:21
the giants are spending on top of all of
9:23
this is $725 billion. That difference
9:28
between what they earn and what they
9:30
need to earn is about 9 to 10 times. Now
9:34
read that one more time slowly. For
9:36
every single dollar that the AI industry
9:38
is bringing in, the tech giants are
9:41
spending 9 to 10 times more than they
9:44
earn. This is why SEOA's David Khan
9:46
calls this the $600 billion question. a
9:49
$600 billion annual revenue deficit that
9:52
nobody knows who will fill. Now the
9:53
single biggest argument against this
9:55
crazy number is Ganesh enterprises will
9:57
pay money. Every single one of these
9:59
companies will become profitable and
10:01
investors will make money when the
10:03
enterprises will pay money because AI is
10:06
making all enterprises very very
10:07
efficient at dirt cheap cost. Okay.
10:11
Well, that is not the right argument
10:14
because even I thought the same and then
10:15
I found the service. McKenzie says 73%
10:18
of enterprise AI deployments are failing
10:20
to achieve projected return on
10:22
investment. BCG says only 5% of
10:25
companies are seeing substantial ROI
10:26
from AI. MIT says there is a 95% failure
10:29
rate in achieving measurable financial
10:31
returns. Only 29% of the executives can
10:33
even measure their AI return on
10:35
investment. And this is where the story
10:38
gets its first phase. Meet Flo. He runs
10:40
an AI startup in San Francisco called
10:42
Lindy. They have about 25 employees. In
10:45
June 2026, he did an interview with CNBC
10:47
that shook the AI industry. His team was
10:50
spending more on Anthropic Cloud API
10:52
than on their entire payroll. So, you
10:55
know what Flo did? Flo switched 100% of
10:57
his traffic to Deep Seek and his cost
10:59
dropped by 90%. And then Uber CTO
11:01
admitted publicly that Uber had blown
11:03
its entire annual AI budget in just 4
11:06
months. And that ladies and gentlemen is
11:09
the twist because everyone assumed that
11:11
enterprises would keep paying more and
11:13
more for AI tokens forever. That was the
11:16
whole model. That is why OpenAI is worth
11:18
$850 billion. That is why Anthropic is
11:21
worth $965 billion. But in June 2026,
11:24
Enterprise started doing something that
11:25
the market did not expect. They started
11:28
looking for cheaper alternatives. Which
11:30
is why Alex Karp, the CEO of Palanteer
11:33
went on CNBC and said this on 1st of
11:35
July. Every single enterprise I deal
11:38
with, they're like, I am paying for
11:39
tokens that create no value. These
11:42
people are stealing the weights and
11:43
alpha of my business and they're
11:45
creating a wealth tax. And the reason
11:46
for it is because [music] these models
11:49
have been completely over irresponsibly
11:51
oversold. And Palanteer, if you saw our
11:53
previous case study, is one of the
11:55
biggest software enterprise companies on
11:57
earth. They sell to the CIA, the US
11:59
government, Airbnb, JP Morgan, and god
12:02
knows how many large companies. and the
12:05
CEO of that company is telling you that
12:07
something has completely gone wrong.
12:09
Now, at this point, I know exactly what
12:11
you're thinking. You must be thinking,
12:12
"Yeah, Ganesh, this is a rich man's
12:14
problem. Nvidia losing 500 billion, Sam,
12:16
Dario, Sundar, they're all billionaires.
12:18
How am I getting affected by all of
12:20
this?" Well, let me take you to South
12:22
Korea and show you how.
12:26
This is a factory in South Korea that is
12:27
owned by Samsung. This factory makes a
12:30
very specific kind of memory chip called
12:32
DM. the same DAM that goes into your
12:34
laptop, your smartphone, your Xbox, and
12:37
even your washing machine. In 2024,
12:39
Samsung had a choice. It could sell its
12:41
DAM to consumer companies like Apple,
12:43
HP, or Dell. Or it could sell a special
12:47
extremely expensive version called high
12:50
bandwidth memory to AI data centers. And
12:53
guess which one pays more? The AI data
12:56
centers paid 10x more per module. So
13:00
Samsung, SKH Highix and Micron, the
13:02
three companies that control 90% of the
13:04
world's memory chip supply, did what
13:06
[music] any factory would do. They
13:08
shifted 93% of their production towards
13:11
AI memory because that is a rule of
13:13
capitalism, right? Capital always flows
13:15
to the highest bidder. Now watch what
13:17
happens at bigger scales. DM prices are
13:20
up by 171% year-over-year as of March
13:23
2026. DDR5 memory are up 4x since
13:27
September 2025. A contract price for PC
13:29
memory is up by 105 to 110% in one
13:32
quarter. In fact, Dell CEO said that the
13:34
price of 1 GB of DAM went from 0.43 to
13:37
$2.39 in just 6 months. That is a 5 and
13:41
a half times increase in price. In fact,
13:43
that is why on 25th of June 2026, Apple
13:46
did something that it had never done
13:47
before. In the middle of a product year,
13:50
Apple simply raised their prices. This
13:52
is the reason why they said, "We have
13:54
never seen a competent price increase
13:56
this much this quickly. We've shielded
13:58
our customers from these increases so
14:00
far. But now we've reached a point where
14:02
we need to begin raising prices. Now
14:04
that is Apple telling you this guys. The
14:06
richest most vertically integrated tech
14:08
company in the world is telling you that
14:10
they cannot absorb this cost. That is
14:13
how you are paying the AI tax. But this
14:16
is where a scary question arises. If
14:19
enterprises are moving off claw to save
14:21
90%. If Apple cannot absorb cost
14:24
anymore, if the ROI is broken, then why
14:27
are Amazon, Microsoft, Google, and Meta
14:29
still spending more? Well, the answer is
14:32
one of the most fascinating concepts in
14:34
economics, and it explains every single
14:36
bubble in human history. It's called the
14:38
capital cycle. [music] In this cycle,
14:40
there are four steps. Step number one,
14:42
high returns attract capital. Step
14:44
number two, capital keeps flowing until
14:47
over capacity is built. Step three,
14:49
return over capacity eventually results
14:51
into collapse. And step four, everyone
14:54
dies except a few survivors who
14:56
eventually make a fortune when demand
14:58
catches up. And every bubble in modern
15:00
history has followed this exact same
15:02
pattern. Let me show you how. In 1996,
15:05
the US passed the Telecommunications Act
15:07
because just like AI, the internet back
15:09
then was a life-changing technology
15:11
which was exploding in demand. The story
15:13
was so intoxicating because it was clear
15:15
to the world that internet was the
15:17
future. Data traffic was exploding and
15:19
everybody just knew that bandwidth
15:21
demand would grow forever. Some founders
15:24
even believed that internet traffic
15:26
would double every 3 months. So money
15:28
came pouring in to build the fiber optic
15:31
cables. And then came the flood. Several
15:33
companies raised to lay fiber optic
15:34
cables across the country. And in just 5
15:37
years after that act, telecom companies
15:39
poured more than $500 billion into
15:41
cables, switches and networks. And if
15:43
you look at the financials of these
15:44
companies, you will see why the AI
15:46
bubble is very similar. A company called
15:48
Global Crossing went from a small equity
15:50
check to a $47 billion valuation without
15:54
ever making a single year of profit.
15:56
Corvis, a fiber equipment startup,
15:57
pulled off a $1.1 billion IPO with
16:00
literally 0 in revenue and carried a $32
16:03
billion market cap. And just when
16:05
everyone thought they'll become
16:06
millionaires and billionaires, the
16:08
collapse happened. You know what
16:10
happened? Everyone thought that the
16:12
internet will explode by 1,000% year on
16:14
year, but the internet traffic only
16:16
exploded by 100% year on year, which was
16:18
great, but not great enough to justify
16:21
the cost of investment. You know how
16:23
much of this installed fiber was
16:24
actually utilized? Take a guess. 50%,
16:28
20%, [music]
16:30
10%, at least 5% must have been
16:32
utilized, right? Well, guess what? By
16:34
early 2000s, as little as just 2.7% of
16:38
the installed fiber was actually
16:40
carrying data. Over 95% sat unused
16:43
underground. That is how trillions of
16:45
dollars of cable got buried without
16:46
earning anything. So when there was no
16:48
revenue, bandwidth prices collapsed by
16:50
up to 90% and the giant started failing.
16:53
WorldCom, after hiding $3.8 billion of
16:55
expenses to fake profits, filed the
16:57
biggest bankruptcy in US history. Global
16:59
Crossing, that $47 billion darling went
17:01
bankrupt. And in total, the telecom
17:03
crash wiped out $2 trillion of market
17:05
value with stocks going down by 95%. And
17:08
then [music] came step four, the
17:10
survivors. Now, here's where the twist
17:13
comes in which makes it the perfect
17:14
mirror for AI. Those fiber optic cables
17:17
did not vanish. They stayed in the
17:19
ground and within a few years, demand
17:21
finally arrived. YouTube happened,
17:23
streaming started, cloud storage became
17:25
a real thing, and [music] smartphone
17:27
became popular. And suddenly the world
17:29
needed exactly what had been overbuilt.
17:32
So the survivors bought the wreckage for
17:34
dirt cheap prices and that wasted cable
17:36
became the physical backbone of the
17:38
modern internet. The same infrastructure
17:40
that made Google, Netflix [music] and
17:42
AWS possible. So do you realize that
17:45
technology was real? The internet did
17:47
change everything but the bubble still
17:49
burst. Why? Because the demand was
17:51
exploding but not so much to justify
17:53
over capacity. So the technology
17:55
survived but the companies that built
17:57
did not. Now, here's what the pattern
17:58
looks like. Britain in 1846 authorized
18:01
9,500 miles of track and one/ird of it
18:03
never got built. And then the bubble
18:05
burst. America in 2000 laid millions of
18:07
miles of fiber and 97% of it was unused
18:10
and eventually the bubble burst. In
18:12
2026, America alone is building 725
18:16
billion of data centers per year and we
18:19
don't know how much of it will actually
18:21
be used. So the question is, will it all
18:22
be worth it and become the greatest tech
18:24
story ever told? Or will it go down as
18:27
the greatest bubble in world history?
18:29
Only time can give us the answer. So is
18:32
this definitely a bubble? Well, we don't
18:34
know that yet. Why? Because the
18:35
companies in the telecom bubble were
18:37
funded by debt and they were losing
18:38
money. But Nvidia earned $120 billion in
18:41
net income last year. And Microsoft,
18:43
Google, and Amazon are literally the
18:44
most profitable enterprises in human
18:46
history. So they won't collapse like
18:47
other weak companies. Similarly, at the
18:49
2000.com peak, the NASDAQ 100 forward PE
18:53
was about 60x. Today, it's around 26x.
18:56
It's higher than normal, but nowhere
18:58
near the insanity of 1999. So, if anyone
19:01
tells you for certain that this is a
19:02
bubble, they're lying to you. And anyone
19:04
tells you that it is definitely not a
19:06
bubble is also lying to you because the
19:08
truth is uncomfortable and it's
19:09
somewhere in between. There is a very
19:11
high possibility of a bubble, but not a
19:13
certainty. The technology is real, the
19:15
revenue is real, and we're not betting
19:17
on whether AI changes the world or not.
19:18
We are betting on whether the price for
19:20
it actually makes sense or not. So now
19:23
the question is what exactly is going to
19:24
happen if the bubble burst? And what if
19:26
it doesn't? Well, there are two
19:29
possibilities. Path one, the bubble
19:31
pops, jobs are lost, the NASDAQ crashes,
19:34
and every big tech company slams the
19:36
brakes on spending, and that spending is
19:38
what feeds our Indian IT and service
19:40
sector. So your cousin's first coding
19:42
job disappears before the boom can catch
19:44
him. Path two is that the bubble doesn't
19:46
pop. Instead, to justify those trillion
19:48
dollar valuations, the company will try
19:49
to race towards profit. So the price of
19:51
AI, as in the token cost will shoot up
19:53
and suddenly only the giants will be
19:55
able to afford AI. So the cheap AI tools
19:57
that you use today will eventually
19:59
become a luxury. So a lot of AI products
20:01
might die not because the tech failed,
20:03
but because it just got too expensive to
20:04
run. Or lastly, we could expect a
20:07
miracle that will drop down the token
20:09
cost, will make enterprises pay, and
20:11
everyone will make money. But that, my
20:14
dear friends, is a teeny tiny
20:16
possibility. This, my dear friends, is
20:18
the story of the AI bubble. Now, you
20:19
tell me in the comments what do you
20:20
think about the situation with the
20:22
trillion dollar valuation that we’ve
20:23
seen. Is this really a bubble or is
20:25
this the greatest bet humanity has ever
20:27
taken? That's all from my side for
20:29
today, guys. If you learned something
20:30
valuable from this history, please
20:32
hit the like button to support our work.
20:34
And for more such business and political
20:35
histories, please subscribe to our
20:37
channel. Thank you so much for watching.
20:38
I will see you in the next one. Bye-bye.
20:50
[music]
~謝謝閱讀，全文約4,930字~
https://www.youtube.com/watch?v=WcckBmkauBQ&t=497s

高職的英文的確是比高中簡單，但一定要知道: 出社會後，只要是同個工作職責，那無論學經歷是高中/大學、或高職/科技大學，所需要面對和用到的英文難度其實很接近。***本文的鄭重聲明和定義在本文第六點，因為這一點很多人可能覺得較無趣，故將之放到第六點，請需要者觀看，謝謝。
不少人去讀高職的原因之一是 “覺得自己不會讀書，所以故意去讀高職，覺得這樣子的話，某些科目的難度就會降低”，但沒想到，竟然變成 “想躲狐狸，卻遇上了老虎”:
我自己教過很多背景的社會人士，也教過讀高職、高中的學生，發現了有些現象，所以，我心裡一直困惑為什麼沒有人告訴高職生這些事情:
「難道沒有人在高職/高中階段就告訴學子們: “雖然選走高職這條路的學生，在高職階段學的英文，會比高中生簡單很多，但大學畢業、進入社會工作後的實際狀況是:
同事的背景有各式各樣的，自己領域所要面對、要溝通的很多國外客戶是一樣的、你們想爭取或一起完成的成果可能是一樣的，你們需要說出口的英文是一樣的、需要聽懂的英文也是一樣的、公司發給全體同仁的英文信是一樣的，而且這些英文信的起草人是擁有美國/英國/澳洲等以英語為母語的國家的碩士學歷的人寫的，在這些時候，都不是只有和跟自己一樣是高職畢業的人們共事而已。
還有，只要是同個領域，無論是讀大學、或讀科技大學，所需要用流利英文交流的全英文研討會、所需聽懂的全英文演講、所需要看的論文，難道有不同嗎? 只要是同個領域，都是一樣難度的，並不會因為讀高職/科技大學體制就只需要懂某些簡單的英文，也不是讀高中/大學體制才會需要看較難的英文，而是只要是那個領域的，想要做到那麼好，那到後來，勢必還是會需要達到該領域所需要的英文程度。
既然最後的終點都是要用同樣難的英文去研究，那為什麼卻沒跟讀高職的人說清楚:
“Hey! 雖然我們安排高職時期的英文比讀高中簡單很多，但要是你們以後出社會，可能會用到跟讀高中體制、出國留學回國的人一樣難的英文喔!”--- 這樣才能讓大家有正確認知:
讀高職時學比高中簡單的英文，但出社會後，有些人所需要的英文能力很可能還是要達到跟讀高中/大學這些學較難的英文的人一樣，所以，不要以為自己讀高職，未來就不會變成需要跟讀高中的人一樣難度的英文喔~」--- 這也就是我寫本文的目的，希望讓儘可能多的人知道這點。
｜二、實際教學狀況、與實際人生｜
在高職階段是躲避了讀較難的英文、國文和某些自己覺得不想學的科目，但卻在出社會、有同事之後，發現到頭來還是要面對某些學科比自己強很多的同事---那些原本以為在讀高職/高中時，就已跟自己走上不同人生道路的那些人，在 “看似”走上不同道路之後，多年後在職場上又再次與自己背景不相同的人相遇，那些曾經以為 “會讀書的去讀高中”，結果後來還是跟他們變成需要用到差不多英文能力的人;
曾經自己在高中/高職時以為和他們讀的英文不同，沒想到工作後，所面臨的英文挑戰仍舊相同，噢，不! 是更難了。因為當年只是這些學科程度/成績不夠好，現在卻是直接要面對英文能力比自己強很多的上司、同事、下屬，因為他們在高中、大學、留學階段累積了很深的英文能力，而且出了社會後，大家大多沒多少時間學習和累積英文能力，進步當然也就緩慢許多~
還有，我遇過不少人在聽不懂、說不出很多英文對話時，常將問題歸因於:
(1)因為某些同事有國外學歷、從國外畢業，他們的英文才那麼流利”;
(2)或是因為急著想要精進英文能力，而過於急躁，沒看到那些擁有自己欣羨的英文能力的人，除了出國外，早在國高中、大學階段曾經怎樣努力的學英文過，但自己卻誤以為 “想要優秀的英文聽說能力，只需要從英文聽說下手，完全忽略閱讀和寫作對表達的重要性”。
｜三、無論讀高職/科大、或高中/大學，在工作後所面對的英文，有很多都是一樣的，並沒有因為讀高職或讀高中就不一樣｜
我一對一教學21年中，所教過的社會人士有高職/四技二專/科技大學體系畢業，也有高中/大學體系畢業，他們所面對的學習英文的內容和問題，有很多都是一樣的，並沒有因為讀高職或讀高中就不一樣:
(1) 他們想要懂的英文文法是一樣的；
(2) 單詞字彙雖會隨著不同領域，而在專有名詞上有所不同；
(3) 但很多常會使用的單詞字彙是共通的。
我教英文也教過高職生和高中生，高職的難度的確低許多，我了解這是很多人覺得 “既然讀高職的學生沒那麼愛讀書，而且高職很多科系會花較多時間在實作，那某些共通科目就不要那麼難--- 從這個角度來看，這樣安排好像沒錯，但實際狀況是:
“等到進入社會工作時，很多領域的工作都是會有各種學歷的同事，要一起完成或爭取一樣的案件，面對同樣到訪的國外人員，所以要用到的英文程度也是相近的。
那麼，若自己在讀高職時，覺得只要讀好高職教的內容，但在此同時，讀高中、跟自己年齡相仿的同儕卻是學更多、比自己在高職階段學的更難的，長此以往，等到進入社會、同個職場時，兩者的程度當然會有相當的差距。
雖然也還是有很多讀高中、在高中階段已經學較多較難的英文的人，抱怨自己的英文程度不夠、也還是要在出社會後去上英文課，但卻已經在過往就比讀高職的同事累積更多英文程度，可以用相對較短的時間學會更多;
｜四、在學時已學更多的人，對英文的理解和語感當然比較好，那在出社會之後要學更多英文，當然就能理解得更快｜
還有一個現象: 因為有些人覺得台灣教育體制教的用不上、也不想讀，可是，到出社會要用英文時，那些被說用不上的東西，卻常常只是基本該懂的東西，而且，不論用不用得上，學更多的人，對英文的理解和語感就是比沒學那麼多的人好，那在出社會之後要學更多英文，當然就能理解得更快。
所以，在高中/高職階段就先學比較多的人，之後需要花時間學基礎的時間就越少，但若自己過往真的就是沒學起來，那對英文的語感和理解當然就沒那麼好，已經累積一定程度的人在工作後再繼續學，花的時間當然就相對少，但過往沒好好累積、對英文理解和語感都沒那麼好，當然就要花更多時間累積，一來一回，差別就會很明顯。”
當然這“不”是說讀高中/大學體制的人的英文國文就一定學得好，而且也有人讀高職/科技大學體制、且學英文學得比前者更勤快的人，我們在此討論的是 “以比例上相對多的人”的狀況來看，而非百分百的絕對:
就像也有讀高中/大學體制、明明要花很多時間讀書，但卻願意像高職生一樣花時間去精進某種技術/技藝一樣，這樣的少數人的確是存在的，但很多高中生就是連讀書時間都不夠、沒時間在讀高中的階段去實作某些技藝;
同樣的，大部分高職生從來都沒想過: “我絕對不滿足只學高職教的英文內容，我讀高職時所學的英文，要學得跟讀高中的人一樣難，這樣我以後進入社會工作後，才不會落後那些讀高中/大學體制、和出國留學回國的同事。”--- 會這樣想和實際行動的人是少數。
｜五、禍福相依: 高職階段的英文雖較高中簡單、看似讓人喘了一口氣，但若有朝一日想學，可能要花更大心力。｜
當然這並不是說每個人都要很努力學英文，更非鼓吹傾向讀高職的人要因此改讀高中，我寫這篇文章是我在教了很多社會人士、高職生和高中生後，約在我教學十多年時，發現和觀察到的事情，所以，我寫出來，也是希望讓選擇讀高中或高職的人知道:
「讀高職階段的某些學科雖較高中簡單、看似讓人喘了一口氣，實際也暗藏著未來的危機，也就是 “禍福相依”，事情總有正面和反面，現在看似輕鬆，但若有朝一日想學，可能要花更大心力，甚至你會發現 “比在高職時，要努力學好英文，難上更多倍，因為出了社會後，就沒什麼時間學習了(更何況還有自己工作的專業領域要學，不是所有時間都能投注在英文)”。
我的意思“不”是每個人都一定要多努力的學英文，也“不”是說每個人都要多努力的讀高中的英文，而是看了此篇後，知道了自己可能想都沒想過的事情，在知道這前因後果後，若自己就是 “心甘情願的不想努力”，那當未來發生上述狀況時，就要知道: “自己早就知道、這是自己選的，因此，要欣然接受自己的選擇所帶來的後果”，不要嫉妒比自己強的人、不要憤世嫉俗地以為別人的英文好都是因為他們能出國，卻沒看到他們還沒出國前就努力的學英文……等等。
希望升高職/高中的學生能理解這些，而不是在知道高職階段的英文國文比較簡單而竊喜之時，卻完全不知道 “雖然自己高職三年不用那麼努力，未來工作後卻可能會遇到那些自己過往根本不想學的英文”，結果在高中/高職階段浪費了很多原本可以拿來學英文的時光，也在出社會後，面對比在高職/高中階段學好英文更加難上加難的挑戰--- 希望儘可能多的人知道這些，這就是這篇文章的用意。」
｜***六、本文的鄭重聲明和定義｜
本文的鄭重表明和定義: 我寫這篇是出於我實際教學經驗，而我教學對象大多是長期而言會需要不斷精進英文的類型，包括公司的管理者、研究某些專業領域、知識傳播者……等等，而我教過的這些領域的社會人士學生，有讀高職/四技二專/科技大學體制者、也有讀高中/大學體制，所以，我才會觀察及寫出本文的內容。
而這同時也意味著: “並非所有人，無論是讀高職/科大體制、或讀高中/大學體制者，都會在工作後需要精深的英文，例如: 很多服務業，只要不是想要做得超好、走上國際舞台、變得超強那種，而只是沒有太大野心、想做得相對不錯這種，則通常所需要用到的英文，大多都是相對簡單許多的生活會話而已--- 所以，很多這些領域的人們經常會批評 “為什麼學校教育體制不把焦點放在學生活會話、幹嘛要花時間學那麼多難的英文、甚至誤以為學校教的只是要讓人看得懂各種書籍和論文”，但其實當然不是這樣。
這些誤解是源於 “不同領域的人，對學英文有不同需求，想想看: 若學校真的讓學生都只聚焦在學英文會話，那也無法培養出從事上一段所列出的那些工作的人才，那難道整個國家都沒有從事研究、都沒有傳遞知識、都沒有管理跨國公司或組織的人嗎? 那還得了!
總之，不同工作、不同讀者所需要的技能不盡相同，因此，請考慮所有人的工作需求，本文在此也鄭重聲明: “本文是針對某些工作上會需要不斷精進英文的人會遇到的情況，不是所有人都需要不斷精進英文。
不過，又有一個現實常發生的問題，就是: 很多人在讀高中/高職階段，以為自己不需要學那些英文，直到後來，才知道原來自己需要，要不然也不會有那麼多人等到工作後，才開始彌補英文。
~謝謝閱讀，全文約4,077字~

從我寫文章以來，儘管我是一直保持著幫助別人的善心，也將定義寫得明確，但有時候就是會出現斷章取義，不看清楚我寫的內容和意思，明明不了解很多事情、還充滿著許多顯而易見的錯誤觀念、卻很敢批評的人; 最重要的是: 網路上不友善的人發表那些言論的本質並"非"真的想討論事情。
事實上，這樣不友善的人一直都存在著，而且，也不是只有在網路上有，我在學術圈，在實際教那麼多社會人士學生/在學生/跟家長溝通的這至今21年多的過程中，在日常生活的各種人際關係中，也都多少有遇到過這樣不友善的人，連日常生活遇到我都不在意，又為何要回應網路上的不友善？
其實我若有回應網路上不友善的原因是“因為我想幫助這些不友善的人了解更多關於學習和教育的事情”，但其實也不清楚不友善的人的真實動機，你對他們友善，他們會承認自己錯嗎？不會，唯一可以確認的是：
“就算他們真有不懂，可以像很多其他網友一樣禮貌詢問、可以虛心學習，而非貶低或批評創作者。"
既然我創作文章和影片的目的“之一”是為了幫助人，那當網路上有不友善的人時，也就給他們時間學習和成長成一棵棵大樹🌳
【網路上不友善的人發表那些言論的本質並"非"真的想討論事情】
時不時看到很多公眾人物回應被網路上不友善的人批評的事情，我都會覺得這些公眾人物其實沒有必要回應，尤其對我喜歡的公眾人物，我更希望我喜歡的公眾人物根本不需要回應，因為: "網路上不友善的人發表那些留言的本質並非真的想討論事情。"
而且，當我身為觀眾時，我根本不會被這些不友善的留言影響，我也不在乎這些留言說了什麼，因為我求真的意願和能力，能讓我去找出真相、讓我知道我支持的人說的是對是錯。
就跟我希望我喜歡的公眾人物不要回應網路上不友善的言論一樣，我相信追蹤我的網友也不希望我回應不友善的言論，同時，也祝福不友善的網友變成友善的網友🙂祝福生活中不友善的人變成友善的人🙂

因為這劇裡，台詞經常會扯到性，第一次看時，覺得有點太超過和粗俗
🚨🚨因為很多美劇牽扯到性和暴力，我覺得很少美劇可以用來教學或介紹給“30歲以下”的學生看。
This 2nd time I watch it is much better than the 1st time. For the 1st time I watched it, I felt this TV series is kind of vulgar and repulsive, because lines are, frequently, in connection with sex.
🚨🚨Violence and sex are often involved in many American dramas, which is why I reckon very few of them qualify being introduced to students under the age of 30.
但我還是全看完了，主因是我想更熟悉裡面幾個角色說英文的方式和腔調等等，還有，劇情實在太白目太荒謬了，闖出一堆很奇怪的禍但都不用解決，然後下一集就像沒事一樣、直接進入下一個主題~ 害我每次都在心裡吶喊:
“喂! 上次的事情還沒解決耶!”
However, I still finished all seasons all episodes, because I’d like to be acquainted with the speaking ways and accents of many a character in the series; plus, the plots are too stupid and way too absurd--- these two protagonists often get into troubles or even screw up things, but they don’t need to pick up the pieces, nothing has to be done, any problems would just work out themselves--- the reset button is being hit again and again, and then in the next episodes: like nothing screwed-up had happened, it's a brand new page. All of these made me shout insanely in my mind:
“Hey!!! You guys haven’t solved the problems yet.”
覺得這種寫劇本的手法也是很有趣，還是這部劇是從漫畫直接改編的???(我剛剛Google了:不是) 因為沒看過類似這種電視劇，我就一直看下去、看完全部了。
The way of writing this kind of scripts is also very intriguing, or is it really a comic book adaptation? (I just googled: No!) Never had I watched any TV series akin to this one, I just kept on watching it and finished watching it.
但這次會重看，是因為我想看這兩個無血緣(但一樣破產)的女孩成功創業“杯子蛋糕”的過程，因為我完全不記得了。看一看又覺得其實這兩個人的相處蠻溫馨的，怎麼第一次看都沒這種感覺~第一次看只覺得劇情很白目
What drives me to rewatch it is I’d like to rewind how they start up their cupcake business and succeed, ‘cause I barely remember what happened. And surprisingly, this second time I watch it, I also feel their dynamic is pretty sweet and heartwarming--- How come I never felt it for the first time? Oh~ I was feeling the plot is pretty ridiculous back then.
~謝謝觀看Thx for reading~

50歲以前才丙級廚師執照? 怎麼不是乙級?(乙級比丙級厲害) ... “乙級就60歲之前好了”--- 那麼長的準備時間?
“給自己一點緩衝嘛! 我又不是專業餐飲業的人、目前也沒計畫要開餐廳、有就不錯了”--- 那幹嘛要考?
因為我希望自己學得全面一點，而且在學的過程中，其實可以學到很多煮菜的技巧，像最近我就學到了怎樣切洋蔥可以幾乎不會流淚。
從在餐飲界打工以來，我遇到很多餐飲界的人都沒有丙級，問他們為什麼不考，他們之中很多人都回我:
“會煮最重要，要那個考試幹嘛? 又不代表會煮” --- 這回答簡直就跟談到學英文和英文考試時一模一樣，因為很多人會說:
“會英文聽說就好了，要考試幹嘛? 又不代表會說”--- 但這是錯誤的想法！因為我教學到目前21年多，還沒遇過誰英文檢定考試高分但卻不會說的，會這樣批評別人是因為某些因素（以後再寫另一篇文章討論此事）。
🚨｜為什麼很多餐廳只要客人一多，供餐水準馬上就會崩？｜
要考試幹嘛？真的是這樣嗎? 我發現:
如果你的專業不是某技能，可能沒關係，但當這個技能是專業工作時，會遇到一些嚴重的考驗，其中最顯而易見的是: “
遇到速度和時間的考驗就會崩盤”¹，這就是為什麼很多餐廳只要客人一多，供餐水準馬上就會崩，進而影響到顧客未來是否會再光顧，這些指標包括:
(1) 餐點品質下降或不均: 餐點品質就是專業，一旦餐點品質不均，人家可能就不想再來了，非常非常非常嚴重，畢竟顧客是去吃飯，首要重點就是餐點品質。
(2) 上菜速度很慢: 大家都會在意的事;
(3) 上菜次序不對: 有些東西要配著一起吃，沙拉就是要在主餐前給，蛋糕就是要配咖啡/飲料喝，若沙拉在主餐後才給，或整片蛋糕都吃完了，咖啡才上，誰會高興?
(4) 廚房團隊中，脾氣較不好的人會開始暴走，其他內場的人的情緒都會因此被影響，內場做菜的人情緒受影響，餐點品質和外場人員的都會受影響。
(5) 客人多時，若內場做菜速度趕不上出餐，外場服務人員就會被客人一直問，甚至一直道歉；且不是所有外場服務人員本身的態度都好，平常服務態度就不是很好了，人一多，服務態度又變得更差，又要催內場趕快給餐，內場情緒又不好，又影響到外場人員的情緒，那服務人員對客人的態度可能又更不好了。
以上幾點就會嚴重影響一家餐廳的營運，平常自己家裡煮煮或少數請幾位朋友，煮再慢也沒人敢罵，就算罵，自己也不在乎，但若變成要賺錢維生的餐廳，那就不一樣了:
1. 生產流程不夠好導致餐點卡住;
2. 教育訓練有問題導致新進人員根本沒有通通都被好好教導，
3. 種種原因導致留不住員工、沒有公平對待員工---這造成人手長期不足，而人手不足又造成工作時很急很忙，一急一忙就導致脾氣暴躁、整體工作環境的人際相處氛圍不佳;
4. 管理者本身是否有心改善所有問題……
總之，能把一間餐廳營運得很好真的不簡單!
🪺｜跟學英文的相同之處｜
就像學英文一樣，任何人平常愛怎樣說就怎樣說，反正能溝通就好、能聽得懂就好，就算因此而到處宣稱自己英文非常好，也沒有幾個人會批評，就算批評自己也不在乎; 但要是把英文變成專業維生的工作:要用英文來在任何正式的形式上工作、公開主持英文會議、寫作等等，那就不一樣了:
1. 遣詞用字是否符合正式工作?
2. 談吐語氣是否合適?
3. 整體英文能力是否足夠？是否在時間限定內完成應完成的英文工作內容?
4. 自己是否有意願長期持續學習以繼續精進這份很需要英文的工作……，
能用英文來把該做的工作都做好也真的不簡單!
🤖｜是否是專業，就是要經過速度和時間限制的考驗｜
(1)餐廳人多的時候是否Hold住，客人沒拿著計時器等，但心中有一個無形的計時器，程度不夠的話會非常有壓力，此時，就跟考試一樣，是能力、時間限制和速度的考驗;
(2)自己的專業工作若需要某種程度的英文，就是必須要能在時間限制和速度下，順利完成那些英文工作的任務，若程度不夠，也一樣是非常有壓力，也是時間限制和速度的考驗。
🍄｜考試能讓人學得更全面｜
在準備英文考試過程中，能學得更加全面，若我只努力學英文聽說、甚至覺得有些方面的英文我不需要懂，那我真的不敢想像我的英文能力會有多麼的偏頗;
同理，何以我學廚藝會想考試？因為考試要學的內容，都是長久有經驗的人覺得煮菜應該要懂的事情，若只自己學而不考試，會錯過很多能讓你判斷食物、或者加強煮菜技巧的知識--- 這段日子，有些我遇到的餐飲界的人教了我一些煮菜或營運的事情，我覺得我這半年學了好多。
而且，專業餐飲經營者若去參加廚藝班，能遇到很多同領域的人，我有聽人說過有老師很有經驗的，也會在上課過程中提到要怎樣改善餐廳的流程、怎樣經營餐廳、怎樣改善上菜速度……等等，所以，考一張廚藝執照，其實並不是只在學做菜而已，並不是 “會煮就好了，考那個證照幹嘛”，而是過程中會學到很多控制時間和速度的方法。
同樣的，我們學英文也是，並不是 “會聽說就好了，考試幹嘛?”而是過程中你會發現自己的很多盲點、很多可以改善的地方，也會看到別人的程度而知道:
“天啊，原來他理解和完成的速度那麼快，自己理解英文的能力原來還有那麼多需要學”，若需要某份需要某種英文程度的工作，也才會知道：
“原來那麼多人比自己強; 尤其在有別人在場、親眼見識到別人完成的時間和速度都比自己快那麼多，難怪別人會被選到--- 但不必因此而灰心喪志，而是因此加強自己，讓自己也能達到那種程度，自我實現”。”
☘️｜有證照又不代表比較會? 重點是自我學習，還有外行會知道:至少你有專業知識｜
至於時不時就聽到有人說的: “有證照又不代表比較會煮/有證照又不代表英文好”，這其實很難說，要怎樣學、要怎樣應用，其實很看個人：
有些人把考試看作是很有壓力、又無用的東西；也有人努力從中學到很多東西，達到既有證照、也煮得好/英文真的很好的境界，當然也有人有證照，但表現普通或差強人意，甚至也有人拿假證照，要怎樣操作是看個人，但就非專業的人來看，人家看你有證照，不是就這樣會判斷你有多強，而是知道:
“至少你有這方面的基本知識”。
而且，無論如何，最重要的重點是自己學到了東西、是自我學習。
🫡｜結語｜
以上草草記下我要考廚藝執照的原因，也串起學英文與學廚藝相同的點，讓人知道 “無論是英文考試、或者其他類型的考試，其實能從考試中學到很多、也把自己不懂的東西學得更全面; 不需要把考試當壓力，給自己多一點緩衝時間，想想看:
若自己是專業餐飲業者，三年準備不了，用五年，至少五年後就考到了，但若完全不去學，十年後也沒有; 同理，我也給自己多一點時間學廚藝，我既不是專業餐飲，也不想要有壓力的準備，那就五十歲考到丙級就好~ 呃~ 其實六十歲才考到丙級也是可以的啦!”
~謝謝閱讀，共約2,381字~