https://www.youtube.com/watch?v=kkbEcsHke9k

那時間還沒到我不太會去設定這個那在7分鐘之後就開始我先去休息一下 The Nha Trang I'm going to make a table. 1 tbs of butter 1 tbs of flour 1 tbs of baking powder 1 tbs of baking soda 1 tbs of baking soda 1 tbs of baking soda 1 tbs of baking soda 1 tbs of baking soda 1 tbs of baking soda 1 tbs of baking soda 1 tbs of baking soda 1 tbs of baking soda 1 tbs of baking soda 1 tbs of baking soda反正有什麼問題可以先留言反正就等下後續中途我們反正就是順便一個直接 q & a 的部分那看平常有在用股票起貨的遇到一些問題像最近應該其實蠻多之前就是已經像比如像台積電大半年都是正價差初期正價差只有0.幾到1塊錢後面都拉到正價差到2塊多甚至有一度到3塊多其實都蠻大那像這種情況就是就像我之前講他的換算成年利率的一個部分的話已經超過3%以上的話這個通常來講不建議啊那就是直接換成股票的方式去運作那還蠻多指數型的或是像 ETF 型的那怎麼樣去搭配配置成你自己的期貨的一個方式像股票期貨其實指數也算是一個期貨就是大型的 ETF 期貨這樣子而已那有問題就是留言吧留言我們就直接在線上直接回答我們以後可能盡量可能採取本來是捲組本來是一個月一次那這時間會比較久所以可能之後下半年因為其實我的巡迴講座已經講完了所以比較時間上會比較充裕以後可能是我們每個月就採取兩呃比如說雙週四比如第二週或第四週或是一三週二四週好了因為一三會有一三五的問題二四周來搭配做兩個禮拜的一個行情的一個分享跟所謂的一些的教育訓練的課程比如說當下有什麼樣的類股或是期貨選擇權該注意像昨天就很明顯的是七月份的選擇權就是蠻異常的一個扣的部分就增加蠻多那這種蠻異常增加蠻多的話,其實有些人會解讀怎麼樣的方式買電力股居多缺電啊為什麼今天剛好有一個一個投信的戴超級機器人來問我為什麼買電力股居多啊缺晶片你知道高階的晶片是很缺沒錯但是電力更缺啊你會發覺電力更缺每個行業都需要啊對啊啊晶片現在來講是因為是 AI高階這一塊目前是很缺沒錯但是你要想的電力什麼股都缺啊開玩笑連軍工醫療生技各行各業都缺沒有錯但是你要想的這電力什麼股都缺啊開玩笑連軍工醫療生技各行各業都缺那他缺的廣泛程度更多而且缺口就以臺灣而言就缺口更大阿不然你看有沒有各區三不五時都停電不是不是說停電跳電華晨呃重電我不會去看因為其實重電華不管是華晨重電我不會去看因為其實不管是華晨,市電,中興電來看的話他的重點其實有在聊就是產能的問題嗯遇到實際下單操作不太熟如何踏出第一步就像買股票一樣買個兩張的意思買一口就等於是買兩張的意思概念是這樣時間到了咩還有一分鐘,最後一分鐘7點半就準時開始來查略一下股票期貨是個還不錯的一個工具但是整個運用很多人好那7點半那我們現在開始就是講一下股票期貨的運用時機跟方式那7月份的異常call那個等一下我後面再分享給大家基本上來股票期貨的運用通常我會把它分成幾個類別呃我先講我自己平常在做呃會運用到股票期貨的原因跟指數期貨概念是一樣就是其實就是我是常目前反正幾年是常態性多頭的那我習慣是滿倉的一個做法那滿倉做法基本上你滿倉做法你像我會提倡的Cover Call那你試想一下比如說1000萬你買1000萬的股票買進去那你手邊不是都沒錢嗎那你還做什麼Cover Call就不可能嘛那所以變成說我們在做有些的股票的部分好像廣達 四季鋼這些都會轉成相對來講換成股票期之前台積電也是一樣但是因為台積電的增加差太大轉成現股那比如說我買一口的廣達好了那以廣達現在來講的話大概是60萬那60萬的話我其實我準備大概多少我只要準備五分之一不到那我只能準備10萬塊就可以做廣達期貨的一口的概念去做那做進去之後是不是剩下50萬50萬其實我會放在期貨的保證金因為是一樣是用60萬再做廣達期貨只一口兩張的概念那四季鋼也是一樣就是六十幾萬那你一樣買一口等於是買兩張張的概念那市集剛也是一樣就是六十幾萬那你一樣買一口等於是買兩張也六十幾萬兩個加起來就一百二十幾萬那事實上用保證金就只有20萬左右那剩下空的100萬這100萬就是我要做couple code之後再運用調整那通常來講的話這個東西我的運作方式在於說這個在資金裡上其實是呃不是太缺乏資金的時候你槓桿倍數沒有開太大的時候那股票期貨的運用方式就是用這樣子那剛才試想一下如果我今天是完全空手沒有單子的人那我其實市場啊比如像廣達市集剛各買一口好了那事實上來講120幾萬的資金要照理講120幾萬但是我們股票期貨做只花了20幾萬而已那你去試想一下那這樣子方式來講120幾萬的資金要照理講120幾萬但是我們股票起碼做只花了20幾萬而已那你去試想一下那這樣子方式來講的話我還可以控出將近有100萬的資金100萬的資金如果今天不考慮加差我100萬一年我還可以多生出1.78%利息等於是光利息收入還可以1.7萬那1.7萬來講的話扣除每個月的轉賬成本所以在一口了不起算100塊200200块,2000多块钱我还可以省下当年将近有1万5的交易成本那扣除现在来讲说是正价仓所以为什么我们要算那个正价仓的幅度不能超过3%以上,基本上2.5%以上就已经有点犹豫了3%以上的话就比较不考虑好,那这样的考虑下来的话就是说它如果在2.5%以内的话我为什么其实有时候比定存还高一点你还是用因为为了我剩下空出来的资金可以做cover call这个平均cover call的做法平均大概是好歹再怎么烂你至少一年有3-5%的一个概念模式所以这个东西拿来控制你的交易成本多的增加他的转仓其实是划得来那这样的做法来讲的话变成说我的习惯性的是股期的运用是我一直在讲期货选择权是辅助股票现货交易的所以我是拿来用辅助说我的股票交易的时候把资金控出来部分去做cover code运作的一个概念那这种做法的话通常在股票期货他是呈现平价差或是正价差只有在两个%左右上下甚至更低的话这个时候状况我就会去运用那甚至有些时候是股票期货他是呈现逆价差有出现过特别是以往年其实有出圈期的时候曾经有弃权弃席或是什么样股票我曾经有棄權棄息或是怎麼樣股票其實我曾經有逆價差的時候這個其實狀況裡面你更我如果是長期要持有的人我更需要更一定要去把股票賣掉換成股票期貨才運作那這種做法其實對很多人剛才講說不曉得怎麼賣出第一步事實上你就可以事實上賣出第一步的概念來講去挑出那種股票期貨的正價差幅度就是每年轉差每年轉差我怎麼看每年轉差你去看今年12月份的個股期貨算起來跟現在的股票價格有沒有多超過1%以上的或1.2%以上有超過1过1%以上的或1.2%以上有超过1.2%以上的那就不考虑有在1.2%以内的这些股票期货的部分我就当做我把这两张股票的买的方式直接买一口远月份的一个股票期货等于就是直接买两张的股票的概念这样我就可以长期的放下去放到12月最早放到12月那你如果不太想转仓你直接远月份看起来这样换算起来看可不可以说这样子放时间比较长那基本上我会习惯是用仅月份原因是在于说我还是需要流动性的一个考量因为实际上越远的月份它的流动性越来越差那所以用这种方式去考量这运作的话就是我就换成股票期货去运作那是刚才就讲了那我如果像是广告市集刚刚买一口事实上等于是各买两张的需要120万的资金的时候那我放进来期货的保证金也是基本原则上如果不开刚刚就是放120万可是你下进去的时候发觉只用20万而已还空出100万干嘛所以这个状况里面的话通常这种运用的方法我会给大部分人建议来讲说好你就当做是用融资买股票你就留五六十万的资金在期货保证金的账户上剩下的五六十万的六十万的部分就放银行去放定存去升那个利息那为什么多放一些每个月在转仓或者是如果大盘这些个股在回挡修正的时候补保证金的问题这些可以忽略掉就省略掉不太会出现这个问题所以用这样子来讲的话就是模式来讲的话相对性來講就不會造成說你資金遺産但是你剩下一半的保證金就放銀行去升利息為什麼你放期貨保證金沒有錢啊你說60萬能算多少60萬好歹一年也將近有快一萬塊利息一個月也將近有800塊啊叫他減嘛對不對你加個油可能加個三三四公升二三四公升也差不多當作出去玩也可以或者吃一頓大餐也行啊何必把這個錢當備在那邊對不對對啊你放在那邊對對營業員也沒什麼貢獻對企業公司就增加利息收入而已這沒有必要好所以這個東西來講說變成說你如果是足額保證金的狀態不開槓桿的話就是利用這種模式那為什麼其實如果是足额保证金的状态不开杠杆的话就是利用这种模式那为什么其实会要多放一些保证金刚才讲20万那你我多放个五六十万还能还有30万要干嘛事实上因为我的模式在于说我的持股现股的部分除了这些以外还有比如说还有其他的股票大概这样大概有50档左右那这些股票我把它當成合成起來相當於一個大盤加權指數的概念那為什麼可以把這個方式因為裡面一些權值股像台積電仿達像鴻海有一點點那像比如說還有富邦青啊這些權值股這些前十大權值股裡面有一些會跟大盤的聯動係數其實還是相對來高有蠻高的那我會用這樣子把它當成是权指数的概念那也就是说同样我的股票有120万的部分可不可以把它视为是有买了一口小台对不对有些是股票期货有些是现股那股票期货是省下来的钱省下一半的钱就是当保证金嘛剩下来的钱就当那个放银行去损利息那有些股票是买现股的期貨是省下來的錢省下一半的錢就是當保證金嘛剩下來的錢再當放銀行去存利息那有些股票是買現股的部分我做什麼我就把它放成那個做股票出借那像比如說像高麗都還可以借到4%以上那像世紀剛都還可以借到1.7%像還有一檔那個兩三年前買的那個什麼祥明8091的部分有一借都是借了大半年以上甚至借了一整年都沒有還那個利率都可以借到1.2到1.6之間那也不錯啊那概念就變成說你偶把相對來講買的股票一樣還是有利息收入的概念去做那好不管有沒有出借反正都還是股票就等於是你自己手上還有股票期貨那我把它組合起來像120萬當做一口我就可以賣出價外的選擇權扣賣出一口為什麼我看要賣出周的還是月的啊比如說賣月的來講的話我會抓一個區間間距我們現在上周昨天啊前天就寫說間距至少1200點那因為是選擇權那權利金波動異常的位置大概是1200點是比較明顯那基本上為什麼我們會選�择卖价外因为我们最主要是还在赚股票上涨这一段的1200点这个区间里面在上涨的过程里面不会因为选择权而把我的获利绩效吃掉所以要卖远一点你当然我可以卖个500点可以很近可是涨超过500点你的股票获利就變成持平了因為有什麼被選擇權的扣給吃掉了那我要被吃掉的間距我會拉大一點為什麼我主要是要賺股票的上漲這一段的空間我不是要賺錢權利金是輔助是搭配用的不是他不是最主要的你的獲利來源否則你會看這這兩年漲這麼多你如果只是因为赚这个权利金那你会发现你行情其实错失的更多这种概念来讲cover code的做法的所以为什么股期的运用这样运用下就是同样在满仓的状态之下用股票期货的运用呃组保证你的钱足额够的时候这种运用方式就是空出你的那个资金的部分可以做cover code的运作甚至有时候资就是控出你的资金的部分可以做Cover Code运作甚至有时候这些控出来的资金的时候可以做加减码调控的时候这个资金就是有必要的否则你买完了之后一般买股票通常要买的直接再借出来你直接不要花钱吗我问股票期我又不用花钱而且还可以省利息这个差别就是这样的状态里面像之前以前都还用那种直接的话你发现那我直接借出来的钱再去用那如果股票期用的话事实上因为我要避险的资金其实期权的方式的资金不需要那么多可以用这种方式来运作那刚才你讲说手续费很低手续费很低对这个是属于说你如果是要做短线上的话那你会发觉说手续费当然低很多那像比如说我今天早上就是有去充开机店那个我买955然后买957这样子光这两块钱基本上一块钱差价就可以赚钱了你用现股基本上是不会赚到钱那用股票起货做一块钱基本上一块钱差价就可以赚钱了你用现股基本上是不会赚到钱那用股票期货做一块钱就已经赚钱了这概念来讲的话就是说就是要交易成本相对很低那这种交易成本低特别是股价越高它的效益性会越强那通常来讲其实也不用的50块以上的股價股票期貨這個交易成本就會差很多那其實很多期貨商或是期貨營業員大概跟你講說股票期貨的交易所需費稅金其實很低所以說實在的股票期貨基本上你如果被收超過30塊以上我覺得那個都是都是相對貴蠻多的你自己去評量有些可能他有些什么服务性比较好的那你就认了吧对那不然正常来讲的话其实市场上应该是价格其实也没有那么高那也就是说这种状况里面去运作的话你会发现说一买一卖手续费可能就不到100块然后税金加起来可能就100块那也就是说这样的状况里面你像台积电你随便买一张,不要说两张你光一张的那个税金你看快100万了嘛100万你跟税金的话就多少钱?3000块了那这样子其实你会发觉说这个倍数差了,还是手续费的话差10-50倍,这种状态里面就差蛮多好,那基本上剛才講說Sale code原價外來吃下完整上漲這個我等一下再繼續再回答那再來就是股票期貨來講的話,有些如果你的稅率相對比較高比較多的人那你比較高比較多的人,就剛才講說如果股票期貨在轉,這個所謓的跟现股的价差比较低像最近这个应该这十几年来台湾的股票的出权期的那个模式在十几年前以前几乎是出权的比重会比较高很多那你会发觉最近这十来年出息的比重越来越高了这种状态里面除息比较高的话这个用股票期货的一个调整会相对来讲会慢慢的会比较适合为什么第一个除息如果你的所得税率高或是分离客户28%来计算的话你计算一下有些如果殖利率太高的现金股息的个股他如果个股期如果股价是差不多的那基本上他就可以做成换股票期货然后把你的股期,如果股價是差不多的那基本上他就可以做成換股票期貨然後把你的股票賣掉那一樣有參加除息那除息一樣參加這個方式來參加的話那相對來講在這個除息的部分就不用納入你的所得不用納入二代結盤像之前去年還前年的時候有那個長榮減資嘛你去看嘛減資一樣啊你用股票期貨去運作的時候減資下來那個錢一樣是減資下來那你一樣股票期貨運作的時候一樣下來那個錢馬上其實當天其實這個調整權利金那個契約金的調整的時候這個方式其實都會給你去補償那補償的狀況之下你會發覺說那當天的錢比如說我减值或是除息的钱通常可能要隔半个月那像股票期货的运用方式来讲的话不用,我除息当天钱就直接拿到手所以在资金的运用弹性的空间上来讲的话它会好非常多所以像这样来讲刚才讲的第一个方式是如果足额快我要去省下一些资金做弹性运用的时候就用股票起货那要考量的是转仓每年的转仓的正价差的一个成本不能高过2.5%以上就会考量3%以上就不考虑那再来就是如果是你要比较偏向短线交易的部分那股票企业来讲的话它运用方式的话就是它的交易成本比较低不过我这边来讲还是一样奉劝就是短线交易尽量就我个人不代表市场尽量避开做短线交易做多做爽只是爽到营业员跟企业公司那对你的绾效有沒有比較好我不知道那我要常常我跟有來聊的人我會講其實就很簡單你怎麼評量今年年初你要做的這一檔股票或股票企業從年初買到現在它的損益數字跟你一直衝來衝去的损益数字哪一个比较好哪一个比较好就做哪一个方式那以我的方式就是抱着会比冲来冲去好所以我会用抱着方式去做所以事实上来讲这样做因为我还要搭配cover call那对我来讲就更好那我会习惯这样子运作那再来就是股票期貨其實在運作的時候呢有幾個地方的部分通通要避開這個其實很多營業人可能不太了解或者第一個剛才講就是一個正價差的幅度過大的時候這個東西包含每個月轉倉都是正價差的時候這個比較不建議好像之前台積電現在台積電也是一樣就正價差幅度過大這個我就不建議去做台積電現在台積電也是一樣就正價大幅度過大這個我就不建議去做那有些人講說那如果他是比較偏短線上短線上歸短線如果是你是屬於長線上來講的話這一種來講就不是第二種方式是在於說他本身的流動性並不是太高的狀況之下股票期貨要做可以口數要降低不是说每一档股票期货都可以去运作这样去调整那这种的做法的话通常你就要去考量这个方式去运作那有时候你会看到有时候股票期货在运作的时候常常有时候在比如说现在是7月然后8月9月7、8、9月有时候几个月份不同的状况的时候价格不同你会发现说像上次我有提到那个台积电的部分你会发现光12月份它就比现股高多了几块钱到了8、9块钱的一个甚至有到多了8、9块钱这么高的一个价格那8、9块钱代表是什么它已经基本上就已经�價差是超過1%以上甚至有到1%多以上那這種狀況甚至還在那時候曾經一度高達11塊多那11塊多你去想在台積電900多塊11塊多是高達1.3%到1.4%一年等於是高了2.6% 2.7%這個就剛好講的就是整年度高的增加他快將近3%這樣不錯那這個增加在他路過的如果今天像以前我們是自營部的話这个就刚刚我讲的就是整年度高的挣价它快将近3%这样不做那这个挣价如果今天像以前我们是自营部的话因为自营部就不缺钱反正钱多只要有利益就做那这种中房来讲成本又低通常这种做法就会变成我会去空很多的那个台积电的原价外挣价它可能10来1块甚至11 12块的股票期货,11、12塊的股票期貨買同等值的股票去對鎖那對鎖我只要鎖住7月8、9、10、11、12半年鎖完之後,剩下股票要做結算價嘛對不對做結算價,因為股票是多單嘛那多單的部分就是在結算那一天呢從12點半到1點半做均價模式把它賣掉為什麼做均價模式?做均價模式之後合成起來的結算價就幾乎是一樣那直接就結算掉,這個東西來講年化利率將近3%的套利空間就出來了這種做法就適合在製銀部在做那考量稅率什麼問題那個其實就稅率就很低那基本上就不用看那這種做法的話就是在於有時候在儲權期忘記的時候有時候那個氣息的價差如果過大的時候這張股票剛好你有的時候那記得如果說它呈現利價差蠻大的那反正你不是要短線操作你也要長期持有的时候记得就当下就马上把这股票期货股票直接卖掉换成股票期货的赌它会怎么既然有溺价差这种千载难逢的机会那尤其越大越好这种代表是怎样你可以直接把你的成本降低一下像我之前就一直在提就讲说世纪刚從去年的8月1號開始股票期貨上市之後他每個月轉倉就-0.6 0.7甚至0.8 還有到0.9 莫名其妙後來去查一下原來是很多那個發那個公司債到了價內很多人因為是已經到價內是空期貨去做幣錢去對數結果把它空下來就空成逆價差結果光那幾個月平白無故送你6塊錢的幾個月平白無故送你六塊錢的幾個月累積下來送你六塊錢的利價差那你是想成本我用股票期貨這樣爆爆爆爆就這樣子爆半年多就是省下六塊錢反而成本降低了六塊錢所以你會發現說那這個時候為什麼不用股票期貨當然得用股票期貨所以概念是這樣所以很多的方式你可以去運用那像剛還有一個部分就是說像前昨天還前一往前這直播要講的那個很多做空單的部分那做空單的部分去買股票因為為什麼股票容易買那同樣反過來有沒有人去買買多單然後去放空股票的部分當然可以是這樣做但是你放空股票有些有券的問題嘛所以這些人會用股票期貨去運作來走那這個東西我要考量是期貨的那個正例價差的差別跟股票期貨的差別像如果現在期貨跟股票期貨都有一點呈現是小多的正價差那這種狀況呢如果今天股票期貨這裡像如果現在像期貨跟股票期貨都有一點呈現是小多的正價差那這種狀況呢如果今天股票期貨它的正價像台積電這些權值股的正價差都還蠻大都保持在1% 2%以上甚至2%多以上的話那指數期貨它是平價差甚至是逆價差的狀態之下那我可不可以反過來做空這些一攬子的全值股的那个那个所谓的股票期货然后去买加钱指数也可以啊但是你要留意一下就是因为你空这一篮子的加钱那个全值股的一个股票期货的时候你会漏掉是一块就是我们昨天在讲说相对性有些比较平常不动的这些全值这些股票的时候那这些股票你相对性是没有空到它变成是用期货来讲买期货是买到它那这些股票会不会是强弱就会影响到你的这个操作策略的运作所以像这种做法相对性比较不多的原因是在于说因为这个东西一般来讲的话我们通常是买股票是买强势的所以通常来讲为什么这种做法跟前两天在直播在讲的是说我买我空一篮子的期货但是买一篮子空期货买一篮子的股票的这种机会比较多反而反向过来的就比较少因为他比较不符合效益而且相对性的效益是比较差所以这个原因來講是說這個做因為以前在製銀什麼東西都做什麼東西要搞搞出來發現說這個真的真的有可行性比較多這可行性比較小然後後來有些東西做一做甚至被電到電電到就覺得莫名其妙幹嘛做這個白癡的動作但是常常就是遇到這種問題所以也就是說股票局位的運作方式大概就是這幾種方式大家去思考呃不是說哎常常很多音樂到这种问题所以也就是说股票局位的运作方式大概就是这几种方式大家去思考不是说常常很多营业人跟你讲说这两张股票两张股票对我也知道是两张股票但是什么时机点该出手什么时机点不该出手那这种有时候这种做法的话如果你是报长期的话基本上就是还是一样我们这个产业基本面不变那你如果是要短线上的话那技术分析或筹码不变那你如果是要短线上的话那技术分析或筹码分析这当然一样那短线上的话如果你的量不大股票期货的量也够大的话那适合就是用股票期货去运作cover去做的那你如果是长期来讲的话就是除非你要你如果要资金才去运用的话股票期货也可以运用如果都不考量的话那正常其实还是建议是不是在股票里面去运作如果都不考量的話那正常其實還是建議是不是在股票裡面去運作那這樣子會比較好一點大概就是股票期貨運用的方式大概是幾種然後我們現在來找一下幾個 qa 的問題就是剛才講說第一步怎麼踏出去那就是你找出長期的你想要做的持股那有兩張的就把兩張股票賣掉直接換股票期貨一换股票取回一口就接下来就是每个月转账这样就试着做就好七月份的扣有异常就是因为昨天行情其实在跌的时候甚至在平板的时候你看七月份的扣我有截图在那一张在粉专上面大概是在价外的1200点也就是说23300点以上的这些选择权出现了问题出现了既然是指数在下跌CALL的权利金居然在上涨这个就不符合常规那我要讲一下CALL在上涨是代表很多买房是有些买房进场但是不是看多我要给大家的是不一定为什么不一定因为它有可能是相对性是做什么动作空期货Buy Call在对它有些人可能是有些人玩Gamma的有些人可能在玩什么其他策略的这些东西的做法可能会有这样甚至像之前像以前有个朋友叫D神一个同事那像他做法可能會有這樣甚至像之前像有以前有個朋友叫D神有個同事那現在他的做法就是選擇權買兩邊買很多買個幾千口那幾千口之後剩下保證金幹嘛如果瞬間殺個1000點他開始反向的買期貨多單為什麼因為他的put已經買好了在那邊可能交換1000點那你疊1000點就開始買期貨那再疊?因為他的PUT已經買好了可能交換1000點嘛,那你跌1000點就開始買期貨,那再跌1200點,再買,1300點再買,他會買的期貨的量跟那個BUYPUT的量基本上是互抵調可能就是互抵調,所以往下跌的風險會不會有出現很大功能不會,但是他在賺什麼?如果今天期貨反彈三五百點三五百點他賺掉平常之後光這個期貨賺的錢比他買兩邊的權利金更好那代表他那一次的合約基本上一定穩贏的那如果有彈起來更多,這賺更多那彈起來之後,又再來刷洗一次重的話,那他會賺更多但是這種做法相對性就是要賭要抓出那個會有大行情的波動的時候他才會去做那像什麼時候狀況裡面比如說哎股票股票市場的現股市場爆大量或是有一些所謂的漲跌的漲跌幅的一些所謂的指標的一些股數哎加公司加速的變化比較異常的時候這些幾個幾幾個指標過濾下來發覺統計下來一些股数加公司加速的变化比较异常的时候这些几个几几个指标过滤下发掘统计下来他的相对性接下来行情波动大的时候像这种策略就可以出手但是你要记得这种策略出手的时候通常如果行情不动的不太动的话他光一天以他部位来讲他之前就吃饭的时候在我几年前吃饭的时候在分享说如果指数如果都不太动的话一天光权利金损失消耗就是几百万那这样子你敢不敢扛这样的问题我这种概念一下我那个刚才讲Cold的异常就是这样子那代表很多人在买但是很多人在买的话权利金很高所以昨天的我的方式就是那你在买权利金高很多吗那我的持股也沒有什麼沒有想動的時候那我本來就cover call那你既然漲高了就當然是賣就賣一些嘛所以昨天當然賣了一些call就往上去賣就是加外1200點以上的call就賣一些為什麼當做如果沒有上去當做配息就權利金補償沒有漲上去當做我的補償ok 啊你沒問題啊反正月反正就是扣除周的月我還可以賣多多多賣一賣那接下來W1的合約開始要做了我也會持續在賣扣那會用籌碼表位置去看他的位置點在哪,就像我們今天在講說為什麼看23000點差點壓力因為之前前面累積的幾天的籌碼量23000點本來压力因为之前前面累积的几天的筹码量23000点本来就卖方就比较重加上昨天的夜盘23000也是卖方今天有没有突破23000点有啊可是你会发觉23000点的call从头到尾始终都是站在卖方这个是之前有跟我讲说就算你要做多做多到这个位置關卡點他都沒有測的時候至少我要尊重一下那所以你後面今天壓下來其實並沒有什麼我是覺得就很正常所以我今天的做法來講的話就是做他這個區間就是23000點做一個最高點的然後這樣下來後來慢慢這個區間有慢慢的往那個22950點的位置去移嘛所以大概就在这个中间其实差不多所以到最后这个部位其实小赚一点钱其实很正常的概念的方式好那再来就是手续费很低对股票期货是足额保证金做这其实很适合但是有超过太多的你只要留一半就好了比如两张股票是60万你放30万保证金30万就放银行成立息,真的不要放保证金里面没有任何意义C扣远价外吃下来完整的上涨空间可以抵消正价吗对,没错,就像台积电来讲的话刚才讲说每个月转2块多吧,你如果用股票期会去做的话那你会试想一下,比如说1050元的扣或是1030元的扣就是距离我还要涨个7、80块�或是1030元的扣就是距離我還要漲個七八十塊錢或是漲10%的空間的位置點這個扣的權利金大概可以抵掉你的正加差的部分差不多有這樣的幅度可以去運作這個東西可以做但是它的缺點是那如果漲10%以後我就賺不到這個概念是這樣所以你要放那如果是這樣的方式的話我寧可是說我抓15%那抓15%的方式的話增加他的幅度會比那個扣的權利金還要多一點嗎多一點沒有關係為什麼因為我省下來資金就該我可以資金當下來的時候我可以用台企運用的時候這個可以做所以遠遠價外的扣可以來抵消增加成本是可以但是要強調你主要是要賺上漲波本是可以但是要强调你主要是要赚上涨波段的钱不是要赚权利金的钱所以波段的这个空间是主要考量的原因权利金是次要的考量原因期货建议凹单吗如果我是长期要投资要转仓我是转仓那个就不是要凹单了我是长期要放的至于凹单你房企长期要放的至于凹单你如果觉得你的买进这档股票的呃原油都不见的话那当然是砍掉如果都不在了那当然是就砍掉那就不会去凹了那如果这个你买进的这档股票的理由都还存在那当然这是我就会继续放对呀期货最大的风险是怎样怎么让自己毕业哦期货最大的风险是怎样怎么会让自己毕业期货最大的风险就是杠杆开太大比如刚才讲我说60万60万两张股票60万丢进来那你买一口差不多花多少钱10万块左右而已那10万块左右我刚才讲说为什么放30万那30万放怎样你这样放保证金里面30万你如果看到我还20萬我可不可以下第2口,可以耶那可不可以放第3口,也可以耶那外面的30萬如果你沒有移出去30萬你可不可以放6口那也沒有問題,問題出現一個你如果放到6口槓桿倍數是開6倍以上那問題來了,下跌10%你就等於是賠60%你可不可以下7口,也可以下到7口你下7口之後,如果下跌10%是賠70%兩根跌停板再見了可是剛才我們來講是說我用30萬來做一口然後另外30萬放銀行兩根跌停板是多少錢兩根跌停板60萬,兩根跌停板12萬,你30萬扣掉12萬,你還18萬保證金都還不用補這個東西為什麼就這樣子期貨的風險在哪邊?就是貪婪不知道你自己要畢業為什麼要畢業因為你把部位的風險槓桿倍數開太大那你為了要避免這個畢業的方式你要怎麼去控制?你去買的這個期貨合約要用合約值來算不要用保证金就像我你如果买一口小台子你现在是算两万三千点好了代表现在是代表一百一十五万的合约值所以你买一口小台子代表你就要心理准备我是用我在买一百一十五万的股票那么多我用保证金六万可不可以做可以啊现在保证金六万块做一口问题是你要想着你用六万可不可以做可以啊现在保证金六万块做一口问题是你要想着你用六万块是在做一百一十五万这个杠杆倍数会不会太大会那你如果不想太大如果只是开两倍一百一十五万除以二多少你好歹也要准备个五十几万吧对不对那你如果就算开个三倍你至少放个四十万差不多概念这样子这样子来讲才可以避免你会被畢業的一個現象那40萬放裡面保證6萬還有34萬要幹嘛你不用放那麼多,放個20萬,20萬放銀行去存利息這樣子那你有剩下其實剩下之間再做cover call其實都綽綽有餘了這概念去做這樣才不會讓你去畢業,長期有個股期可持有個股期可以持有個股期可以我現在持有廣島一年多廣島持有一年多啦世紀剛現在快去年八九十十一十二一二三四五六也存了十個月啦這概念可以持只要他的正價差年度這個年化的每個月每個月轉倉的成本合計起來這個正價差的利率低於2.5%以下的以現在的利率來算,低於2.5%以下的我還是會用股票期貨長期持有,就每個月轉倉轉倉轉倉反正,這樣子狀況可以金融股用股票期貨來做槓桿配置合適嗎?正加差的耗損,對啊所以正加差的耗損,就剛才講的啊,就是你要考慮它一整�加差耗損對啊所以增加的耗損就剛才講的啊就是你要考慮他一整年度的耗損多少我現在講的2.5%以內你目前來看的話我是可以接受我最大容忍範圍就到3%啊如果超過3%的話我就不要那你的耗損就剛才講的我們要講的那個耗損增加差耗損我可不可以用很遠價外的那個CALL來補補一些我是覺得這種方式是可以运用的好不好这样可以运用的方式怎么样图表,筹码怎么样图表化图表化这个东西要问Excel的高手,基本上我有看过几个网友,应该好多,我已经看过好几个版本有写出来,但是我不会用我只是用别人写出来版本而已这个可能要问Excel的高手才可以選擇成代表可以我只要有選擇籌碼不就可以代表我可以了可以啊但是我要記得選擇全籌碼表的部分只限當天的運用會有效你要長期運用的話一天一天做但是一天做一支累積起來去算的話這個參考的比重是當盤這個籌碼表的參考比重可能會70-80%以上剩下的期間前面幾天的累積可能會參考就是20-30%的概念而已那所以像今天來講籌碼表很明確跟你講說23000點就是SEKO部分就是打死部隊後面越賣越多代表23000點過不去再來22950點的PUT今天神經一度現股就是跌破到了22900點的時候你會發現22950的SELLPUT口數也擋在那就以當盤來講這個數據以當盤來講參考的比重很高但是有甚至有到七八成但是你如果要長期去運作的話你就要考慮前面幾天甚至更久以前的成但是你如果要长期去运作的话你就要考虑前面几天甚至更久以前的成本你要把它给回推回来那一般来讲我会只抓一周一周再抓前面大概三五天的一个数据这样累积去看所以像今天来讲的话就很明确的是大概就两万三上下左右概念这样因为后来的调整上来的筹码反正就是昨天夜盘跟白天在做Self的概念这个底�調把它底調下面的支撐力有往上移但是上面的壓力並沒有往上調太多所以的概念就是說以當盤跟前幾天累積這個可以做但是記得以當盤的百分之七八十為主剩下兩三成你如果要流暢跟他去玩這個部分這個比重盡量一定要把它壓到原本的部位如果你可以做十口,頂多留個一兩口兩三口的概念就是了不起就這樣子的方式去做你不能說真的所有的部位都流暢跟他去玩這樣會被咬到假說期貨在78912都正價差正價差不多的數值不多某期貨78912月都正價差差不多增加差不多的數值不多某期或7、8、9、12月都增加差差不多的增加差數值差不多數值差不多,如果是長期要持有的話是每個月的增加差都差不多嗎那就是我剛才講的換算每個月轉倉的成本這樣累積下一整年有沒有超過那個百分比就2.5%以下分比就2.5%以下OK啦,2.5%以上就是看你要不要轉那3%以上我就不考慮,就換成現貨這個概念是做的,你就是每個月加差轉倉算去起交所調高保證金的意義是什麼因為就是他一直要控制槓桿倍數在20倍上下左右指數上漲自然保證金已經會調對,那指數下來保證金可能是他槓桿倍數在20倍上下左右指數上漲自然保證金已經會調對那指數下來保證金可能是他槓桿倍數降到87倍之後可能降到15 16倍的時候他又會把它提高那個保證金就會降下來讓槓桿倍數再回去這個只是純粹是那個槓桿倍數的調整的起角度的部分流動性個股個股選因為流動性是否能交易的標的也不多當然了個股選可以做的部分的話目前只有少數的幾檔而已像台積電其實就很少聯電有些有些然後也金融股這些如果真的各股選其實很少這量真的太少所以相對不建議大家再去做這個動作像除非像台積電CPU我們這做法我們就是掛架等掛架等掛架等而已有就有中沒中就拉倒那通常來講的話有時候我們自己要做稍微量比較大一點的時候通常會做一個巡架跟去跟起腳手做巡架可是這不是一般人會去做的東西所以呃這樣做然後簡單的雙哎教學雙C嗎好比如現在來講我講一下今天兩個籌碼的數據今天盤中這樣一路往上找的時候其實你會發覺說23000點是關卡就是Cellcode的位置那我們之前講嘛我的Cellcode通常會擋在那邊你23000點是壓力我就退一檔或兩檔就是23050或者是23100我去做Cellcode同樣的今天在夜盤開出來的時候退一檔或兩檔就是23050或者是23100我去做C-code同樣的今天在夜盤開出來的時候夜盤的數據是22800點的PUT它是賣方式相對就開始出現1000多個比較多的那一樣我要退嘛就22750 22700像這種來講就是雙C就是C就是會C到這個地方兩邊都各C一口在這邊就是Code的大量的退一檔到兩檔,就是50到100點Put大量的50點到100點的地方去做雙C,我的習慣性是這樣那這個雙C來講的話,就在就是看著盤中的籌碼表它的數值有沒有變化如果它是往多方調5000口上去那我的部分就是Put或是Code的部分再往上去那我的部分就是put或是call的部分再往上去調一組,比如說我是賣23100那變成23150再賣一口往上調那put的部分就會往上調100點比如說22700會變成調22800就是這樣子慢慢去做壓縮其實就是雙c的壓縮一直往上隨著往上去調但是這種調法,你會記得這種調法的話就是貼著盤面去調,那這種調法其實我是覺得很累因為我不喜歡這樣調一調去所以像這種調法,像以往在以前像在置頂部,為什麼一天可以做17萬個一直調一調,一直調一調行情一直做上下上下調調到後面前面幾分鐘要做什麼單根本就不知道做下來會不會真的比較好我覺得不一定耶真的是隨著這樣行情走有時候真的好有時候不好可是真的花費太多時間我一直在想其實希望的是大家學的是把時間空下來去研究好的策略研究好的公司跟產業去做這樣其實會比較好從賣場支撐壓力口數大於多少就會像以今天來講的話超過3000口就很明顯了就不會過了你像23000點你去看一下3000口的code累積到3000口的量大概是在12點多就已經累積出來了那接下來是22950的PUT到結算的時候一點左右的時候那時候還有幾點的權利金結果你會發覺他已經到了3000口那大概也不用看了那個大概也不會跌下去那個時候雖然雖然殘留價值3.5點你這樣照賣反正那個都會歸零就收一收這種做法基本上單一鋁業家超過2000口以上就可以稍微出手3000口以上大概就壓力就很大四五千口就不用看了很難去撼動的概念除非是我之前講你的對應出來另外一邊的口數更大其實你要看支撐壓力我是覺得不用去看支撐壓力之前就我跟我講就是畫圖嘛你如果這個數字看不出來就畫圖那個圖形會很明顯就像現在圖形就一樣很明顯就是23000點本來是一個高峰一個大蠻頭這個形狀或者是大蠻頭是往22950稍微往下移一點就是這個很明顯就這個區間你這個區間畫出來雙C我就C在這個它會凹下去賠錢的地方這兩邊我就直接賣一賣就好了所以這個支撐壓力就這樣就出來就直接做這兩邊就可以了當盤的選擇權籌碼表啊這個就是看那個籌碼表那個我的YT上面有啊就直接用這個方式直接你自己去做一張之後你自己圖形就會出現了好不好對啊上面直接有 ok好今天的 Q&A 也問到這還有沒有其他問題有問題就盡量問對啊然後如果各位其實有做股票取貨或是呃有興有做股票取貨或是呃有興趣做股票取貨或是做cover code類似像這種東西其實就歡迎就隨時然後我們接下來群組的直播是在7月3號晚上的7點然後接下來就是7月16號會有一堂就是也是免費線上的大家就是跟着讲接下来下半年的产业选股的趋势方向跟所谓的微型台值要上市微型台值上市之后再做避险的调整会更精细会更好因为现在台值小台值一口就100多万问题是我这上午10万我要做这种避险这个做法会去调所以这个比例上来讲的话到时候微型台子跟選擇權搭配運用的方式會有更多元化那我們7月16號會有一些線上的課程線上的講座來再做講這一塊然後個股期一般我看一下個股期一般是比較推薦每個月轉賬嗎如果有如果剛才講的我是比較傾向每個月因為我要流動性的問題如果遠月份的我是比较倾向每个月因为我要流动性的问题如果远月份的总计的价差比每个月的转仓还便宜直接买远月份如果你要做长期的话这样子做就好这样省你的交易成本交易次数这样成本可以降低就是怎么做成本最低就做哪个方法那你如果不是考虮流動性的問題的話那就是哪一邊最便宜就是遠月份直接遠月份比如說增加他5塊錢可是我每個月轉倉只只有1.2那1.2的話6個月長相7.7塊多7塊多比5塊錢高那我當然直接買直接買遠月份增加他5塊錢就好不要每個月去转还浪费多浪费两块钱懂吗就这样的观念是做呃直播流浪我会放在那个youtube上面的群组群组就之前讲有一些条件呢1000人群组不是不是不是500人的群组对啊呃基本面会参考什么方法本益比现金流不会我会考虑这个可能比较少接触听我来讲我一直是看的是产业趋势我不再看本益比跟现金流什么折线法我只看现在这个时间点往后两三年去看哪些产业它的产值产量会比现在好就买那些如果你相信只是AI产业会越来越好那你就相信台积電在未來兩三年它晶圓的代工的產值會更高獲利會更強那我就買它如果沒有就不買它那就像我看能源類台灣缺電兩三年後需要的電力更大這個東西來講的話綠色電力要更大那我就會買如果你覺得不需要綠色電力那麼大那就不买它甚至放红它多有可能概念是这样不是看本益比的现金流而是看产业趋势从现在这个时间点看往后试着站在往后两年的那个时间点回过头来看现在有什么东西在两三年后是很需要而且量现在是缺太多比较多的就买这个产业世纪钢的护城世纪钢的护城有啊就几个第一个他的所谓的钢购的那个所谓的成本整个成本来讲的话比韩国的SK集团的成本低了那再来就是就是它的護城就是那個海上的作業平台船的成本這個東西在國內來講的話平均一天就是40萬歐元1000萬台幣左右這個東西來講的話你從韓國過來這成本就更高所以韓國已經不用考慮它進不進真的問題了再來國內有沒有競爭對手要進來有進來可以啊我就之前司機剛的他的優勢是在誰有港口嘛而且要省水港那你是想誰有港口誰拿天否則你要做這港口要多這麼大一座要組裝廠房要那個24小時可以做的那個這種廠房台灣誰來如果有那就代表競爭像24小時可以做的這種廠房,台灣誰來如果有,那就代表競爭沒有,我幹嘛去看這方式關係就這樣子所以它的庫存額其實有蠻多的所以基本上這樣子然後軍工股的看法軍工股,我覺得是一陣一陣很多可能就是我反而覺得就是以AI加能源這兩組其實能源也是跟AI相關所以AI在這企業的話能源類一定要用很多所以像上次我6月從6月14號後來最後面加碼就是換掉股票剩下的資金就是換成世紀豐田跟鴻德能源那個世紀豐電跟宏德能源嘛那世紀豐電就套了就是那時候加碼的部分賠錢嘛,到今天是賠錢那宏德能源賺到一根多的漲幅那今天208塊那時候才173塊半賺了快兩根就一樣嘛比一比比世紀豐電好嘛整體上來講還算不錯啦還可以的對啊隱藏股萬達寵物對啊,萬達寵物比一比,比四季風電好嗎?整體上來講還算不錯啦,還可以的對啊隱藏骨萬達寵物,對啊,萬達寵物,這個就是之前有講的隱藏骨為什麼現在人數在人越來越少,而且講來聽來是生小孩真的很耗成本更耗精神我真的是養小孩養條狗跟貓還不一樣,可是你會發現養狗養貓那個一樣所以這個產業就是一樣為什麼找萬達寵物你發覺說未來趨勢一樣現在年輕人不生小孩不養小孩就是養寵物養寵物這個市場其實還會再成長就像我們年紀大的一樣如果小孩子都出去了年紀大也可能養寵物這概念一樣我也想說這產業�趋势嘛你从过几年回头看什么东西会可能会成长的机会很高就买这个东西而已嘛所以问拿宠物也是这样看法对啊新贵股票是不是会考虑新贵股票我不管我不是考虑新贵或不是新贵或是创业版我只考虑未来这个产业会不会成长会成长管你是不是在现在就算未上市人買我也買對呀重電看法出現的問題是產能重電有些屬於是人才的一些的部分不夠所以不是產能的問題所以重電其實他已經到了某個高原期的一個狀態所以配置是會配置一點但是不會配置他不會是主我的不會是我的主角啦,我這樣可以看狗狗嗎?我沒有養狗狗大概就這樣子,還有嗎?創新版的操作除了200萬限制都跟股票一樣的錯誤方式,對啊沒錯就是一開始,那時候得要創新版有財力限制的證明說持股庫存要超過200萬以上後來去用就有了AI這陣子的IP點IP不一定跟AI有相關IP是客製化晶片不代表完全是AI但是客製化晶片有它的市場我是覺得還是可以可以長期性還是不錯AI醫療建議嗎我覺得有機會對呀這個東西AIT但是國內我不曉得之前有人看說長家智能但我不曉得這是東西真的能實現的機會有多大未來這個東西我才會覺得去看那生技股這個我比較弱所以我比較不敢去判斷現在操作股票還會停損嗎我現在操作股票不會停損但是我會加重避險的模式去運作概念是這樣子籌碼課的教學嗎巡迴已經講完了如果大家各件事如果你們自己有人有場地有那個的話就安排你們有場地的話接下來就是跟我們講我們就直接去講就好了創意怎麼看就一樣創意事情都一樣長期基本上還是有它的市場只是這個比重它算是主菜是 AI 跟能源嗎這些都是配菜就配菜一點點那配菜一點點就是當小菜點心都放著這樣概念而已對啊崇瑪克就是主要還是台北啦你如果三五個人大家有約一約直接約個時間出來就直接講一下就可以他外縣市的我們就沒有辦法就是你要有你人約可是我們真的要跑一趟這會比較麻煩大概就這樣還有沒有其他問題沒有的話這樣快一個小時等一下要跟老婆去運動一下不然肚子太胖了那我們今天就先直播到這那如果大家如果還有什麼問題歡迎留言到那個粉專或是YT留言給我們我們看到的話我們再一直回覆給大家好不好對啊啊群組條件有興趣的話就是FB私訊給我們留下你的line id給我們我們再再幫你聯絡好不好好謝謝好拜拜我们再帮你联络好不好好谢谢拜拜

https://www.youtube.com/watch?v=lXLBTBBil2U
Jensen Huang, Founder and CEO of NVIDIA
  Jensen, this is such an honor. Thank you for being here. I'm delighted to be here, thank you. In honor of your return to Stanford, I decided we'd start talking about the time when you first left. You joined LSI Logic, and that was one of the most exciting companies at the time. You're building a phenomenal reputation with some of the biggest names in tech, and yet you decide to leave to become a founder. What motivated you? Chris and Curtis. Chris and Curtis, I was an engineer at LSI Logic, and Chris and Curtis were at Sun. And I was working with some of the brightest minds in computer science at the time, of all time, including Andy Bechtolsheim and others building workstations and graphics workstations and so on and so forth. And Chris and Curtis said one day that they like to leave Sun and they like me to go figure out what they're gonna go leave for. And I had a great job, but they insisted that I figure out with them how to build a company. And so we hung out at Denny's whenever they dropped by, which was, by the way, my alma mater, my first company. My first job before CEO was a dishwasher, and I did that very well. And so anyways, we got together, and it was during the microprocessor revolution. This is 1993. And in 1992, when we were getting together, the PC revolution was just getting going. You know that Windows 95, obviously, which is the revolutionary version of Windows, didn't even come to the market yet. And Pentium wasn't even announced yet. And this is all right before the PC revolution. And it was pretty clear that the microprocessor was going to be very important. We thought, why don't we build a company to go solve problems that a normal computer that is powered by general purpose computing can't? That became the company's mission, to go build a computer, the type of computers, and solve problems that normal computers can't. And to this day, we're focused on that. And if you look at all the problems that in the markets that we opened up as a result, it's things like computational drug design, weather simulation, materials design. These are all things that we're really, really proud of. Robotics, self-driving cars, autonomous software we call artificial intelligence. And then, of course, we drove the technology so hard that eventually the computational cost went to approximately zero and then enabled a whole new way of developing software where the computer wrote the software itself, artificial intelligence as we know it today. So that was it. That was the journey. Yeah. Thank you all for coming. Well, these applications are on all of our minds today. But back then, the CEO of LSI Logic convinced his biggest investor, Don Valentine, to meet with you. He is obviously the founder of Sequoia. Now, I can see a lot of founders here edging forward in anticipation. but how did you convince the most sought-after investor in Silicon Valley to invest in a team of first-time founders building a new product for a market that doesn't even exist? I didn't know how to write a business plan. And so I went to a bookstore. And back then there were bookstores. And in the business book section there was this book and it was written by somebody I knew Gordon Bell and this book I should go find it again but it's a very large book and the book says how to write a business plan and and that was you know highly specific title for a very niche market and it seems like he wrote it for like 14 people and I was one of them. And so I bought the book. I should have known right away that that was a bad idea because Gordon is super, super smart. And super smart people have a lot to say. I'm pretty sure Gordon wants to teach me how to write a business plan completely. And so I picked up this book. It's like 450 pages long. Well, I never got through it. Not even close. I flipped through it a few pages and I go, you know what, by the time I'm done reading this thing, I'll be out of business. I'll be out of money. And Lori and I only had about six months in the bank. We had already Spencer and Madison and a dog. So the five of us had to live off of whatever money we had in the bank. And so I didn't have much time. And so instead of writing the business plan, I just went to talk to Wilf Corrigan. He called me one day and said, hey, you left the company. You didn't even tell me what you were doing. I want you to come back and explain it to me. And so I went back and I explained it to Wilf. And Wilf, at the end of it, he said, I have no idea what you said. And that's one of the worst elevator pitches I've ever heard. And And that's one of the worst elevator pitches I've ever heard. And then he picked up the phone, and he called Don Valentine. And he called Don, and he says, Don, I'm going to send a kid over. I want you to give him money. He's one of the best employees LSI Logic ever had. And so the thing I learned is you can make up a great interview. You could even have a bad interview. But you can't run away from your past. And so have a good past. Try to have a good past. And in a lot of ways, I was serious when I said I was a good dishwasher. I was probably Denny's' best dishwasher. I planned my work, I was organized, I was me some plus, and then I washed the living daylights out of the dishes. And then they promoted me to bus, I was certain I'm the best busboy Denny's ever had. I never left the station empty-handed. I never came back empty-handed. I was very efficient. And so, anyways, eventually I became a CEO. I'm still working on being a good CEO. You talk about being the best. You needed to be the best among 89 other companies that were funded after you to build the same thing. And then with 69 months of runway left, you realized that the initial vision was just not gonna work. How did you decide what to do next to save the company when the cards were so stacked against you? Well, we started this company called for Accelerated Computing. And the question is, what is it for? What's the killer app? And that came our first great decision. And this is what Sequoia funded. The first great decision was the first killer app was going to be 3D graphics. And the technology was going to be 3D graphics. And the technology was going to be 3D graphics. And the application was going to be video games. At the time, 3D graphics was impossible to make cheap. It was million-dollar image generators from Silicon Graphics. And so it was a million dollars, and it's hard to make cheap. And the video game market was $0 billion. So you have this incredible technology that's hard to commoditize and commercialize, and then you have this market that doesn't exist. That intersection was the founding of our company. And I still remember when Don, at the end of my presentation, Don was still kind of, he said, one of the things he said to me, which made a lot of sense back then, makes a lot of sense today, he says, startups don't invest in startups, or startups don't partner with startups. And his point is that in order for NVIDIA to succeed, we needed another startup to succeed. And that other startup was Electronic Arts. And then on the way out, he reminded me that Electronic Arts' CTO is 14 years old and had to be driven to work by his mom. And he just wanted to remind me that that's who I'm relying on. And then after that, he said, if you lose my money, I'll kill you. And that was kind of my memories of that first meeting. But nonetheless, we created something. We went on the next several years to go create the market, to create the gaming market for PCs. And it took a long time to do so, we're still doing it today. We realized that not only do you have to create the technology and invent a new way of doing computer graphics so that what was a million dollars is now, you know, three, four hundred, five hundred dollars that fits in a computer. And you have to go create this new market. So we have to create technology, create markets. The idea that a company would create technology, create markets defines NVIDIA today. Almost everything we do, we create technology, we create markets. That's the reason why people say we have a, you know, people call it a stack, an ecosystem, words like that, but that's basically it. At the core, for 30 years, what NVIDIA realized we had to do is in order to create the conditions by which somebody could buy our products, we had to go invent this new market. And it's the reason why we were early in autonomous driving, it was the reason why we were early in deep learning, it was the reason why we were early in just about all these things, including computational drug design and discovery. All these different areas we're trying to create the market while we're creating the technology. And so that's, okay, and then we got going, and then Microsoft introduced a standard called Direct3D. And that spawned off hundreds of companies. And we found ourselves a couple years later competing with just about everybody. And the thing that we invented the company, the technology we invented 3D graphics with, the consumerized 3D with, turns out to be incompatible with Direct3D. So we started this company, we had this 3D graphics thing, million dollar thing, we're trying to make it consumerized 3D with, turns out to be incompatible with Direct3D. So we started this company, we had this 3D graphics thing, a million dollar thing, we're trying to make it consumerized. And so we invented all this technology. And then shortly after, it became incompatible. And so we had to reset the company or go out of business. But we didn't know how to build it the way that Microsoft had defined it. And I remember a meeting on a weekend and the conversation was, we now have 89 competitors. I understand that the way we do it is not right, but we don't know how to do it the right way. And thankfully, there was another bookstore. And the bookstore is called Fry's, Fry's Electronics. I don't know if it's still here. And so I think I drove Madison, my daughter, on the weekend to Fry's, and it was sitting right there. I think I drove Madison, my daughter, on a weekend to Fry's, and it was sitting right there, the OpenGL manual, which would define how Silicon Graphics did computer graphics. And so it was right there, it was like $68 a book. And so I had a couple hundred dollars, I bought three books. I took it back to the office and I said, guys, I found it, our future. And I handed out, I had three books. I took it back to the office and I said, guys, I found it, our future. And I handed out, I had three versions of it, I handed it out, had a big nice centerfold. You know, the centerfold is the OpenGL pipeline, which is the computer graphics pipeline. And I handed it to the same geniuses that I founded the company with. And we implemented the OpenGL pipeline like nobody had ever implemented the OpenGL pipeline, and we built something the world had never seen. And so a lot of lessons are right there. That moment in time for our company gave us so much confidence. And the reason for that is you can succeed in doing something, inventing a future, even if you are not informed about it at all. And it's kind of my attitude about everything now. When somebody tells me about something and I've never heard of it before, or if I've heard of it and never, don't understand how it works at all, my first thought is always, you know, how hard can it be? And it's probably just a textbook away, you know, you're probably one archive paper away from figuring this out. And so I spent a lot of time reading archive papers, and it's true, it's true. You can Now, of course, you can't learn how somebody else does something and do it exactly the same way and hope to have a different outcome. But you could learn how something can be done and then go back to first principles and ask yourself, given the conditions today, given my motivation, given the instruments, the tools, given how things have changed, how would I redo this? How would I reinvent this whole thing? How would I design it? How would I build a car today? Would I build it incrementally from 1950s and 1900s? How would I build a computer today? How would I write software today? Does it make sense? And so I go back to first principles all the time, even in the company today, and just reset ourselves. Because the world has changed. And the way we wrote software in the past was monolithic, and it's designed for supercomputers, but now it's disaggregated, so on and so forth. And how we think about software today, how we think about computers today, just always cause your company, always cause yourself to go back to first principles. And it creates lots and lots of opportunities. The way you applied this technology turns to be revolutionary. You get all the momentum that you need to IPO and then some more because you grow your revenue nine times in the next four years. But in the middle of all of this success, you decide to pivot a little bit the focus of innovation happening at NVIDIA based on a phone call you have with this chemistry professor. Can you tell us about that phone call and how you connected the dots from what you heard to where you went? Remember, at the core, the company was pioneering a new way of doing computing. Computer graphics was the first application. But we always knew that there would be other applications. And so image processing came, particle physics came, fluids came, so on and so forth. All kinds of interesting things that we wanted to do. We made the processor more programmable so that we could express more algorithms, if you will. And then one day we invented programmable shaders, which made all forms of imaging and computer graphics programmable. That was a great breakthrough, so we invented that. On top of that, we invented, we tried to look for ways to express more sophisticated algorithms that could be computed on our processor, which is very different than a CPU. And so we created this thing called CG. I think it was 2003 or so. C for GPUs. It predated CUDA by about three years. The same person who wrote the textbook that saved the company, Mark Kilgard, wrote that textbook. And so CG was super cool. We wrote textbooks about it. We started teaching people how to use it. We developed tools and such. And then several researchers discovered it. Many of the researchers here, students here at Stanford was using it. Many of the engineers that then became engineers at Nvidia were playing with it. A doctor, a couple of doctors at Mass General picked it up and used it for CT reconstruction. So I flew out and saw them and said, what are you guys doing with this thing? And they told me about that. And then a computational, a quantum chemist used it to express his algorithms. And so I realized that there's some evidence that people might want to use this. And it gave us incrementally more confidence that we ought to go do this, that this field, this form of computing could solve problems that normal computers really can't and reinforced our belief and kept us going. Every time you heard something new, you really savored that surprise. And that seems to be a theme throughout your leadership at NVIDIA. It feels like you make these bets so far in advance of technology inflections that when the apple finally falls from the tree, you're standing right there in your black leather jacket waiting to catch it. How do you find the conviction? Always seems like a diving catch. Always does seem like a diving catch. You do things based on core beliefs. You know, we deeply believe that we could create a computer that solves problems normal processing can't do. That there are limits to what a CPU can do. There are limits to what general purpose computing can do. And then there are interesting problems that we can go solve. The question is always, are those interesting problems only or can they also be interesting markets? Because if they're not interesting markets, it's not sustainable. And NVIDIA went through about a decade where we were investing in this future, and the markets didn't exist. There was only one market at the time. It was computer graphics. For 10, 15 years, the markets that fuels NVIDIA today just didn't exist. And so how do you continue with all of the people around you, our company, NVIDIA's management team and all of the amazing engineers that are there creating this future with me, all of your shareholders, your board of directors, all your partners, you're taking everybody with you, and there's no evidence of a market. That is really, really challenging. The fact that the technology can solve problems and the fact that you have research papers that are used that are made possible because of it are interesting, but you're always looking for that market. But nonetheless, before a market exists, you still need early indicators of future success. You know, we have this phrase in the company, is, you know, there's a phrase called key performance indicators. Unfortunately, KPIs are hard to understand. I find KPIs hard to understand. What's a good KPI? You know, a lot of people, you know, when we look for KPIs, you go gross margins. That's not a KPI, that's a result. You know, you're looking for something that's an early indicators of future positive results, okay? And as early as possible. And the reason for that is because you want early want that early sign that you're going in the right direction. And so we have this phrase, it's called EOFS, you know, early indicators, E-I-O-F-S, early indicators of future success. And it helps people, because I was using it all the time, to give the company hope that, hey, look, we solved this problem, we solved that problem, we solved this problem. The markets didn't exist, but they were important problems. And that's what the company is about, to solve these problems. We want to be sustainable, and therefore the markets have to exist at some point. But you want to decouple the result from evidence that you're doing the right thing. Okay, and so that's how you kind of solve this problem of investing into something that's very, very far away. And having the conviction to stay on the road is to find as early as possible the indicators that you're doing the right things. And so start with a core belief, unless something changes your mind, you continue to believe in it. And look for early indicators of future success. What are some of those early indicators that have been used by product teams at NVIDIA? All kinds. have been used by product teams at NVIDIA? All kinds. I saw a paper. Long before I saw the paper, I met some people that needed my help on this thing called deep learning. At the time, I didn't even know what deep learning was. And they needed us to create a domain-specific language so that all of their algorithms could be expressed easily on our processors. And we created this thing called KUDNN, and it's essentially the SQL, SQL is in storage computing, this is neural network computing, and we created a language, if you will, domain-specific language for that. Kind of like the OpenGL of deep learning. And so they needed us to do that so that they could express their mathematics. And they didn't understand CUDA, but they understood their deep learning. And so we created this thing in the middle for them. And the reason why we did it was because even though there were zero, I mean, these researchers had no money. And this is kind of one of the great skills of our company, that you're willing to do something even though the financial returns are completely non-existent or maybe very, very far out, even if you believed in it. We ask ourselves, you know, is this worthy work to do? Does this advance a field of science somewhere that matters? Notice, this is something that I've been talking about, you know, since the very beginning of time. We find inspiration not from the size of a market, but from the importance of the work. Because the importance of the work is the early indicators of a future market. And nobody has to do a business case on it. Nobody has to show me a P&L. Nobody has to show me a financial forecast. The only question is, is this important work? And if we didn't do it, would it happen without us? Now, if we didn't do it, would it happen without us? Now, if we didn't do something and something could happen without us, it gives me tremendous joy, actually. And the reason for that is, could you imagine? The world got better. You didn't have to lift a finger. That's the definition of ultimate laziness. And in a lot of ways, you want that habit. And the reason for that is this. You want the company to be lazy about doing things that other people always do, can do. If somebody else can do it, let them do it. We should go select the things that, if we didn't do it, the world would fall apart. You have to convince yourself of that. That if I don't do this, it won't get done. That is, and if that work is hard, and that work is impactful and important, then it gives you a sense of purpose. Does that make sense? And so our company has been selecting these projects, deep learning was just one of them. And the first indicator of the success of that was this fuzzy cat that Andrew Ng came up with. And then Alex Kershavsky detected cats, not all the time, but successfully enough that it was, this might take us somewhere. And we reasoned about the structure of deep learning, and we're computer scientists, and we understand how things work. And so we convinced ourselves this could change everything. And anyhow, but that's an example. So these selections that you've made, they've paid huge dividends, both literally and figuratively. But you've had to steer the company through some very challenging times, like when it lost 80% of its market cap amid the financial crisis because Wall Street didn't through some very challenging times, like when it lost 80% of its market cap amid the financial crisis, because Wall Street didn't believe in your bet on ML. In times like these, how do you steer the company and keep the employees motivated at the task at hand? My reaction during that time is the same reaction I had about this week. Earlier today, you asked me about this week. My pulse was exactly the same reaction I had about this week. Earlier today, you asked me about this week. My pulse was exactly the same. This week is no different than last week or the week before that. And so the opposite of that, you know, when you drop 80%, don't get me wrong. When your share price drops 80%, it's a little embarrassing, okay? And you just wanna wear a T-shirt that says, "'Wasn't my fault.'" But even more than that, you just don't wanna, you don't wanna get out of your bed, you don't wanna leave the house. All of that is true, All of that is true. All of that is true. But then you go back to just doing your job. Woke up at the same time. Prioritized my day in the same way. I go back to what do I believe. You gotta gut check, always gut check back to the core. What do you believe? What are the most important things? And just check them off. Sometimes it's helpful to, family loves me? Okay, check. And so you just got to check it off. And you go back to your core and then go back to work. And then every conversation is go back to the core. Keep the company focused back on the core. Do you believe in it? Did something change? The stock price changed, but did something else change? Did physics change? Did gravity change? Did all of the things that we assumed, that we believed, that led to our decision, did any of those things change? Because if those things change, you gotta change everything. But if none of those things change, you change nothing. Keep on going. That's how you do it. In speaking with your employees, they say that you- And try to avoid the public. In speaking with your employees, they've said that your leadership- Including the employees. I'm just kidding. No, leaders have to be seen, unfortunately. That's the hard part. I was an electrical engineering student, and I was quite young when I went to school. When I went to college, I was still 16 years old, and so I was young when I did everything. And so I was a bit of an introvert, kind of, you know, I'm shy, I don't enjoy public speaking. I'm delighted to be here, I'm not suggesting. But it's not something that I do naturally. And so when things are challenging, it's not easy to be in front of precisely the people that you care most about. And the reason for that is because, could you imagine a company meeting, which is our stock prices dropped by 80%? And the most important thing I have to do as the CEO is this, to come and face you, explain it. And partly, you're not sure why, partly you're not sure how long, how bad, you just don't know these things, but you still gotta explain it. Face all these people, and you know what they're thinking. Some of them are probably thinking we're doomed, some people are probably thinking you're an idiot, and some people are probably thinking we're doomed. Some people are probably thinking you're an idiot. And some people are probably thinking something else. And so there are a lot of things that people are thinking, and you know that they're thinking those things. But you still have to get in front of them and do the hard work. They may be thinking of those things, but yet not a single person of your leadership team left during times like this. And in fact- They're unemployable. That's what I keep reminding them. but yet not a single person of your leadership team left during times like this. And in fact- They're unemployable. That's what I keep reminding them. I'm just kidding. I'm surrounded by geniuses. I'm surrounded by geniuses. Other geniuses. Unbelievable. NVIDIA is well known to have singularly the best management team on the planet. This is the deepest technology management team the world's ever seen. I'm surrounded by a whole bunch of them, and they're just geniuses. Business teams, marketing teams, sales teams, just incredible. Engineering teams, research teams, unbelievable. Yeah. Your employees say that your leadership style is very engaged you have 50 direct reports you encourage people across all parts of the organization to send you the top five things on their mind and you constantly remind people that no task is beneath you can you tell us why you've purposefully designed such a flat organization, and how should we be thinking about our organizations that we design in the future? To me, no task is beneath me because, remember, I used to be a dishwasher, and I mean that. I used to clean toilets. I cleaned a lot of toilets. I've cleaned more toilets than all of you combined. Some of them just can't unsee. I don't know what to tell you. That's life. And so you can't show me a task that's beneath me. Now, I'm not doing it only because of, whether it's beneath me or not beneath me. If you send me something and you want my input on it, and I can be of service to you, and in my review of it, share with you how I reason through it, I've made a contribution to you. I've made it possible for you to see how I reason through it, I've made a contribution to you. I've made it possible for you to see how I reason through something. And by reasoning, as you know, how someone reasons through something empowers you. You go, oh my gosh, that's how you reason through something like this. It's not as complicated as it seems. This is how you reason through something that's super ambiguous. This is how you reason through something that's incalculable. This is how you reason through something that's super ambiguous. This is how you reason through something that's incalculable. This is how you reason through something that seems to be very scary. This is how you seem, do you understand? And so I show people how to reason through things all the time. Strategy things, you know, how to forecast something, how to break a problem down, and you're empowering people all over the place. And so that's how I see it. If you send me something, you want me to help review it, I'll do my best, and I'll show you how I would do it. In the process of doing that, of course, I learned a lot from you. Is that right? You gave me the seed of a lot of information. I learned a lot, and so I feel rewarded by the process. It does take a lot of energy sometimes, because in order to add value to somebody, and they're incredibly smart as a starting point, and I'm surrounded by incredibly smart people, you have to at least get to their plane. You have to get into their headspace. And that's really hard. That's really hard. And that takes just an enormous amount of emotional and intellectual energy. And so I feel exhausted after I work on things like that. I'm surrounded by a lot of great people. A CEO should have the most direct reports by definition because the people that reports to the CEO requires the least amount of management. It makes no sense to me that CEOs have so few people reporting to them, except for one fact that I know to be true. The knowledge, the information of a CEO is supposedly so valuable, so secretive, you can only share it with two other people, or three. And their information is so invaluable, so incredibly secretive, that they can only share with a couple more. Well, I don't believe in a culture and environment where the information that you possess is the reason why you have power. I would like us all to contribute to the company and our position in the company should have something to do with our ability to reason through complicated things, lead other people to achieve greatness, inspire, empower other people, support other people. Those are the reasons why the management team exists. In service of all of empower other people, support other people. Those are the reasons why the management team exists. In service of all of the other people that work in the company, to create the conditions by which all of these amazing people volunteer to come work for you instead of all the other amazing high-tech companies around the world, they elected, they volunteered to work for you. And so you should create the conditions by which they could do their life's work, which is my mission. You probably heard it. I've said that pretty clearly, and I believe that. What my job is is very simply to create the conditions by which you could do your life's work. And so how do I do that? What does that condition look like? Well, that condition should result in a great deal of empowerment. You can only be empowered if you understand the circumstance. Isn't that right? You have to understand the context of the situation you're in in order for you to come up with great ideas. And so I have to create a circumstance where you understand the context, which means you have to be informed. And the best way to be informed is for there to be as little layers of information mutilation between us. And so that's the reason why it's very often that I'm reasoning through things like in an audience like this. I say, first of all, this is the beginning facts. These are the data that we have. This is how I would reason through it. These are some of the assumptions. These are some of the unknowns. These are some of the knowns. And so you reason through it. And now you've created an organization that's highly empowered. NVIDIA's 30,000 people. We're the smallest large company in the world. We're a tiny little company. But every employee is so empowered and they're making smart decisions on my behalf every single day. And the reason for that is because they understand my condition. They understand my condition, I'm very transparent with people. And I believe that I can trust you with the information. Oftentimes the information is hard to hear, and the situations are complicated, but I trust that you can handle it. A lot of people hear me say, you're adults here, you can handle this. Sometimes they're not really adults, they just graduated. I'm just kidding. I know that when I first graduated, I was barely an adult. But I was fortunate that I was trusted with important information. So I want to do that. I want to create the conditions for people to do that. I do want to now address the topic that is on everybody's mind, AI. Last week, you said that generative AI and accelerated computing have hit the tipping point. So as this technology becomes more mainstream, what are the applications that you personally are most excited about? Well, you have to go back to first principles and ask yourself, what is generative AI? What happened? What happened was we now have the ability to have software that can understand something. They can understand why, you know, what is, first of all, we digitized everything. That was, you know, like, for example, gene sequencing. You digitize genes. But what does it mean? That sequence of genes, what does it mean? We've digitized amino acids, but what does it mean? And so we now have the ability, we digitize words, we digitize sounds, we digitize images, videos, we digitize a lot of things, but what does it mean? We now have the ability through a lot of studying, a lot of data and from the patterns and relationships, we now understand what they mean. Not only do we understand what they mean, we can translate between them. Because we learned about the meaning of these things in the same world, we didn't learn about them separately. So we learned about speech and words and paragraphs and vocabulary in the same context. And so we found correlations between them and they're all registered, if you will, registered to each other. And so now, not only do we understand the modality, the meaning of each modality, we can understand how to translate between them. And so for obvious things, you could caption video to text. That's captioning. Text to images, mid-journey. Text to text, chat GPT, amazing things. And so we now know that we understand meaning and we can translate. The translation of something is generation of information and all of a sudden you have to take a step back and ask yourself, what is the implication in every single layer of everything that we do? And so I'm exercising in front of you, I'm reasoning in front of you, the same thing I did 15 years ago when I first saw AlexNet some 13, 14 years ago, I guess, how I reasoned through it. What did I see? How interesting? What through it. What did I see? How interesting? What can it do? Very cool. But then most importantly, what does it mean? What does it mean? What does it mean to every single layer of computing? Because we're in the world of computing. And so what it means is that the way that we process information fundamentally will be different in the future. That's what NVIDIA builds, you know, chips and systems. The way we write software will be fundamentally different in the future. The type of software we'll be able to write in the future will be different, new applications. And then also, the processing of those applications will be different. What was historically a retrieval-based model where information was pre-recorded, if you will, almost. You know, we wrote the text pre-recorded, and we retrieved it based on some recommender system algorithm. In the future, some seed of information will be the starting point. We call them prompts, as you guys know. And then we generate the rest of it. And so the future of computing will be the starting point. We call them prompts, as you guys know. And then we generate the rest of it. And so the future of computing will be highly generated. Well, let me give you an example of what's happening. For example, we're having a conversation right now. Very little of the information I'm conveying to you is retrieved. Most of it is generated. It's called intelligence. And so in the future, we're going to have a lot more generative. Our computers will perform in that way. It's going to be highly generative instead of highly retrieval-based. Then you go back and you're going to ask yourself, you know, now for entrepreneurs, you've got to ask yourself, what industries will be disrupted therefore? Will we think about networking the same way? Will we think about storage the same way? Will we think about, would we be as abusive of internet traffic as we are today? Probably not. Notice we're having a conversation right now, and I don't have to get in my car every question. So we don't have to be as abusive of transformation, information transporting as we used to. What's going to be more? What's going to be less? What kind of applications? You know, et cetera, et cetera. So you can go through the entire industrial spread and ask yourself what's going to get disrupted, what's going to be different, what's going to get nude, you know, so on and so forth. And that reasoning starts from what is happening? What is generative AI? Foundationally, what is happening? Go back to first principles with all things. There was something I was going to tell you about organization. You asked the question, and I forgot to answer it. The way you create an organization, by the way, someday, don't worry about how other companies' org charts look. You start from first principles. Remember what an organization is designed to do. The organizations of the past where there's a king, CEO, and then you have all these, the royal subjects, the royal court, and then e-staff. And then you keep working your way down. Eventually, they're employees. The reason why it was designed that way is because they wanted the employees to have as low information as possible because their fundamental purpose of the soldiers is to die in the field of battle, to die without asking questions. You guys know this. I only have 30,000 employees. I would like none of them to die. I would like them to question everything. Does that make sense? And so the way you organize in the past and the way you organize today is very different. Second, the question is, what does NVIDIA build? An organization is designed so that we could build whatever it is we build better. And so if we all build different things, why are we organized the same way? Why would this organizational machinery be exactly the same irrespective of what you build? It doesn't make any sense. You build computers, you organize this way. You build health care services, you build exactly the same way. It makes no sense whatsoever. And so you have to go back to first principles. Just ask yourself, what kind of machinery? What is the input? healthcare services, you're built exactly the same way. It makes no sense whatsoever. And so you have to go back to first principles, just ask yourself what kind of machinery, what is the input, what is the output, what are the properties of this environment, you know, what is the forest that this animal has to live in? What are its characteristics? Is it stable most of the time, you're trying to squeeze out the last drop of water? Or is it changing all the time, being attacked by everybody? And so you got to understand, you know, you're the CEO, your job is to architect this company. That's my first job, to create the conditions by which you can do your life's work. And the architecture has to be right. And so you have to go back to first principles and think about those things. And I was fortunate that when I was 29 years old, I had the benefit of taking a step back and asking myself, how would I build this company for the future, and what would it look like? And what's the operating system, which is called culture? What kind of behavior do we encourage, enhance, and what do we discourage and not enhance? So on and so forth. And anyways. I want to save time for audience questions, enhance and what do we discourage and not enhance? So on and so forth. Anyways. I want to save time for audience questions, but this year's theme for View From the Top is Redefining Tomorrow. And one question we've asked all of our guests is Jensen, as the co-founder and CEO of NVIDIA, if you were to close your eyes and magically change one thing about tomorrow, what would it be? Were we supposed to think about this in advance? I'm going to give you a horrible answer. I don't know that it's one thing. Look, there are a lot of things we don't control. You know, there are a lot of things we don't control. Your job is to make a unique contribution. Live a life of purpose. To do something that nobody else in the world would do or can do. To make a unique contribution. So that in the event that do or can do to make a unique contribution so that in the event that after you're done, everybody says, you know, the world was better because you were here. And so I think that to me, I live my life kind of like this. I go forward in time and I look backwards. I live my life kind of like this. I go forward in time, and I look backwards. So you asked me a question that's exactly from a computer vision pose perspective, exactly the opposite of how I think. I never look forward from where I am. I go forward in time and look backwards. And the reason for that is it's easier. I would look backwards and kind of read my history. We did this, and we did it that way, and we broke that problem down. Does that make sense? And so it's a little bit like how you guys solve problems. You figure out what is the end result that you're looking for, and you work backwards to achieve it. And so I imagine NVIDIA making a unique contribution to advancing the future of computing, which is the single most important instrument of all humanity. Now, it's not about our self-importance, but this is just what we're good at, and it's incredibly hard to do. And we believe we can make an absolute unique contribution. It's taken us 31 years to be here, and we're still just beginning our journey. And so this is insanely hard to do. And when I look backwards, I believe that we made, I believe that we're going to be remembered as a company that kind of changed everything. Not because we went out and changed everything through all the things that we said, but because we did this one thing that was insanely hard to do, that we're incredibly good at doing, that we love doing, we did for a long time. I'm part of the GSB lead, I graduated in 2023. So my question is, how do you see your company in the next decade as, what challenges do you see your company would face and how you are positioned for that? First of all, can I just tell you what was going on through my head? As you say what challenges, the list that flew by my head was so large that I was trying to figure out what to select. Now, the honest truth is that when you asked that question, most of the challenges that showed up for me were technical challenges. And the reason for that is because that was my morning. If you were, you know, chosen yesterday, it might have been market creation challenges. There are some markets that I, gosh, I just desperately would love to create. I just, can't we just do it already? You know? But we can't do it alone. NVIDIA is a technology platform company. We're here in service of a whole bunch of other companies so that they could realize, if you will, our hopes and dreams through them. And so some of the things that I would love, I would love for the world of biology to be at a point where it's kind of like the world of chip design 40 years ago. Computer-aided design, EDA, that entire industry, really made possible for us today. And I believe we're going to make possible for them tomorrow. Computer-aided drug design, because we're able to now represent genes and proteins and even cells now, very, very close to be able to represent and understand the meaning of a cell, a combination of a whole bunch of genes. What does a cell mean? It's kind of like, what does that paragraph mean? Well, if we could understand a cell like we can understand a paragraph, imagine what we could do. And so I'm anxious for that to happen. I'm kind of excited about that. There's some that I'm just excited about that I know we're around the corner on. For example, humanoid robotics. They're very, very close around the corner. And the reason for that is because if you can tokenize and understand speech, why can't you tokenize and understand manipulation? And so these kind of computer science techniques, once you figure something out, you ask yourself, well, if I've got to do that, why can't I do that? And so I'm excited about those kind of things. And so that challenge is about those kind of things. And so that challenge is kind of a happy challenge. Some of the other challenges, of course, are industrial and geopolitical and they're social. But you've heard all that stuff before. These are all true. The social issues in the world, the geopolitical issues in the world. Why can't we just get along things in the world? Why do we have to say those kind of things in the world? Why do we have to say those things and then amplify them in the world? Why do we have to judge people so much in the world? All those things, you guys all know that. I don't have to say those things over again. My name is Jose. I'm a class of the 2023 from the GSB. My question is, are you worried at all about the pace at which we're developing AI? And do you believe that any sort of regulation might be needed? Thank you. Yeah, the answer is yes and no. We need, you know that the greatest breakthrough in modern AI, of course, deep learning and it enabled great progress. But another incredible breakthrough is something that humans know and we practice all the time. And we just invented it for language models called grounding, reinforcement learning, human feedback. I provide reinforcement learning, human feedback every day. That's my job. And for their parents in the room, you're providing reinforcement learning human feedback all the time. Okay, now we just figured out how to do that at a system systematic level for artificial intelligence. There are a whole bunch of other technology necessary to guardrail, fine-tune, ground, for example? How do I generate tokens that obey the laws of physics? Right now, things are floating in space and doing things, and they don't obey the laws of physics. That requires technology. Guard railing requires technology. Fine-tuning requires technology. Alignment requires technology. Safety requires technology. Guard railing requires technology. Fine tuning requires technology. Alignment requires technology. Safety requires technology. The reason why planes are so safe is because all of the autopilot systems are surrounded by diversity and redundancy and all kinds of different functional safety and active safety systems that were invented. I need all of that to be invented much, much faster. You also know that the border between security and artificial intelligence, cybersecurity and artificial intelligence is going to become blurry and brillary and we need technology to advance very, very quickly in the area of cybersecurity in order to protect us from artificial intelligence. And so in a lot of ways we need technology to go faster a lot faster okay regulation there's two types of regulation there's social regulation I don't know what to do about that but there's product and services regulation know exactly what to do about that okay so the FAA the FDA the NHTSA you name name it, all the Fs and all the Ns and all the FCCs, they all have regulations for products and services that have particular use cases. Bar exams and doctors and so on and so forth. You all have qualification exams. You all have standards that you have to reach. You all have to continuously be certified. Accountants and so on and so forth. Whether it's a product or a service, there are lots and lots of regulations. Please do not add a super regulation that cuts across of it. The regulator who is regulating accounting should not be the regulator that regulates a doctor. You know, I love accountants, should not be the regulator that regulates a doctor. I love accountants, but if I ever need an open-heart surgery, the fact that they can close books is interesting but not sufficient. And so I would like all of those fields that already have products and services to also enhance their regulations in context of in the context of AI okay but I left out this one very big one which is the social implication of AI and how do you how do you deal with that I don't have great answers for that but you know enough people are talking about it but it's important to subdivide all of this into chunks doesn't make sense so that we don't we don't become super hyper-focused on this one thing at the expense of a whole bunch of routine things that we could have done. And as a result, people are getting killed by cars and planes. And it doesn't make any sense. We should make sure that we do the right things there. Very practical things. May I take one more question? Well, we have some rapid fire questions for you as view from the tradition. OK. I was trying to avoid that. OK, all right, fire away. Fire away. Well, your first job was at Denny's. They now have a booth dedicated to you. What was your fondest memory of working there? My second job was AMD, by the way. Is there a booth dedicated to me there? I'm just kidding. I love my job there. I did. I loved it. It's a great company. If there were a worldwide shortage of black leather jackets, what would we see you wearing? Oh, no, I've got a large reservoir of black jackets. I'm the, I'll be the only person who is not concerned. You spoke a lot about textbooks. If you had to write one, what would it be called? I wouldn't write one. You're asking me a hypothetical question that has no possibility of. That's fair. And finally, if you could share one parting piece of advice to broadcast across Stanford, what would it be? It's not a word, but have a core belief. Gut check it every day. But have a core belief. Gut check it every day. Pursue it with all your might. Pursue it for a very long time. Surround yourself with people you love and take them on that ride. So that's the story of NVIDIA. Jensen, this last hour has been a treat. Thank you for spending it with us. Thank you very much.. you

Nvidia CEO Jensen Huang and the $2 trillion company powering today's AI | 60 Minutes

 [00:01.560 -> 00:07.640]  Only four companies in the world are worth more than $2 trillion – Microsoft, Apple,

[00:07.640 -> 00:12.880]  Alphabet, parent company of Google, and computer chip maker Nvidia.

[00:12.880 -> 00:19.400]  The California-based company saw its stock market value soar from $1 trillion to $2 trillion

[00:19.400 -> 00:26.560]  in just eight months this past year, fueled by the insatiable demand for its cutting-edge technology, the

[00:26.560 -> 00:31.760]  hardware and software that make today's artificial intelligence possible.

[00:31.760 -> 00:37.840]  We wondered how a company founded in 1993 to improve video game graphics turned into

[00:37.840 -> 00:41.360]  a titan of 21st century AI.

[00:41.360 -> 00:53.420]  So we went to Silicon Valley to meet Nvidia's 61-year-old co-founder and CEO, Jensen Huang, who has no doubt AI is about to change everything.

[00:54.960 -> 00:58.000]  The story will continue in a moment.

[01:13.160 -> 01:14.460]  At NVIDIA's annual developers conference this past March, the mood wasn't just upbeat.

[01:16.680 -> 01:17.400]  It was downright giddy.

[01:30.520 -> 01:31.620]  More than 11,000 enthusiasts, software developers, tech moguls, and happy shareholders filed into San Jose's pro hockey arena to kick off a four-day AI extravaganza.

[01:37.360 -> 01:37.880]  They came to see this man, Jensen Huang, CEO of NVIDIA.

[01:39.980 -> 01:40.640]  Welcome to GTC!

[01:44.600 -> 01:45.100]  What was that like for you to walk out on that stage and see that?

[01:47.540 -> 01:48.040]  You know, Bill, I'm an engineer, not a performer.

[01:53.440 -> 01:53.600]  When I walked out there and all of the people going crazy, it took the breath out of me.

[01:56.640 -> 01:58.000]  And so I was the scariest I've ever been. I'm still scared.

[01:59.400 -> 01:59.820]  You'd never know it.

[02:06.180 -> 02:06.280]  Clad in his signature cool black outfit, Jensen shared the stage with NVIDIA-powered robots.

[02:07.320 -> 02:07.620]  Let me finish up real quick.

[02:10.380 -> 02:10.600]  And shared his vision of an AI future.

[02:12.320 -> 02:18.900]  A new industrial revolution. It reminded us of the transformational moment when Apple's Steve Jobs unveiled the iPhone.

[02:19.460 -> 02:24.940]  Jensen Huang unveiled NVIDIA's latest graphics processing unit, or GPU.

[02:25.440 -> 02:26.520]  This is Blackwell.

[02:26.880 -> 02:31.400]  Designed in America but made in Taiwan like most advanced semiconductors,

[02:31.920 -> 02:35.080]  Blackwell, he says, is the fastest chip ever.

[02:35.460 -> 02:37.360]  Google is gearing up for Blackwell.

[02:37.360 -> 02:40.520]  The whole industry is gearing up for Blackwell.

[02:41.020 -> 02:47.540]  NVIDIA ushered in the AI revolution with its game-changing GPU, a single chip able

[02:47.540 -> 02:53.860]  to process a myriad of calculations all at once, not sequentially like more standard chips.

[02:54.480 -> 03:02.080]  The GPU is the engine of NVIDIA's AI computer, enabling it to rapidly absorb a fire hose of

[03:02.080 -> 03:06.340]  information. It does quadrillions of calculations a second.

[03:06.340 -> 03:08.500]  It's just insane numbers.

[03:08.500 -> 03:10.880]  Is it doing things now that surprise you?

[03:10.880 -> 03:12.720]  We're hoping that it does things that surprise us.

[03:12.720 -> 03:14.020]  That's the whole point.

[03:14.020 -> 03:16.220]  In some areas, like drug discovery,

[03:16.220 -> 03:19.860]  designing better materials that are lighter, stronger.

[03:19.860 -> 03:23.860]  We need artificial intelligence to help us explore the universe

[03:23.860 -> 03:26.240]  in places that we could have never done ourselves.

[03:26.400 -> 03:28.540]  Let me show you. Here, Bill, look at this.

[03:28.760 -> 03:36.920]  Jensen took us around the GTC convention hall to show us what AI has made possible in just the past few years.

[03:37.060 -> 03:38.340]  I'm making your drink now.

[03:38.480 -> 03:40.660]  Some creations were dazzling.

[03:40.660 -> 03:43.460]  This is a digital twin of the Earth.

[03:46.020 -> 03:53.660]  dazzling. This is a digital twin of the earth. Once it learns how to calculate weather, it can calculate and predict weather 3,000 times faster than a supercomputer and a thousand times less

[03:53.660 -> 04:03.480]  energy. But NVIDIA's AI revolution extends far beyond this hall. Blue metallic spaceship.

[04:03.480 -> 04:05.080]  And let's generate something.

[04:06.540 -> 04:08.400]  Pinar Seyhan Demirda is originally from Istanbul,

[04:08.840 -> 04:11.300]  but co-founded Qubrick near Boston.

[04:11.980 -> 04:13.320]  Her AI application

[04:13.320 -> 04:15.420]  uses NVIDIA's GPUs

[04:15.420 -> 04:16.740]  to instantly turn

[04:16.740 -> 04:18.220]  a simple text prompt

[04:18.220 -> 04:20.040]  into a virtual movie set

[04:20.040 -> 04:21.820]  for a fraction of the cost

[04:21.820 -> 04:23.360]  of today's backdrops.

[04:23.360 -> 04:24.360]  This isn't something

[04:24.360 -> 04:26.120]  that's already planned.

[04:26.260 -> 04:28.960]  No, we're doing it in real time. It's live.

[04:29.160 -> 04:30.820]  Is Hollywood knocking at your door?

[04:31.700 -> 04:33.300]  We're getting a lot of love.

[04:34.880 -> 04:37.200]  Nearby at Generate Biomedicines,

[04:37.200 -> 04:40.540]  Dr. Alex Snyder, head of research and development,

[04:40.880 -> 04:46.680]  is using NVIDIA's technology to create protein-based drugs. She was surprised

[04:46.680 -> 04:49.480]  at first to see they showed promise in the lab.

[04:49.820 -> 04:54.260]  Initially, when I was told about the application of AI to drug development, I sort of rolled

[04:54.260 -> 04:59.140]  my eyes and said, yeah, you know, show me the data. And then I looked at the data, and

[04:59.140 -> 05:00.080]  it was very compelling.

[05:01.140 -> 05:07.720]  Dr. Snyder's team asks its AI models to create new proteins to fight specific diseases

[05:07.720 -> 05:09.800]  like cancer and asthma.

[05:09.800 -> 05:13.940]  A new way to defeat the coronavirus is now in clinical trials.

[05:13.940 -> 05:19.940]  You're now working with proteins that do not exist in nature, that you're coming

[05:19.940 -> 05:22.440]  up with by way of AI?

[05:22.440 -> 05:23.520]  Yes.

[05:23.520 -> 05:26.020]  We are actually generating what we call de novo,

[05:26.020 -> 05:30.060]  completely new structures that have not existed before.

[05:30.060 -> 05:31.520]  Do you trust it?

[05:31.520 -> 05:34.320]  As scientists, we can't trust, we have to test.

[05:34.320 -> 05:36.380]  We're not putting Franken-signs into people.

[05:36.380 -> 05:37.920]  We're taking what's known,

[05:37.920 -> 05:39.860]  and we're really pushing the field,

[05:39.860 -> 05:42.240]  we're pushing the biology to make drugs

[05:42.240 -> 05:45.240]  that look like regular drugs drugs but function even better.

[05:45.900 -> 05:48.200]  This is a technology that will only get better from here.

[05:48.560 -> 05:54.520]  Brett Adcock is CEO of Figure, a Silicon Valley startup with funding from NVIDIA.

[05:55.060 -> 06:02.520]  Look at his answer to labor shortages, an NVIDIA GPU-driven prototype called Figure One.

[06:03.260 -> 06:07.240]  I think what's been really extraordinary is the pace of progress we've made in 21 months.

[06:07.240 -> 06:08.540]  From zero to this in 21 months.

[06:08.540 -> 06:09.540]  Zero to this, yeah.

[06:09.540 -> 06:12.340]  We were walking this robot in under a year

[06:12.340 -> 06:14.240]  since I incorporated the company.

[06:14.240 -> 06:17.040]  Could you do this without NVIDIA's technology?

[06:17.040 -> 06:20.260]  We think they're arguably the best in the world at this.

[06:20.260 -> 06:23.000]  I don't know if this would be possible without them.

[06:23.000 -> 06:26.020]  I'm here to assist with tasks as requested.

[06:26.640 -> 06:32.020]  We were amazed that Figure 1 is not just walking, but seemed to reason.

[06:32.780 -> 06:34.640]  Hand me something healthy.

[06:35.660 -> 06:36.020]  On it.

[06:36.440 -> 06:41.940]  Figure 1 was able to understand I wanted the orange, not the packaged snack.

[06:41.940 -> 06:42.800]  Thank you.

[06:43.580 -> 06:44.920]  It's not yet perfected.

[06:44.980 -> 06:45.000]  You're going to get it. not the packaged snack. Thank you. It's not yet perfected.

[06:45.000 -> 06:46.000]  You're going to get it.

[06:46.000 -> 06:48.000]  But the early results are so promising,

[06:48.000 -> 06:53.000]  German automaker BMW plans to start testing the robot

[06:53.000 -> 06:56.000]  in its South Carolina factory this year.

[06:56.000 -> 06:59.000]  I think there's an opportunity to ship billions of robots

[06:59.000 -> 07:02.000]  in the coming decades onto the planet.

[07:02.000 -> 07:04.000]  Billions.

[07:04.000 -> 07:08.700]  I would think that a lot of workers would look at that as,

[07:08.700 -> 07:11.360]  this robot is taking my job.

[07:11.360 -> 07:13.520]  I think over time, AI and robotics

[07:13.520 -> 07:17.940]  will start doing more and more of what humans can and better.

[07:17.940 -> 07:20.120]  But what about the worker?

[07:20.120 -> 07:22.360]  The workers work for companies.

[07:22.360 -> 07:27.060]  And so companies, when they become more productive, earnings increase.

[07:27.680 -> 07:33.160]  I've never seen one company that had earnings increase and not hire more people.

[07:33.160 -> 07:37.160]  There are some jobs that are going to become obsolete.

[07:38.040 -> 07:39.340]  Well, let me offer it this way.

[07:39.620 -> 07:43.880]  I believe that you still want human in the loop because we have good judgment,

[07:44.340 -> 07:46.000]  because there are circumstances that the machines

[07:46.000 -> 07:48.000]  are just not going to understand.

[07:48.000 -> 07:50.000]  The futuristic NVIDIA campus

[07:50.000 -> 07:54.000]  sits just down the road from its modest birthplace,

[07:54.000 -> 07:56.000]  this Denny's in San Jose.

[07:56.000 -> 07:58.000]  Good morning.

[07:58.000 -> 08:01.000]  Where 31 years ago, NVIDIA was just an idea.

[08:01.000 -> 08:03.000]  My goodness.

[08:03.000 -> 08:06.920]  When he was 15, Jensen Huang worked as a dishwasher at Denny's.

[08:07.320 -> 08:14.100]  As a 30-year-old electrical engineer married with two children, he and two friends, NVIDIA

[08:14.100 -> 08:21.560]  co-founders Chris Malachowski and Curtis Preem, envisioned a whole new way of processing video

[08:21.560 -> 08:22.500]  game graphics.

[08:22.780 -> 08:25.760]  So we came here, right here to this denny's, sat right back there,

[08:26.120 -> 08:28.600]  and the three of us decided to start the company.

[08:29.360 -> 08:31.000]  Frankly, I had no idea how to do it.

[08:32.060 -> 08:32.820]  And nor did they.

[08:33.080 -> 08:34.400]  None of us knew how to do anything.

[08:35.280 -> 08:36.280]  Their big idea?

[08:37.120 -> 08:39.520]  Accelerate the processing power of computers

[08:39.520 -> 08:41.620]  with a new graphics chip.

[08:42.260 -> 08:44.220]  Their initial attempt flopped

[08:44.220 -> 08:48.420]  and nearly bankrupted the company in 1996.

[08:48.420 -> 08:53.980]  And the genius of the engineers and Chris and Curtis, we pivoted to the right way of

[08:53.980 -> 08:55.300]  doing things.

[08:55.300 -> 08:58.320]  And created their groundbreaking GPU.

[08:58.320 -> 09:07.160]  The chip took video games from this to this today. Completely changed computer graphics,

[09:07.680 -> 09:08.420]  saved the company,

[09:09.100 -> 09:11.500]  launched us into the stratosphere.

[09:12.160 -> 09:14.220]  Just eight years after Denny's,

[09:14.480 -> 09:17.440]  NVIDIA earned a spot in the S&P 500.

[09:18.180 -> 09:19.740]  Jensen then set his sights

[09:19.740 -> 09:21.900]  on developing the software and hardware

[09:21.900 -> 09:25.580]  for a revolutionary GPU-driven supercomputer,

[09:26.060 -> 09:29.320]  which would take the company far beyond video games.

[09:29.800 -> 09:32.520]  To Wall Street, it was a risky bet.

[09:32.960 -> 09:36.300]  To early developers of AI, it was a revelation.

[09:37.020 -> 09:38.880]  Was that luck or was that vision?

[09:39.440 -> 09:41.980]  That was luck founded by vision.

[09:42.160 -> 09:43.980]  We invented this capability.

[09:44.980 -> 09:46.380]  And then one day the

[09:46.380 -> 09:52.000]  researchers that were creating deep learning discovered this architecture

[09:52.000 -> 09:55.600]  because this architecture turns out to have been perfect for them.

[09:55.600 -> 09:56.800]  Perfect for AI.

[09:56.800 -> 09:57.800]  Perfect for AI.

[09:57.800 -> 09:59.800]  This is the first one we've ever shipped.

[09:59.800 -> 10:14.520]  In 2016 Jensen delivered NVIDIA's AI supercomputer, the first of its kind, to Elon Musk, then a board member of OpenAI, which used it to create the building blocks of ChatGPT.

[10:14.880 -> 10:15.320]  How are you?

[10:15.680 -> 10:20.260]  When AI took off, so did Jensen Huang's reputation.

[10:21.620 -> 10:22.780]  Can we get a picture?

[10:23.020 -> 10:23.520]  Yeah, yeah.

[10:23.520 -> 10:26.000]  He's now a Silicon Valley celebrity.

[10:26.000 -> 10:33.000]  He told us the boy who immigrated from Taiwan at age nine could never have conceived of this.

[10:33.000 -> 10:41.000]  It is the most extraordinary thing, Bill, that a normal dishwasher busboy could grow up to be this.

[10:41.000 -> 10:47.460]  There's no magic. It's just 61 years of hard work every single day.

[10:48.120 -> 10:53.640]  I don't think there's anything more than that. We met a humble Jensen at Denny's. Back at

[10:53.640 -> 11:00.600]  NVIDIA's headquarters in Santa Clara, we saw he can be intense. Let me tell you what some of the

[11:00.600 -> 11:05.480]  people who you work with said about you. Demanding. Perfectionist.

[11:05.920 -> 11:07.220]  Not easy to work for.

[11:07.900 -> 11:08.740]  All that sound right?

[11:09.020 -> 11:09.760]  Perfectly, yeah.

[11:10.740 -> 11:12.000]  It should be like that.

[11:12.520 -> 11:14.980]  If you want to do extraordinary things,

[11:15.740 -> 11:16.640]  it shouldn't be easy.

[11:17.200 -> 11:18.480]  All right, guys, keep up the good work.

[11:18.900 -> 11:20.840]  NVIDIA has never done better.

[11:21.440 -> 11:22.780]  Investors are bullish.

[11:22.780 -> 11:27.060]  But last year, more than 600 top AI scientists,

[11:27.260 -> 11:34.160]  ethicists, and others signed this statement urging caution, warning of AI's risk to humanity.

[11:34.760 -> 11:38.000]  When I talk to you and I hear you speak, part of me goes,

[11:38.600 -> 11:43.980]  gee whiz. And the other part of me goes, oh my God, what are we in for?

[11:44.200 -> 11:45.180]  Yeah, yeah.

[11:45.260 -> 11:45.980]  Which one is it?

[11:46.220 -> 11:47.900]  It's both. It's both.

[11:48.400 -> 11:50.560]  Yeah, you're feeling all the right feelings. I feel both.

[11:51.220 -> 11:51.820]  You feel both?

[11:51.840 -> 11:52.940]  Sure, sure.

[11:53.400 -> 12:01.360]  Humanity will have the choice to see themselves inferior to machines or superior to machines.

[12:01.360 -> 12:08.400]  Pinar Seyhan Demirda is an AI optimist, though she named her company Kubrick, an homage to

[12:08.400 -> 12:12.300]  Stanley Kubrick, the director of 2001, A Space Odyssey.

[12:12.300 -> 12:14.440]  Hello, Hal, do you read me?

[12:14.440 -> 12:18.960]  In that film, Hal, the AI computer, goes rogue.

[12:18.960 -> 12:22.100]  Open the pod bay doors, Hal.

[12:22.100 -> 12:23.800]  I'm sorry, Dave.

[12:23.800 -> 12:25.400]  I'm afraid I can't do that.

[12:25.400 -> 12:33.240]  I think that's what worries people about AI, that we will lose control of it.

[12:33.740 -> 12:42.320]  Just because a machine can do faster calculations, comparisons, and analytical solution creation, that doesn't make you smarter than you.

[12:42.720 -> 12:44.380]  It simply computates faster.

[12:42.340 -> 12:42.800]  that doesn't make you smarter than you.

[12:44.380 -> 12:45.380]  It simply computates faster.

[12:47.280 -> 12:47.880]  In my world, in my belief,

[12:50.360 -> 12:50.760]  smarts have to do with your capacity to love,

[12:53.020 -> 12:54.340]  create, expand, transcend.

[12:58.140 -> 12:58.680]  These are qualities that no machine can ever bear,

[13:00.360 -> 13:00.740]  that are reserved to only humans.

[13:02.360 -> 13:02.860]  There is something going on.

[13:09.800 -> 13:10.460]  Jensen Huang sees an AI future of progress and prosperity, not one with machines as our masters.

[13:13.000 -> 13:13.380]  We can only hope he's right.

[13:15.960 -> None]  Thank you all for coming. Thank you.

Nvidia CEO Jensen Huang and the $2 trillion company powering today's AI | 60 Minutes

 [00:05.280 -> 00:08.600]  Wow, good morning and welcome everyone again.

[00:08.600 -> 00:13.760]  Joe really doesn't require any introduction from me, but I do want to call out that among

[00:13.760 -> 00:19.700]  the many different roles that Joe has, Joe is also a very passionate philanthropist and

[00:19.700 -> 00:23.120]  also the proud owner of Brooklyn Nets.

[00:23.120 -> 00:27.560]  So Joe, thank you very much for joining us today. Jack Ma

[00:27.560 -> 00:34.340]  actually spoke at our very first China Summit back in 2005, and we cannot be more thrilled

[00:34.340 -> 00:41.540]  and privileged to have you at our 20th. So before we go to Alibaba, which will be a longer discussion,

[00:42.220 -> 00:46.000]  I want to start by asking you, what do you think the next season holds

[00:46.000 -> 00:48.360]  for Brooklyn Nets and New York Liberty?

[00:48.640 -> 00:52.780]  And whether we can expect a China tour anytime soon?

[00:54.420 -> 00:57.760]  Well, first, thank you, Cam Shing, for having me.

[00:57.940 -> 01:00.260]  And it's an honor for me to be on the stage.

[01:00.400 -> 01:03.940]  And congratulations to your 20th China Global Summit.

[01:05.100 -> 01:07.880]  The question about Brooklyn Nets and New York Liberty,

[01:08.060 -> 01:09.440]  the first I'll talk about New York Liberty.

[01:09.620 -> 01:11.840]  We are starting the season.

[01:12.380 -> 01:14.880]  We're four games into the season.

[01:15.040 -> 01:15.700]  We're 4-0.

[01:16.220 -> 01:18.300]  So that bodes well for the rest of the season.

[01:18.420 -> 01:20.360]  That's our women's team in New York.

[01:21.040 -> 01:24.980]  The Brooklyn Nets is at a crossroads in a way.

[01:28.820 -> 01:36.540]  I think we didn't do as well as we expected last season. We didn't make the playoffs, but we hope to revamp the team and make sure that we can

[01:36.540 -> 01:42.100]  compete in the long run. I think there's a difference when people ask owners, what do you

[01:42.100 -> 01:49.980]  want to do with a basketball team? There's a difference between I want to win versus I want to build a winning mentality

[01:49.980 -> 01:51.840]  and culture that's sustainable.

[01:52.520 -> 01:54.180]  Those two are very different things.

[01:54.180 -> 01:59.920]  If you want to be just win now, you could ruin your future by trading away all of your

[01:59.920 -> 02:01.780]  assets and just win now.

[02:02.180 -> 02:05.180]  But I think what I wanted to do with the Brooklyn Nets is to

[02:05.180 -> 02:09.320]  take a longer term approach and build a winning, sustainable winning culture.

[02:09.320 -> 02:14.400]  Well, the same philosophy, I'm sure, for you for running businesses as well. So can we

[02:14.400 -> 02:18.500]  expect a China tour anytime?

[02:18.500 -> 02:28.000]  Yeah, I think because of the geopolitical situation right now, it would be very difficult to bring

[02:28.000 -> 02:30.840]  the NBA to mainland China.

[02:30.840 -> 02:38.640]  But in due course, I hope to see a China tour because what that reflects is a more benign

[02:38.640 -> 02:46.040]  geopolitical environment, especially the relationship between the two great powers, United States and China.

[02:46.040 -> 02:48.920]  Okay, let's hope that you will be really soon.

[02:48.920 -> 02:52.320]  So Joe, you became the chair of Alibaba

[02:52.320 -> 02:53.760]  in September last year,

[02:53.760 -> 02:57.080]  and since then you've reorganized the management teams

[02:57.080 -> 02:59.680]  and made a number of changes.

[02:59.680 -> 03:02.680]  Tell us the thinking behind these changes,

[03:02.680 -> 03:04.480]  and more specifically,

[03:04.480 -> 03:08.640]  how are you gonna invigorate the spirit of growth and innovation,

[03:08.640 -> 03:14.900]  which clearly has propelled Alibaba from a marketplace for small Chinese businesses to

[03:14.900 -> 03:22.260]  today the multinational tech company that Alibaba is with over a billion dollar of consumers.

[03:22.260 -> 03:25.500]  Some critics would say that that spirit might have been somewhat

[03:25.500 -> 03:29.240]  lacking in the recent times.

[03:29.240 -> 03:34.000]  About a year ago, we announced that we were going to reorganize ourselves.

[03:34.000 -> 03:41.520]  The thinking internally, the primary thinking behind that is to ensure that our decision

[03:41.520 -> 03:45.460]  making can be faster and that we give autonomy to more the business

[03:45.460 -> 03:51.980]  unit CEOs rather than having one group CEO having to make a hundred decisions a day.

[03:51.980 -> 03:52.980]  That's not possible.

[03:52.980 -> 04:00.520]  So we solved the CEO bandwidth issue by delegating authority to the business units and also to

[04:00.520 -> 04:07.340]  younger people. And then we had some personnel changes.

[04:08.100 -> 04:10.440]  So I'm in my position where I am now

[04:10.440 -> 04:11.980]  because of those personnel changes.

[04:12.540 -> 04:14.780]  The great thing is I work with a CEO

[04:14.780 -> 04:17.280]  who's much younger than I am.

[04:17.500 -> 04:19.040]  He is the day-to-day guy.

[04:19.140 -> 04:20.280]  Eddie Wu is our CEO.

[04:20.940 -> 04:24.500]  And he has huge credibility within the company

[04:24.500 -> 04:26.540]  because he had been a founder of the

[04:26.540 -> 04:27.540]  business.

[04:27.540 -> 04:34.820]  So we were all in that apartment back in 1999 and he had been involved in developing the

[04:34.820 -> 04:43.560]  major platforms like Taobao, Alipay, as well as all the monetization technology for those

[04:43.560 -> 04:45.600]  platforms. monetization technology for those platforms, so he's very

[04:46.240 -> 04:48.120]  well equipped

[04:48.120 -> 04:52.020]  To be the CEO of the company and having that in place

[04:54.100 -> 05:00.300]  The next thing we said is we want to have focus and if you look at Alibaba today what we have

[05:00.960 -> 05:05.980]  Communicated internally to our employees is that we're in two businesses, e-commerce

[05:05.980 -> 05:06.960]  and cloud computing.

[05:07.960 -> 05:13.100]  And then there are a lot of businesses that could support, that are strategic to us.

[05:13.640 -> 05:19.040]  So to give you an example, we're in the meals delivery business through a platform called

[05:19.040 -> 05:19.360]  Elema.

[05:19.820 -> 05:20.380]  Oh, yes.

[05:20.520 -> 05:20.660]  Yep.

[05:20.860 -> 05:23.340]  So do we have to be delivering meals?

[05:23.480 -> 05:24.660]  Is that our core business?

[05:26.640 -> 05:27.220]  Probably not. However,

[05:36.040 -> 05:41.180]  Ulema is totally strategically strategic, important to us because the on-demand delivery infrastructure that it has established, not just to deliver meals, but also other fresh,

[05:41.320 -> 05:46.280]  perishable items like medicine, like flowers, fruits, that instant delivery

[05:46.280 -> 05:52.120]  infrastructure is completely strategic to us. And that's why it's important part of our business.

[05:52.600 -> 05:59.660]  So we've been able to, we went through a process of understanding what is core, what is strategic,

[06:00.120 -> 06:06.000]  and what are some of the non-core stuff or financial investments that we can exit over time?

[06:06.940 -> 06:07.940]  Right. Great.

[06:08.240 -> 06:14.280]  So artificial intelligence is going to be discussed, I think, throughout the summit these couple of days.

[06:14.920 -> 06:19.660]  And Alibaba is a very active investor into generative AI.

[06:20.400 -> 06:26.320]  And so could you share with us what you think the role AI will play in the world in the coming years?

[06:26.760 -> 06:31.140]  And maybe more specifically, how the AI adoption will happen in Alibaba?

[06:31.680 -> 06:36.760]  Yeah. Yeah. First, well, for me to sit here and talk about AI is like banmen nongfu.

[06:36.920 -> 06:45.660]  You know, like we have a lot of people in the audience who are in actually working on AI projects on a day in and day out basis.

[06:46.840 -> 06:50.380]  So the way as a lay person, if you will,

[06:50.380 -> 06:55.200]  to understand AI is with today's AI,

[06:55.200 -> 06:58.320]  which is very focused on large language models,

[06:58.320 -> 07:02.000]  you're basically trying to train a brain

[07:02.000 -> 07:03.860]  to achieve machine intelligence

[07:03.860 -> 07:05.720]  that could approximate human intelligence.

[07:06.580 -> 07:12.880]  And through that training process, that's like educating a child. Let's say you have children

[07:12.880 -> 07:19.080]  and you send them to school, you send them to middle school, high school, college, and then

[07:19.080 -> 07:26.960]  eventually they get PhDs or eventually they get multiple PhD degrees. This is the race. That's what's going on in the

[07:26.960 -> 07:32.560]  large language model race. So when people try to compare large language models and mine is better

[07:32.560 -> 07:38.040]  than yours, they're really saying, well, I have a child that has like three PhD degrees and they

[07:38.040 -> 07:46.840]  are well versed in biology, math, and psychology, whatever it is, all subject matters.

[07:46.840 -> 07:53.240]  The thing is, if you can understand AI and the training of machine intelligence in that

[07:53.240 -> 07:58.360]  context, in the context of educating kids.

[07:58.360 -> 08:03.620]  So what's happened is it takes about 22 years to send a kid to college and graduate from

[08:03.620 -> 08:06.720]  college and maybe they'll pursue postgraduate degrees.

[08:06.720 -> 08:10.340]  It took us maybe three or four years

[08:10.340 -> 08:13.480]  to get to their large language model

[08:13.480 -> 08:17.140]  to be as smart as human beings

[08:18.140 -> 08:19.780]  in terms of knowledge

[08:19.780 -> 08:22.960]  and certain sort of mathematical computations

[08:22.960 -> 08:24.560]  and things like that

[08:24.560 -> 08:26.540]  to a point where they're

[08:26.540 -> 08:29.180]  just as smart as PhD students.

[08:29.180 -> 08:30.940]  And that's pretty scary.

[08:30.940 -> 08:32.580]  That's incredible.

[08:32.580 -> 08:35.360]  So that's the context.

[08:35.360 -> 08:38.960]  Alibaba is involved in AI in three different ways.

[08:38.960 -> 08:46.420]  Number one, just fundamentally as a technology company, as a pioneer in technology, we believe in

[08:46.420 -> 08:52.160]  continuous advancement of machine intelligence that machines will get smarter and smarter.

[08:52.160 -> 08:59.680]  A lot of people talk about artificial general intelligence, this ideal of reaching AGI.

[08:59.680 -> 09:06.240]  I'm sure at some point we will have machines that could have some aspect of AGI

[09:06.240 -> 09:08.720]  based on however you define AGI.

[09:08.720 -> 09:10.500]  Just like, think about a child.

[09:10.500 -> 09:14.340]  They could be super, super smart in physics.

[09:15.260 -> 09:16.700]  That could be, that's AI.

[09:16.700 -> 09:21.180]  But if you ask them to go and make friends,

[09:21.180 -> 09:23.780]  make 10 friends in a day, they may not be able to do that.

[09:23.780 -> 09:24.620]  Yeah.

[09:24.620 -> 09:26.900]  Right? So in certain respects,

[09:27.180 -> 09:33.340]  machines can become even smarter than human beings. So we believe in this ideal of AGI

[09:33.340 -> 09:40.700]  and continuous development. And today with the idea of scaling law, which means that the more

[09:40.700 -> 09:51.020]  resources, whether it's data resources or computing power that you put into it, the marginal return in terms of performance of these large language models does not diminish.

[09:51.020 -> 09:52.020]  It continues.

[09:52.020 -> 09:55.440]  And in fact, it actually grows in a super linear way.

[09:55.440 -> 10:01.540]  That's pretty scary because it just means that the machine can get smarter and smarter

[10:01.540 -> 10:05.420]  as long as you feed the machine with data.

[10:05.420 -> 10:12.240]  And data is the food and also the books in the library when you're educating a child,

[10:12.240 -> 10:13.240]  right?

[10:13.240 -> 10:15.380]  You use that analogy.

[10:15.380 -> 10:17.420]  So that's number one.

[10:17.420 -> 10:20.540]  As a pure ideal, we're going for it.

[10:20.540 -> 10:25.460]  And Alibaba has developed our proprietary large language model called Tong Yi Qian Wen,

[10:25.460 -> 10:29.680]  which is actually one of the leading models in China. And in certain respects, we are

[10:29.680 -> 10:36.760]  competitive globally as well. That's number one. Number two is we have a cloud computing

[10:36.760 -> 10:48.800]  business. I think Alibaba is actually probably one of the very few companies that both have proprietary in-house AI capability

[10:48.800 -> 10:50.380]  and also a cloud business.

[10:50.380 -> 10:54.520]  If you think about it, Microsoft and OpenAI are two separate companies.

[10:54.520 -> 11:00.600]  They have a very nice partnership right now, but maybe in the future, they will maybe go

[11:00.600 -> 11:02.060]  their separate ways.

[11:02.060 -> 11:06.080]  So Microsoft actually does not have their proprietary development of AI.

[11:06.560 -> 11:08.700]  They basically outsourced it to open AI.

[11:09.080 -> 11:10.860]  Amazon is in the cloud business,

[11:10.860 -> 11:13.080]  but they don't have proprietary AI

[11:13.080 -> 11:14.420]  that they developed themselves

[11:14.420 -> 11:15.960]  in terms of large language model.

[11:16.500 -> 11:18.780]  Facebook has their large language model,

[11:18.880 -> 11:20.260]  which they open source, Lama,

[11:20.680 -> 11:22.120]  but they don't have a cloud business.

[11:22.780 -> 11:24.140]  The only American company

[11:24.140 -> 11:25.200]  that has sort of both internal, in-house is Google, but Google don't have a cloud business. The only American company that has sort of both

[11:25.200 -> 11:32.380]  internal, in-house is Google, but Google is number three in cloud and AI is, you know, some say

[11:32.380 -> 11:37.560]  they're not as good as open AI, right? But so you come to China, you look at Alibaba, we're the only

[11:37.560 -> 11:45.200]  company that both run a leading cloud business and also we're competitive in AI.

[11:45.200 -> 11:49.500]  So that combination between AI and cloud is important.

[11:49.500 -> 11:50.500]  Why?

[11:50.500 -> 11:56.360]  Because anybody that uses your AI, so we have both open source versions of our AI and also

[11:56.360 -> 12:03.040]  our more proprietary version where people can access through APIs.

[12:03.040 -> 12:07.600]  Anybody that uses our AI will need to use cloud computing power.

[12:07.760 -> 12:08.540]  They need compute.

[12:09.180 -> 12:09.460]  All right.

[12:09.580 -> 12:14.940]  We also developed the largest open source AI community called Model Scope, which has

[12:14.940 -> 12:18.760]  a lot of other people's open source AIs in that marketplace.

[12:19.280 -> 12:23.380]  So when they use the open source AI within our community, they need compute.

[12:23.740 -> 12:24.880]  So they need computing power.

[12:24.980 -> 12:25.340]  That's how we can So they need computing power. That's how

[12:25.340 -> 12:31.580]  we can grow our cloud computing revenue. In the last quarter, our AI revenues in the cloud business

[12:31.580 -> 12:37.340]  have grown triple digits. So that's very exciting to us, right? So the combination of AI and cloud

[12:37.340 -> 12:45.900]  is a terrific combination. So that's the second way. The third way we're involved in AI, and it's important to us,

[12:45.980 -> 12:49.640]  is that we can apply AI in numerous vertical applications.

[12:50.260 -> 12:52.940]  And if you think about e-commerce, the scenarios in e-commerce

[12:52.940 -> 12:55.040]  in terms of recommending what to buy,

[12:55.240 -> 12:58.780]  if you want to buy something for your friends,

[12:59.380 -> 13:02.520]  you need recommendations for their birthday, let's say.

[13:03.900 -> 13:09.780]  And you want to walk into a virtual fitting room to see how some clothing fits

[13:09.780 -> 13:10.220]  on you.

[13:10.640 -> 13:12.160]  You need personal assistance.

[13:12.420 -> 13:13.540]  You need customer service.

[13:13.800 -> 13:18.620]  A lot of those things can be drastically enhanced by AI technology.

[13:19.320 -> 13:23.700]  And when we look at our use cases in e-commerce, it's incredible.

[13:23.940 -> 13:27.740]  We just see so much upside that AI could be applied.

[13:28.360 -> 13:31.120]  And so through these ways, we're involved in AI,

[13:31.220 -> 13:32.300]  and that's why we're all in.

[13:33.040 -> 13:35.800]  Right. I see that you are uniquely positioned

[13:35.800 -> 13:40.220]  given that you have both the cloud business and also the AI business.

[13:41.240 -> 13:46.360]  AliCloud is probably now the largest platform in Asia Pacific.

[13:46.360 -> 13:48.760]  So you're definitely very well positioned there.

[13:48.760 -> 13:52.680]  And I love your analogy about AI and raising child.

[13:52.680 -> 13:57.560]  Being a mother of two, I know how hard and how long it takes to raise a child.

[13:57.560 -> 14:06.840]  But you, aside from your own proprietary large language model, Tong Yi Qian, where you have also made strategic investments

[14:06.840 -> 14:11.080]  in five other large language models.

[14:11.080 -> 14:12.380]  And do you see synergy there?

[14:12.380 -> 14:14.760]  And how are you looking at the progress?

[14:14.760 -> 14:16.120]  Sure.

[14:16.120 -> 14:22.220]  There's definitely synergies in that, well, first, besides being a parent, we're uncles

[14:22.220 -> 14:27.680]  or aunties to five other large language models through which we have relationships.

[14:28.300 -> 14:31.860]  When they train their models, they have to use our cloud computing resource.

[14:32.420 -> 14:34.900]  And that helps our cloud business.

[14:35.140 -> 14:35.240]  Right.

[14:35.520 -> 14:37.820]  But it's also a way of hedging our bets.

[14:38.020 -> 14:43.940]  We've learned through the last 25 years that, you know, do you go proprietary?

[14:44.340 -> 14:46.100]  Do you go open source? Do you invest

[14:46.100 -> 14:52.360]  in somebody else? Uh, if you can afford it, a lot of people can't, if you can afford it,

[14:52.360 -> 14:59.160]  you do want to hedge the bets. AI is too important of a area where you just go one path.

[14:59.960 -> 15:09.240]  Um, I, it reminds me of, uh, Yogi Berra, Yogi Berra saying, uh, he said, when you come to a fork in the road, take it.

[15:12.640 -> 15:18.420]  I don't know if people got the joke, but when you come to a fork in the road, take it.

[15:18.800 -> 15:27.440]  So I think that's, you know, we want to be well hedged, but having said that, obviously our proprietary large language

[15:27.440 -> 15:31.380]  model is very, very important to us and a lot of resources being put into that.

[15:31.380 -> 15:32.380]  Okay.

[15:32.380 -> 15:37.640]  Well, so maybe let's pivot a little bit and talk about the challenges that Alibaba is

[15:37.640 -> 15:38.640]  facing.

[15:38.640 -> 15:39.640]  Right?

[15:39.640 -> 15:44.560]  Geopolitics clearly is getting more complicated.

[15:44.560 -> 15:48.240]  China's economy is still slowing. That's also the

[15:48.240 -> 15:56.880]  regulatory scrutiny. And of course, the competition in e-commerce is ever intensifying. How do you

[15:56.880 -> 16:06.980]  think about these issues and challenges? That's a great question. You have mentioned regulatory scrutiny, you have mentioned competition,

[16:07.780 -> 16:14.940]  geopolitics, Chinese economy. But think about it. Every Chinese technology company faces

[16:14.940 -> 16:20.500]  exactly the same issue, right? You know, all those factors. The unfortunate thing is,

[16:20.500 -> 16:25.040]  in the last three or four years, those factors, those sort of negative factors

[16:25.040 -> 16:27.260]  have become the narrative for Alibaba.

[16:27.860 -> 16:28.940]  And it shouldn't be.

[16:29.440 -> 16:30.860]  Alibaba is about growth.

[16:31.300 -> 16:33.000]  We're about technology innovation.

[16:33.620 -> 16:39.840]  We're about applying our technology into our core business to create value for our customers

[16:39.840 -> 16:42.000]  and eventually also for our shareholders.

[16:42.000 -> 16:42.820]  and eventually also for our shareholders.

[16:50.020 -> 16:50.460]  So for us, it is what it is, geopolitics or regulatory, whatever it is.

[16:55.520 -> 16:58.440]  We think we're now entering a phase where we're in a stable regulatory environment in the sense that regulation is quite predictable.

[16:58.440 -> 17:03.300]  We know what are the red lines, what we can do and cannot do.

[17:04.640 -> 17:05.380]  Competition is always, I mean, since day one, we've had competition. the red lines, what we can do and cannot do.

[17:05.380 -> 17:09.060]  Competition is always, I mean, since day one, we've had competition.

[17:09.060 -> 17:15.040]  So you can't cut costs and cut your way into competition.

[17:15.040 -> 17:18.440]  You have to have a growth mindset when you compete.

[17:18.440 -> 17:20.560]  And that's where we are.

[17:20.560 -> 17:22.360]  That's what we've decided to do.

[17:22.360 -> 17:25.320]  We decided to very much focus on our core,

[17:26.760 -> 17:29.440]  e-commerce, cloud computing, and our goal over the next 10 years

[17:29.440 -> 17:33.740]  is to be able to get back to growth,

[17:34.140 -> 17:35.780]  get back to double-digit growth.

[17:35.920 -> 17:38.120]  We have said we're sort of,

[17:38.400 -> 17:40.040]  from a planning standpoint,

[17:40.400 -> 17:41.760]  on a three-year time horizon

[17:41.760 -> 17:47.860]  by our fiscal year 2027, March end 2027, we like to be growing

[17:47.860 -> 17:53.880]  double digits. Great. I think we are holding our breath. There are many investors or shareholders

[17:53.880 -> 17:59.900]  of Alibaba in the room, and I'm sure they are delighted to hear that. So you and Jack co-founded

[17:59.900 -> 18:08.100]  Alibaba 25 years ago, and through all the two monumental shifts the past two and a half

[18:08.100 -> 18:14.880]  decades, how has your leadership evolved and maybe also vision for Alibaba?

[18:14.880 -> 18:16.760]  Yeah.

[18:16.760 -> 18:29.940]  Well when I first started at Alibaba 25 years ago, I was a specialist in finance, legal, and I became the CFO of the company. I was CFO

[18:29.940 -> 18:38.980]  of the company for 13 years. So my job was very much day-to-day. You had to look after,

[18:39.120 -> 18:48.960]  not just look after investors, you have to look at accounting, tax, audit, internal audit, you know, and just making

[18:48.960 -> 18:57.240]  sure that we have integrity in our numbers and have a well-established finance department that

[18:57.240 -> 19:10.200]  can handle all those things. And it's very much of a day-to-day job. And today, as coming back in as chairman of the company, you know, the job is very different.

[19:11.320 -> 19:17.660]  We as a group, management team as a group, including our CEO, are in the doing the work of

[19:17.660 -> 19:28.020]  allocating resources and deciding what company resources that we allocate ourselves to. That's not just money, it is also people, right?

[19:28.020 -> 19:34.660]  Who to put in which division to run which business.

[19:34.660 -> 19:39.380]  Once those decisions have been made, then you do everything, as senior management, you

[19:39.380 -> 19:45.520]  do everything to ensure that those resources can be met, the needs can be met.

[19:51.720 -> 19:51.960]  That's about making sure that you establish a team of people.

[19:54.460 -> 19:54.740]  If you appoint a CEO to a business unit,

[19:58.440 -> 20:00.000]  you want to make sure that their direct reports are good people that you help them to recruit.

[20:00.760 -> 20:03.260]  You want to establish incentive plans

[20:03.260 -> 20:09.760]  so that their personal financial situation can be tied to the performance of the company.

[20:10.940 -> 20:12.520]  Designing those plans.

[20:13.160 -> 20:16.180]  You want to make sure that they have capital resources.

[20:16.760 -> 20:18.280]  So allocation of capital is important.

[20:18.400 -> 20:27.340]  So that's the nature of the job today than it was like 15 years ago.

[20:27.340 -> 20:33.400]  So much more strategic and providing directions and visions for the company.

[20:33.400 -> 20:38.080]  I think that's too grandiose of a way to say.

[20:38.080 -> 20:42.320]  I say my job is to get out of people's way.

[20:42.320 -> 20:49.220]  Let your CEOs, let your operating management team operate and make day-to-day decisions.

[20:49.220 -> 20:52.540]  Because they're on the front line, they're closest to our users, they're closest to our

[20:52.540 -> 20:57.880]  customers, they should be in the primary position to make those decisions rather than me.

[20:57.880 -> 21:05.980]  Okay, fantastic. So maybe I would, you alluded to it, but there are probably many shareholders

[21:05.980 -> 21:07.500]  of Alibaba in the room

[21:07.500 -> 21:11.460]  and potential shareholders of Alibaba in the room.

[21:11.920 -> 21:15.280]  What would you, what can they expect of the company

[21:15.280 -> 21:18.740]  in the next one, three, five years?

[21:21.920 -> 21:30.240]  So they, I think the first thing our shareholders should expect is that management has extreme

[21:30.240 -> 21:31.240]  focus.

[21:31.240 -> 21:36.400]  We're extremely focused on our core businesses and how to create value and how to serve our

[21:36.400 -> 21:39.300]  customers, right?

[21:39.300 -> 21:43.360]  To give you an example, strategically, we have decided in our e-commerce business that

[21:43.360 -> 21:50.080]  we always put users first. Alibaba has always struggled in terms of trying to define who our

[21:50.080 -> 21:55.100]  customers are. Is it the merchants that are selling or are they the users that

[21:55.100 -> 21:59.820]  are actually purchasing your products? We have decidedly gone into users first.

[21:59.820 -> 22:05.320]  That's because they're actually eventually spending the money to benefit our merchants.

[22:06.040 -> 22:18.060]  They're actually converting it to use our internal language, converting into purchases that will create business volume for us.

[22:18.500 -> 22:19.740]  So users first.

[22:20.580 -> 22:31.600]  So having that extreme focus and having a very clear strategic direction of what's important and what's not, that's what our shareholders should expect from management.

[22:32.380 -> 22:37.500]  And we're not going to spend time on unimportant things.

[22:37.800 -> 22:44.620]  I mean, there are a lot of businesses within our portfolio right now that, to be honest, are not that important.

[22:45.260 -> 22:48.540]  So that's not going to occupy a ton of mindshare.

[22:48.540 -> 22:54.360]  The only mindshare that we will apply to that is we'll figure out ways to exit those businesses.

[22:54.360 -> 23:02.160]  So we can expect continued divestment of some of these non-core businesses from the company?

[23:02.160 -> 23:03.160]  Yeah.

[23:03.160 -> 23:07.620]  As you know, we've said in the last quarter, we've sold down non-core financial assets,

[23:07.620 -> 23:13.080]  and we declared a special dividend from the proceeds of those sales.

[23:14.000 -> 23:19.620]  I think people should expect that that's an ongoing project, and we have established

[23:20.340 -> 23:25.840]  special teams to manage that because we don't want that to distract our operating

[23:25.840 -> 23:34.960]  management team. Fantastic. So we started the conversation about sports. I'd like to end

[23:34.960 -> 23:40.920]  our discussion today by asking you a question. How did you keep yourself in this tip-top condition?

[23:40.920 -> 23:45.920]  Do you join your team and play basketball or what do you do to keep yourself

[23:47.200 -> 23:55.600]  fit? 25 years you seem to look pretty much the same. Thank you. People think when you own an

[23:55.600 -> 23:59.520]  NBA team you can actually go on the court and play with your players. That's not true.

[24:00.480 -> 24:10.900]  No, I thought that was the privilege of the owner. This is a privilege, but they would be just to try to, you know, they will reluctantly go through that.

[24:11.280 -> 24:13.120]  No, I can't force them to play with me.

[24:13.400 -> 24:16.880]  Now, I can't even force my kids to play basketball with me.

[24:16.940 -> 24:19.920]  I have two sons who both played high school basketball.

[24:20.860 -> 24:23.080]  One is 21, the other one is 17.

[24:23.520 -> 24:24.640]  They have now refused.

[24:27.820 -> 24:29.420]  I have gotten to a point where they refuse to play me because I'm not good enough.

[24:29.420 -> 24:35.880]  So to your question, I think it's very important to stay disciplined when it comes to just

[24:35.880 -> 24:44.180]  exercise, watch what you eat, and have a regular life.

[24:44.180 -> 24:50.800]  The challenge for me is I travel quite a bit. So the jet lag does get to you.

[24:52.480 -> 24:58.800]  I used to prioritize exercise over sleep. So for example, you arrive at six o'clock in the morning,

[25:00.000 -> 25:05.040]  you know, you go to the gym, right? Now I prioritize sleep over exercise.

[25:05.880 -> 25:08.480]  There's a book that I would recommend everybody to read.

[25:08.740 -> 25:10.300]  It's called Why You Sleep.

[25:11.000 -> 25:14.140]  Basically, what it says is that 99% of the people in the world

[25:14.140 -> 25:16.840]  need at least seven to eight hours of sleep.

[25:17.260 -> 25:20.060]  And if you think you're that 1%, you're not.

[25:22.680 -> 25:24.840]  I am the 99%.

[25:24.840 -> 25:25.000]  Yeah, I used to, you know, for example, I am the 99%.

[25:25.000 -> 25:26.000]  Yeah.

[25:26.000 -> 25:31.360]  I used to, for example, during a phase in the time, I still remember six months before

[25:31.360 -> 25:37.460]  our IPO in 2014, I slept three or four hours a day for six months.

[25:37.460 -> 25:42.380]  And that's not the way to live.

[25:42.380 -> 25:45.880]  You're going to crash yourself if you don't get enough sleep.

[25:45.880 -> 25:46.880]  Okay.

[25:46.880 -> 25:49.240]  Six to seven hours of sleep every day.

[25:49.240 -> 25:50.240]  No, seven to eight.

[25:50.240 -> 25:51.240]  Seven to eight.

[25:51.240 -> 25:52.240]  Okay.

[25:52.240 -> 25:57.280]  And when you sleep, we talked about this in the green room, when you sleep, keep your

[25:57.280 -> 25:58.280]  temperature low.

[25:58.280 -> 25:59.280]  Yeah.

[25:59.280 -> 26:00.280]  Yes.

[26:00.280 -> 26:01.280]  Preserve yourself better.

[26:01.280 -> 26:03.280]  Preserve yourself better.

[26:03.280 -> 26:04.280]  Yeah.

[26:04.280 -> 26:06.040]  I feel very cold on this stage.

[26:06.040 -> 26:08.040]  It's good.

[26:08.040 -> 26:09.040]  In the right condition.

[26:09.040 -> 26:12.280]  So ladies and gentlemen, if you can join me and thank Joe.

[26:12.280 -> 26:13.280]  Thank you.

[26:13.280 -> None]  Thank you.