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華爾街下一場(chǎng)金融危機(jī)可能與數(shù)字化有關(guān),人工智能或可成為解決方案

THOMAS DOHMKE
2023-10-05

對(duì)于金融服務(wù)機(jī)構(gòu)以及任何仍在使用COBOL和類似過時(shí)的傳統(tǒng)軟件的機(jī)構(gòu)而言,賦予其開發(fā)人員人工智能相關(guān)能力至關(guān)重要。

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托馬斯·多梅克是GitHub的首席執(zhí)行官。 攝影:CHLOE ELLINGSON——彭博社——蓋蒂圖片社

軟件已經(jīng)成為支撐美國(guó)經(jīng)濟(jì)的基礎(chǔ)。今天,幾乎每一種消費(fèi)體驗(yàn),從自動(dòng)取款機(jī)交易到在線買入賣出股票,再到通過支票和儲(chǔ)蓄賬戶轉(zhuǎn)賬,都離不開軟件開發(fā)人員編寫、運(yùn)行和維護(hù)的數(shù)百萬行代碼。目前,許多華爾街機(jī)構(gòu)仍在使用早在半個(gè)多世紀(jì)前艾森豪威爾時(shí)期編寫的代碼,但這種代碼易受攻擊。盡管金融機(jī)構(gòu)在盡力使其代碼庫(kù)現(xiàn)代化,但我們正處于一個(gè)臨界點(diǎn),可能會(huì)使我們所知的數(shù)字經(jīng)濟(jì)遭到破壞。然而,人工智能可能是其中的解決方案。

通用商業(yè)語言(Common Business-Oriented Language, COBOL)最早創(chuàng)建于1959年。在尚未出現(xiàn)如今這樣嚴(yán)格的監(jiān)管環(huán)境之前,金融機(jī)構(gòu)作為新技術(shù)的早期采用者保持著其領(lǐng)先地位,他們?cè)诖笮陀?jì)算機(jī)部署的應(yīng)用程序中使用COBOL。如今,盡管已經(jīng)過去半個(gè)多世紀(jì),COBOL仍然是支持超過43%的金融機(jī)構(gòu)運(yùn)轉(zhuǎn)的數(shù)字基礎(chǔ)。COBOL可以處理3萬億美元的日常交易,95%的ATM刷卡,以及支票和儲(chǔ)蓄賬戶轉(zhuǎn)賬。在COBOL的全盛時(shí)期,華爾街可以進(jìn)行創(chuàng)新,但隨著Stripe、Paypal和Adyen等支付處理器成為新常態(tài),COBOL阻礙了銀行機(jī)構(gòu)的現(xiàn)代化進(jìn)程,并構(gòu)成了緊迫的安全威脅。

在網(wǎng)絡(luò)間諜活動(dòng)資金充足、行動(dòng)周密的時(shí)代,Beatlemania誕生之前生成的代碼可能會(huì)讓系統(tǒng)像紙牌屋一樣崩潰。安全威脅和漏洞在2022年猛增25%。更具體地說,COBOL容易受到一種被稱為“SQL注入”的攻擊。這是一種削弱性數(shù)據(jù)攻擊,已經(jīng)破壞了數(shù)百萬的信用卡和數(shù)據(jù)交易,使商業(yè)網(wǎng)站崩潰,并泄露了愛沙尼亞幾乎所有公民的健康記錄。由于金融機(jī)構(gòu)的大型計(jì)算機(jī)仍然基于COBOL運(yùn)行,這些威脅可能會(huì)導(dǎo)致數(shù)萬億的經(jīng)濟(jì)價(jià)值喪失,嚴(yán)重影響人們?nèi)粘5呢?cái)務(wù)穩(wěn)定。

此外,真正知道如何維護(hù)傳統(tǒng)代碼的開發(fā)人員由于年齡原因正迅速離開勞動(dòng)力隊(duì)伍。我們正在與時(shí)間賽跑,需要在人才庫(kù)萎縮之前實(shí)現(xiàn)COBOL的現(xiàn)代化。這個(gè)過程持續(xù)時(shí)間并不短,可能需要幾年甚至十年才能完成。在生成式人工智能的幫助下,這種現(xiàn)代化轉(zhuǎn)變的速度和成本可以從根本上降低,反過來又將鞏固數(shù)字經(jīng)濟(jì)的基礎(chǔ)。

在過去的一年里,生成式人工智能結(jié)對(duì)編程工具改變了軟件開發(fā)的本質(zhì),這是世界上第一批人工智能發(fā)揮勞動(dòng)力作用的例子之一。人工智能驅(qū)動(dòng)的開發(fā)工具已經(jīng)可以為開發(fā)人員完成近50%的代碼,并使他們完成工作的時(shí)間縮短近一半。在未來幾年,人工智能將為計(jì)算機(jī)編程的整個(gè)過程提供支持,可以將生產(chǎn)率提升幅度從55%迅速提高到1000%。如今,人工智能能夠解釋這些大型計(jì)算機(jī)代碼庫(kù)中的全部?jī)?nèi)容,并且能夠執(zhí)行從COBOL向Java或Golang等現(xiàn)代軟件轉(zhuǎn)型過程中開發(fā)人員需要處理的高達(dá)80%的代碼和手動(dòng)任務(wù)。隨著未來幾年使用情景和功能的擴(kuò)充,人工智能的能力只會(huì)不斷增強(qiáng)。

對(duì)于金融服務(wù)機(jī)構(gòu)以及任何仍在使用COBOL和類似過時(shí)的傳統(tǒng)軟件的機(jī)構(gòu)而言,賦予其開發(fā)人員人工智能相關(guān)能力至關(guān)重要。當(dāng)今社會(huì)依賴于開發(fā)人員的生產(chǎn)率提升,理解這些過時(shí)的編程語言的能力,以及將這些過時(shí)的代碼熟練轉(zhuǎn)換為更安全、更敏捷的代碼庫(kù)的能力。隨著各機(jī)構(gòu)支持人工智能和軟件的整合,我們可以在幾個(gè)季度內(nèi)實(shí)現(xiàn)經(jīng)濟(jì)整個(gè)數(shù)字支柱的轉(zhuǎn)變,而無需等上幾十年的時(shí)間,并且避免數(shù)萬億的經(jīng)濟(jì)活動(dòng)在此過程中受到損害。

很明顯,華爾街的下一場(chǎng)危機(jī)可能與數(shù)字化有關(guān)。然而,在人工智能的幫助下,我們有希望避免另一場(chǎng)醞釀已久的金融危機(jī)。(財(cái)富中文網(wǎng))

托馬斯?多梅克是GitHub的首席執(zhí)行官。

Fortune.com上發(fā)表的評(píng)論文章中表達(dá)的觀點(diǎn),僅代表作者本人的觀點(diǎn),不代表《財(cái)富》雜志的觀點(diǎn)和立場(chǎng)。

翻譯:郝秀

審校:汪皓

托馬斯·多梅克是GitHub的首席執(zhí)行官。 攝影:CHLOE ELLINGSON——彭博社——蓋蒂圖片社

軟件已經(jīng)成為支撐美國(guó)經(jīng)濟(jì)的基礎(chǔ)。今天,幾乎每一種消費(fèi)體驗(yàn),從自動(dòng)取款機(jī)交易到在線買入賣出股票,再到通過支票和儲(chǔ)蓄賬戶轉(zhuǎn)賬,都離不開軟件開發(fā)人員編寫、運(yùn)行和維護(hù)的數(shù)百萬行代碼。目前,許多華爾街機(jī)構(gòu)仍在使用早在半個(gè)多世紀(jì)前艾森豪威爾時(shí)期編寫的代碼,但這種代碼易受攻擊。盡管金融機(jī)構(gòu)在盡力使其代碼庫(kù)現(xiàn)代化,但我們正處于一個(gè)臨界點(diǎn),可能會(huì)使我們所知的數(shù)字經(jīng)濟(jì)遭到破壞。然而,人工智能可能是其中的解決方案。

通用商業(yè)語言(Common Business-Oriented Language, COBOL)最早創(chuàng)建于1959年。在尚未出現(xiàn)如今這樣嚴(yán)格的監(jiān)管環(huán)境之前,金融機(jī)構(gòu)作為新技術(shù)的早期采用者保持著其領(lǐng)先地位,他們?cè)诖笮陀?jì)算機(jī)部署的應(yīng)用程序中使用COBOL。如今,盡管已經(jīng)過去半個(gè)多世紀(jì),COBOL仍然是支持超過43%的金融機(jī)構(gòu)運(yùn)轉(zhuǎn)的數(shù)字基礎(chǔ)。COBOL可以處理3萬億美元的日常交易,95%的ATM刷卡,以及支票和儲(chǔ)蓄賬戶轉(zhuǎn)賬。在COBOL的全盛時(shí)期,華爾街可以進(jìn)行創(chuàng)新,但隨著Stripe、Paypal和Adyen等支付處理器成為新常態(tài),COBOL阻礙了銀行機(jī)構(gòu)的現(xiàn)代化進(jìn)程,并構(gòu)成了緊迫的安全威脅。

在網(wǎng)絡(luò)間諜活動(dòng)資金充足、行動(dòng)周密的時(shí)代,Beatlemania誕生之前生成的代碼可能會(huì)讓系統(tǒng)像紙牌屋一樣崩潰。安全威脅和漏洞在2022年猛增25%。更具體地說,COBOL容易受到一種被稱為“SQL注入”的攻擊。這是一種削弱性數(shù)據(jù)攻擊,已經(jīng)破壞了數(shù)百萬的信用卡和數(shù)據(jù)交易,使商業(yè)網(wǎng)站崩潰,并泄露了愛沙尼亞幾乎所有公民的健康記錄。由于金融機(jī)構(gòu)的大型計(jì)算機(jī)仍然基于COBOL運(yùn)行,這些威脅可能會(huì)導(dǎo)致數(shù)萬億的經(jīng)濟(jì)價(jià)值喪失,嚴(yán)重影響人們?nèi)粘5呢?cái)務(wù)穩(wěn)定。

此外,真正知道如何維護(hù)傳統(tǒng)代碼的開發(fā)人員由于年齡原因正迅速離開勞動(dòng)力隊(duì)伍。我們正在與時(shí)間賽跑,需要在人才庫(kù)萎縮之前實(shí)現(xiàn)COBOL的現(xiàn)代化。這個(gè)過程持續(xù)時(shí)間并不短,可能需要幾年甚至十年才能完成。在生成式人工智能的幫助下,這種現(xiàn)代化轉(zhuǎn)變的速度和成本可以從根本上降低,反過來又將鞏固數(shù)字經(jīng)濟(jì)的基礎(chǔ)。

在過去的一年里,生成式人工智能結(jié)對(duì)編程工具改變了軟件開發(fā)的本質(zhì),這是世界上第一批人工智能發(fā)揮勞動(dòng)力作用的例子之一。人工智能驅(qū)動(dòng)的開發(fā)工具已經(jīng)可以為開發(fā)人員完成近50%的代碼,并使他們完成工作的時(shí)間縮短近一半。在未來幾年,人工智能將為計(jì)算機(jī)編程的整個(gè)過程提供支持,可以將生產(chǎn)率提升幅度從55%迅速提高到1000%。如今,人工智能能夠解釋這些大型計(jì)算機(jī)代碼庫(kù)中的全部?jī)?nèi)容,并且能夠執(zhí)行從COBOL向Java或Golang等現(xiàn)代軟件轉(zhuǎn)型過程中開發(fā)人員需要處理的高達(dá)80%的代碼和手動(dòng)任務(wù)。隨著未來幾年使用情景和功能的擴(kuò)充,人工智能的能力只會(huì)不斷增強(qiáng)。

對(duì)于金融服務(wù)機(jī)構(gòu)以及任何仍在使用COBOL和類似過時(shí)的傳統(tǒng)軟件的機(jī)構(gòu)而言,賦予其開發(fā)人員人工智能相關(guān)能力至關(guān)重要。當(dāng)今社會(huì)依賴于開發(fā)人員的生產(chǎn)率提升,理解這些過時(shí)的編程語言的能力,以及將這些過時(shí)的代碼熟練轉(zhuǎn)換為更安全、更敏捷的代碼庫(kù)的能力。隨著各機(jī)構(gòu)支持人工智能和軟件的整合,我們可以在幾個(gè)季度內(nèi)實(shí)現(xiàn)經(jīng)濟(jì)整個(gè)數(shù)字支柱的轉(zhuǎn)變,而無需等上幾十年的時(shí)間,并且避免數(shù)萬億的經(jīng)濟(jì)活動(dòng)在此過程中受到損害。

很明顯,華爾街的下一場(chǎng)危機(jī)可能與數(shù)字化有關(guān)。然而,在人工智能的幫助下,我們有希望避免另一場(chǎng)醞釀已久的金融危機(jī)。(財(cái)富中文網(wǎng))

托馬斯?多梅克是GitHub的首席執(zhí)行官。

Fortune.com上發(fā)表的評(píng)論文章中表達(dá)的觀點(diǎn),僅代表作者本人的觀點(diǎn),不代表《財(cái)富》雜志的觀點(diǎn)和立場(chǎng)。

翻譯:郝秀

審校:汪皓

Software has formed the foundation of the United States economy. Today, nearly every consumer experience, from ATM transactions to buying and trading shares online to shifting money through your checking and savings accounts depends on millions of lines of code built, run, and maintained by software developers. Currently, many Wall Street institutions are still operating on vulnerable code written as early as the Eisenhower era over half a century ago. Despite financial institutions’ best efforts to modernize their code bases, we are now at a breaking point that could disrupt the digital economy as we know it. However, AI may be the solution.

Common Business-Oriented Language (COBOL) was first created in 1959. Prior to the rigorous regulatory environment in which they operate today, financial institutions were ahead of the curve as early adopters of new technologies, using COBOL in applications deployed on mainframe computers. Now, over half a century later, COBOL still serves as the digital foundation for over 43% of all financial institutions. COBOL processes $3 trillion of daily commerce, 95% of all ATM card swipes, and our checking and savings accounts. In its heyday, COBOL allowed Wall Street to be innovative, but as payment processors such as Stripe, Paypal, and Adyen have become the new normal, COBOL prevents our banking institutions from modernizing and represents an imminent security threat.

In the age of well-funded and sophisticated acts of cyber espionage, code generated before the birth of Beatlemania could make the system collapse like a house of cards. Security threats and vulnerabilities spiked 25% in 2022. More specifically, COBOL is prone to a form of attack called SQL injection, a debilitating data attack that has compromised millions in credit card and data transactions, crashed commerce websites, and compromised the health records of nearly all the citizens of Estonia. With the mainframes of financial institutions still operating on COBOL, these threats could wipe out trillions in economic value, severely impacting the financial stability of everyday people.

Additionally, the developers who actually know how to maintain legacy code are rapidly aging out of the workforce. We are in a race against the clock to modernize COBOL before the talent pool contracts. That process isn’t quick–and could take years if not a decade to complete. The speed and cost of this transformation can be fundamentally reduced with the aid of generative AI, which will in turn fortify the foundation of our digital economy.

In the past year, generative AI pair programming tools have changed the nature of software development, representing one of the world’s first instances of AI joining the workforce. Already, AI-powered developer tools are completing nearly 50% of code for developers and allowing them to complete work in nearly half the time. In the coming years, with AI set to power the entire cycle of computer programming, these productivity gains could quickly go from 55% to 1000%. Today, AI is capable of interpreting the entirety of these mainframe code bases and executing up to 80% of the code and manual tasks developers will need to complete the transition away from COBOL into modern software such as Java or Golang. AI’s capabilities will only increase as it continues to gain context and capabilities in the coming years.

It’s vital that financial services institutions–and anyone who is still operating on COBOL and similar outdated legacy software–empower their developers with the power of AI. Our society is now dependent on the productivity gains of developers, their ability to understand these aging programming languages, and deftly convert aging code to a more secure and agile code base. As organizations embrace the collision of AI and software, we can transform the entire digital backbone of our economy in a matter of quarters, instead of decades, and save trillions of economic activity from being compromised in the process.

It is clear that Wall Street’s next crisis could be digital. However, with the help of AI, we stand a chance of avoiding another financial crisis that has been long in the making.

Thomas Dohmke is the CEO of GitHub.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

財(cái)富中文網(wǎng)所刊載內(nèi)容之知識(shí)產(chǎn)權(quán)為財(cái)富媒體知識(shí)產(chǎn)權(quán)有限公司及/或相關(guān)權(quán)利人專屬所有或持有。未經(jīng)許可,禁止進(jìn)行轉(zhuǎn)載、摘編、復(fù)制及建立鏡像等任何使用。
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