What the Finance Industry Tells Us About the Future of AI

AI在公眾認知中的迅速崛起，讓許多人好奇由AI主導的未來是什么樣的。AI會帶來各行各業(yè)的變革嗎？如果是這樣，各行各業(yè)將更民主化還是更集中化？AI會帶來更好還是更壞的結(jié)果？在過去10年里，日益強大的計算能力和豐富的數(shù)據(jù)不斷驅(qū)動著AI改造金融世界，我們可以在這里找到基礎(chǔ)答案。在AI主導的未來中，金融業(yè)的發(fā)展歷程既鼓舞人心，又發(fā)人深省。它表明，AI將為一些(但不是全部)行業(yè)帶來變革，它將最大程度地惠及大型企業(yè)，而且，就像它讓企業(yè)個體變得更敏捷一樣，AI可能會讓世界變得更遲鈍。

The meteoric rise of artificial intelligence (AI) in the public conscience has caused many people to question what an AI-dominated future looks like. Will AI transform industries? If so, will it democratize or consolidate them? Will it create better or worse outcomes? Outlines of answers can be found in the world of finance which has been transformed in the last decade by the same forces driving AI: the diffusion of ever more powerful computing and the profusion of data. The experience of finance is both encouraging and sobering for an AI-dominated future. It suggests that AI will transform some (but not all) industries, that it will benefit larger players most, and that just as it makes individual players smarter, it may make the world dumber.

信息處理是金融市場的核心功能，因此金融世界就像是探索AI潛在影響的實驗室。各類金融機構(gòu)都在技術(shù)和數(shù)據(jù)方面投入巨資，數(shù)額遠遠領(lǐng)先于其他行業(yè)，從而最大化提高競爭效率。當然，新型大型語言模型在過去六個月里給世界留下深刻印象，僅憑金融業(yè)的經(jīng)驗可能并不能完全體現(xiàn)它的能力。但如果談到AI成本下降和廣泛應用對于大多數(shù)行業(yè)意味著什么，從過去10年金融領(lǐng)域不斷變化的競爭態(tài)勢中可窺一斑。無論更新迭代的AI版本如何發(fā)揮作用，金融業(yè)永遠會第一個感知到經(jīng)濟領(lǐng)域的預警信號。

The world of finance is an obvious laboratory for exploring the potential effects of AI because information processing is the central function of financial markets. Unsurprisingly, financial institutions of all types invest heavily in technology and data well ahead of other industries in order to compete most effectively. Of course, the experience of finance may not fully illuminate the scope of newer?large language models?that have so impressed the world in the last six months. But the changing competitive dynamics within finance over the last decade provide clues about what will happen across many industries when AI becomes cheaper and more widely available. And regardless of how these newer versions of artificial intelligence play out, finance will always to be the canary in the coal mine for the rest of the economy.

首先，AI可以非常迅速地顛覆行業(yè)動態(tài)，這一點是顯而易見的。以資管行業(yè)為例，在過去的15年里，我們見證了兩次重大危機，這些危機源于技術(shù)和數(shù)據(jù)逐漸顯露優(yōu)勢。首先，基金行業(yè)經(jīng)歷了被動型基金經(jīng)理(即投資基于指數(shù)而非基于分析的基金經(jīng)理)的崛起，以及主動型基金經(jīng)理(即選股者)的衰落。數(shù)據(jù)和技術(shù)的加持使被動投資更具競爭力，主動投資經(jīng)理更難獲得信息優(yōu)勢，局勢迅速轉(zhuǎn)變。僅在過去8年里，被動管理資產(chǎn)與主動管理資產(chǎn)的比例就從0.6上升至1.2，市場份額發(fā)生了巨大變化。在此之前，主動型基金經(jīng)理收取的高額管理費相當于所管理資產(chǎn)的一個百分點以上，如今這項收入已受到重創(chuàng)，因為被動型基金經(jīng)理證明，他們僅需十分之一的成本，就有能力做到與許多主動型基金管理策略相差無幾。

First, it appears clear that AI can disrupt industry dynamics very quickly. Consider the asset management industry. Over the last 15 years, we have witnessed two significant disruptions that can be traced to the growing dominance of technology and data. First, the mutual fund industry has seen the rise of passive fund managers (i.e., managers who invest in indices with no analysis) and the decline of active fund managers (i.e., stock pickers). This shift has occurred remarkably quickly as data and technology made passive investing more competitive and made it more difficult for active managers to attain informational edges. In the last eight years alone, the?ratio of passively-managed assets to actively managed assets has risen from 0.6 to 1.2?— a dramatic shift in market share. The ability of active fund managers to extract large fees (upwards of one percentage point of assets under management) has been?clobbered?as passive fund managers demonstrated their ability to approximate many active fund management strategies at one-tenth of the cost.

其次，量化投資逐漸取代了以基本面為導向的傳統(tǒng)多空策略，對沖基金行業(yè)因此發(fā)生了變化。傳統(tǒng)經(jīng)驗中，作出多空投資決策需要更慢、更深入的分析，快速分析大量數(shù)據(jù)并創(chuàng)建相對短期策略的能力似乎已經(jīng)推倒前浪。金融領(lǐng)域的這些趨勢表明，AI主導的未來中，成敗僅在一瞬間。

Second, the hedge fund industry has been transformed by the?growing dominance?of quantitative investing over traditional, fundamentals-driven long-short strategies. The ability to analyze large amounts of data quickly and create relatively short-term strategies appears to be beating the slower and deeper analysis that traditionally led to long and short investment decisions. These trends in finance suggests that an AI-dominated future can create outsized winners and losers in very short order.

金融界的經(jīng)驗也表明，并非所有變化都像人們預想的那樣快。雖然金融交易瞬息萬變，導致宏觀經(jīng)濟、市場情緒及公司特定信息迅速發(fā)生變化，但財富管理和信貸行業(yè)受到的影響相對較小。

At the same time, the experience of the financial world suggests that not everything changes as quickly as people predict. While the high-frequency world of financial trading with its confluence of macroeconomic, sentiment, and company-specific information has changed rapidly, the lower frequency worlds of wealth management and lending have changed considerably less.

備受期待的機器人顧問曾使龐大的金融咨詢機構(gòu)黯然失色，但這種侵蝕似乎已經(jīng)停滯，甚至也許正在逆轉(zhuǎn)。看來金融業(yè)的客戶們?nèi)匀桓鼝廴祟?。同樣，AI對信貸的影響也沒有預想的那么大，AI貸款機構(gòu)面臨著相當大的問題。處理個人和企業(yè)信貸數(shù)據(jù)增量并不會像泛金融業(yè)一樣有那么龐大的需求。

The much anticipated ability of robo-advisors to eclipse the massive financial advisory complex?has appeared to stall and may be reversing. It appears that the client side of finance retains a preference for humans. Lending, similarly, has not been transformed by AI nearly as much as was predicted and AI-powered lenders have faced?considerable problems. The incremental amount of additional data to be processed on individuals and business credit may just not be as large or as useful as in financial markets broadly.

看起來，AI顛覆行業(yè)動態(tài)的力量與其處理信息問題的性質(zhì)密切相關(guān)。金融市場面對多維信息問題，需要大量的數(shù)據(jù)和計算能力。在具有類似性質(zhì)的領(lǐng)域，如藥物設計領(lǐng)域，AI應用或已成熟。但在許多其他領(lǐng)域，如服務業(yè)和制造業(yè)的一些細分領(lǐng)域，似乎與AI沒有這樣的相關(guān)性，更像財富管理或信貸行業(yè)那樣。金融業(yè)的經(jīng)驗表明，對于面向人類的服務，數(shù)據(jù)沒那么豐富，變化又十分迅速，那么就可以在人工智能的世界里很大程度上完整保留下來。需要明確的一點是，AI仍然可以通過改善決策產(chǎn)生巨大影響，但就像在財富管理和信貸領(lǐng)域中那樣，AI帶來的改變是漸進式的，并非像它在資管領(lǐng)域所作出的顛覆式影響。

The power of AI to disrupt industry dynamics appears to be tightly connected to the nature of the information problems being solved. Financial markets are a multi-dimensional information problem that requires massive amount of data and computing power. Fields with similar properties, like drug design, may be ripe for AI disruption. But many fields, including those in the services sector and manufacturing, simply may not have the same relevance for AI — they may be more like wealth management or lending. The experience of the finance industry suggests that human-facing services where data is not abundant and fast-changing can remain largely intact in a world of AI. To be clear, AI can still have a?large impact by improving decision making?but it is more likely to be incremental (as it has been in wealth management and lending) rather than transformational (as it has been in money management).

金融世界也可以幫助我們了解AI是使各行業(yè)更民主化還是更集中化。這個答案似乎比較明晰。在AI發(fā)揮關(guān)鍵作用的領(lǐng)域如金融市場，規(guī)模和速度似乎是成功的關(guān)鍵決定因素。當技術(shù)和數(shù)據(jù)占據(jù)主導地位時，成功者會越來越成功，針對技術(shù)和數(shù)據(jù)進行投資的實力是拉開差距的關(guān)鍵。相對于老牌企業(yè)，規(guī)模較小的量化基金在獲取數(shù)據(jù)源和計算能力方面面臨重大挑戰(zhàn)。同樣，被動投資的收入也會繼續(xù)下降，因為參與者規(guī)模越大，與投資者分享越多規(guī)模效益，新入局者就會被排擠。。對于AI影響下變化最大的經(jīng)濟領(lǐng)域，可預測規(guī)模的大小是決定性因素，如果說大量足以挑戰(zhàn)老牌企業(yè)的小型企業(yè)會逐漸涌現(xiàn)出來的話，未免有些言過其實了。

The world of finance can also help us understand if AI will be democratizing or consolidating. Here, it appears that the answer is less equivocal. Where AI has been pivotal (i.e., in financial markets), scale and speed appear to be the critical determinants of success. When technology and data come to dominate, winners keep winning and the ability to invest in technology and data is the key differentiator. A smaller quant fund has significant challenges in acquiring data feeds and computing power relative to established players. Similarly, fees for passive investing just continue to decrease as larger players share the benefits of scale with investors thereby boxing out upstarts. For sectors of the economy where AI is transformational, scale can be expected to be determinative and hopes for a great unleashing of smaller players that challenge established players appear to be overstated.

關(guān)于AI是否對人類有益，金融業(yè)的經(jīng)驗能告訴我們什么？在這方面，金融界的經(jīng)驗更為發(fā)人深省。那些業(yè)績不盡人意卻收取高額費用的主動型經(jīng)理被取代，似乎是令人拍手稱快的進步。但同時，金融市場的核心任務——即信息處理——似乎沒有做得更好，而且可能會變得更糟。有意忽視信息的被動投資者和沉迷量化基金的投資者數(shù)量不斷增加，這意味著處理緩慢、模糊、公司特定信息的艱巨工作或?qū)⒃庥龊鲆?。隨著數(shù)據(jù)和計算逐漸占據(jù)主導地位，各行業(yè)可能會過度依賴快速變化的硬性數(shù)據(jù)，例如股票價格變動、實時信用卡消費數(shù)據(jù)。與此同時，軟性數(shù)據(jù)，如公司的未來前景、管理質(zhì)量、定價策略的長期后果，即使它們對市場更加重要，也可能被削弱和邊緣化。

What can the finance industry’s experience tell us about whether AI is good for humans? Here, the experience of the world of finance is more sobering. The displacement of active managers who were charging large amounts for little excess performance seems like a positive development that is worth cheering. At the same time, it does not appear that financial markets are doing their central task — the processing of information — much better and?it could be getting worse. The rise of investors that either willfully ignore information (passive investors) or obsess about fast-changing information (quant funds) means that the hard work of processing slow-moving, ambiguous, firm-specific information may be getting neglected. As data and computing come to dominate, industries may come to rely excessively on hard data that is fast-changing (e.g., stock price movements, real time credit card data on spending). Meanwhile, softer data (e.g., the future prospects of firms, the quality of management, the longer run consequences of pricing strategies) can be subordinated and diminished — even if it is what really matters for markets.

總結(jié)起來恐怕就是，AI最擅長的是以非結(jié)構(gòu)化方式分析硬性數(shù)據(jù)的能力，正如它對金融市場做的那樣，有望在許多方面改變世界。但這種轉(zhuǎn)變可能僅限于數(shù)據(jù)豐富且變化迅速的環(huán)境。此外，能夠?qū)τ嬎隳芰蛿?shù)據(jù)獲取進行投資，以制定差異化戰(zhàn)略的大型公司有望成為最終贏家。即使軟性數(shù)據(jù)從長期來看是最重要的，但對其進行考量的能力的溢價在短期內(nèi)可能會下降。

I fear this last lesson may generalize particularly well. The ability to analyze hard data in unstructured ways that are not directed by humans — the hallmark of AI — promises to transform the world in many ways, just as financial markets have been. But that transformation may be limited to settings where data is abundant and fast-changing. Moreover, the winners will be the largest firms able to invest in the computing power and data to create differentiated strategies. And the premium on the ability to consider softer data could fall in the short run even if, ultimately, it is what matters the most.

金融市場如何利用人工智能奇跡，而不忽視這些更根本的問題，這件事情有解嗎？目前達成的一個平衡點是，金融市場由提供相對廉價的大宗商品服務的大型企業(yè)主導，卻忽視了對軟性信息的處理。金融界，或許也是我們所有從業(yè)者，面臨的挑戰(zhàn)是要記住，管理者和領(lǐng)導者面臨的最棘手的問題并不完全是由硬性數(shù)據(jù)決定的。我的企業(yè)如何在10年內(nèi)取得成功？我怎樣才能最有效地部署資本，不斷創(chuàng)新，創(chuàng)造出能夠更好地服務客戶的產(chǎn)品和服務？硬數(shù)據(jù)能夠為這些決策提供依據(jù)，但無法完全起決定性作用。決策需要想象力和信念。當AI使利用硬數(shù)據(jù)變得更平價、更高效，判斷力會越來越重要。承認這些人的問題排在首位，并不會減少AI對我們的幫助，而只是強調(diào)了AI僅僅是一種技術(shù)，而對管理者和投資者而言，從根本上來說，人為的努力才能換取最大的回報。

Can financial markets figure out how to capitalize on the wonders of AI and not neglect these more fundamental issues? The current equilibrium appears to be a financial market dominated by large players providing commodity services relatively cheaply but that neglects the processing of softer information. The challenge for the world of finance — and perhaps all of us — is to remember that the hardest questions facing managers and leaders are not entirely determined by hard data. What will allow my enterprise to succeed in 10 years? How can I deploy capital most effectively so that we innovate to create products and services that can serve our customers better? Hard data will inform these decisions but it is unlikely to be entirely dispositive. These decisions require acts of imagination and conviction. Just as the ability to use hard data cheapens and becomes more efficient via AI, it is these acts of judgment that will rise in importance. To acknowledge the primacy of these human questions does not diminish how much AI can help us — it simply reasserts that AI is merely a technology and that the greatest rewards for managers and investors rests in these fundamentally human endeavors.

米希爾· A·德賽?是哈佛商學院瑞穗金融集團金融學教授和哈佛法學院法學教授。