人工智能越來越熱,誰能賺大錢?
每一波重大的科技浪潮襲來時,都會產生幾家非常有價值的科技公司,有的市值甚至會達到幾千億美元。不過在這股浪潮襲來之初,通常很難判斷誰才會成為“大贏家”。 然而只要你研究過科技史,你就會發現,當這些技術浪潮襲來時,有兩類公司的價值和收入增長得最快:一類是生產底層系統和半導體硬件的公司,另一類是生產終端軟件或應用的公司。 比如在PC時代,最有價值和盈利最多的“大贏家”是搞半導體的英特爾和做垂直應用的微軟。(半導體就是計算機芯片,垂直應用就是為特定用途設計的軟件。)到了移動時代,半導體和硬件端的大贏家是高通和ARM,重直應用端的大贏家則是Uber、WhatsApp和Instagram等等,它們都有了幾百億美元的市值。其它幾次技術浪潮也各自催生出了自己的弄潮兒,比如網絡領域的博通和游戲領域的英偉達等等。 現在,一股新的科技浪潮正在席卷世界,它就是機器學習和人工智能。它跟以往的技術浪潮大同小異,也會在半導體和終端用戶垂直應用領域里催生出一批新的“大贏家”。 現在看來,在人工智能技術上,半導體領域的頭號玩家是英偉達,它的圖形處理芯片已經被人工智能界廣泛采用。不過英偉達的芯片并未專門針對人工智能應用進行優化。除了英偉達之外,還有Cerebras、Graphcore和Groq等公司也在該領域發力,有望開辟出一個全新的細分市場。 然而正如英特爾在移動領域建樹有限,英偉達也有可能錯過這一新的市場趨勢,畢竟它的現有芯片架構是針對其他用途(如視頻游戲和其他圖形處理功能)優化的。從歷史經驗來看,這一細分市場必然會出現一家市值幾十億美元的創業公司。從風險投資的角度看,相比于人工智能的發展前景,這一領域的投資依然是相對不足的。 而在軟件方面,也必然會出現幾個垂直應用領域的“大贏家”。雖然目前也有一些“橫向”的公司試圖構建通用的人工智能解決方案,但從短期市場來看,他們不大可能取得成功。短期最有可能成功的很可能是那些針對特定終端用戶應用場景的公司。 在應用領域,很可能會有三類“大贏家”: 首先,一些擁有海量數據的互聯網巨頭很可能成為行業的主導者。比如谷歌、Facebook、亞馬遜和蘋果都等公司都大規模地部署了自己的人工智能程序,用于廣告定位、搜索和語音識別等方面。這些科技巨頭在人工智能領域里處于遙遙領先地位,此外他們還擁有大量專有數據,必然能給用戶帶來有價值的應用。 其次,目前一些新的垂直應用創業公司也正在興起。很多公司都在利用人工智能技術在不同市場上開發新的應用,比如無人汽車領域的Cruise和Waymo、貨運物流領域的Samsara、醫療領域的Color Genomics和Athelas、金融科技領域的Affirm和Stripe等等。很多公司開發的產品已顯著好于目前的主流產品,因為人工智能正是這些產品的核心特色。 第三,有些非科技領域的傳統企業可能會利用人工智能技術釋放他們的數據潛能,這些公司也非常值得關注。各個企業的大公司都坐擁海量的數據。比如喜達屋酒店及度假村集團在房地產和酒店領域擁有龐大的業務,如果用人工智能技術挖掘這些數據的潛力,必然會在定價、信用檢查、出租業務等多個方面對公司更有裨益。同理,Visa、萬事達和美國運通等企業也都擁有海量的數據,可用于電商和信貸等多種用途。 可以想見,如果企業能夠合理利用這些數據,就不難產生新的收入流。這很可能產生一種新的私有股權模式,即私募機構或大型風投公司買斷一些傳統公司,只是為了將這些公司的數據用于新的用途。 總之,與之前的幾波科技浪潮一樣,人工智能的真正價值應該也會集中在兩類公司:一類是那些做底層硬件系統和半導體的公司,一類是那些做人工智能垂直應用的公司。這種整合很可能引起整個科技行業的連鎖反應。估計在不久的未來,就會有私人投資者加倍下注那些做半導體和做AI垂直應用的公司,同時也會有一些私募公司瞄準那些坐擁大量數據的傳統企業。 人工智能行業可能不會出現很多“大贏家”,而真正捕捉到這次商機的人則會實現顯著的利潤。(財富中文網) 本文作者埃拉德·吉爾是一位連續創業人、科技行業經理人和天使投資人。他也是Color Genomics的聯合創始人之一,還是Athelas、Groq、Cerebras和Stripe等公司的投資者之一。 譯者:樸成奎 |
Every major technology wave yields a small number of extremely valuable companies worth tens to hundreds of billions of dollars. Often it’s difficult to predict who the big winners will be when a major new technology emerges. However, if one studies the history of technology, the value and revenue of most technology waves tend to accumulate in two types of companies: those that produce underlying systems or semiconductors, and those that make end-user software or applications. For example, in the PC-era, Intel, which manufactured semiconductors, and Microsoft, which created vertical applications such as Microsoft Office, were the most valuable and profitable winners. (Semiconductors are computer chips and vertical applications are software designed for customized purposes.) In the mobile era, Qualcomm and ARM benefited on the semiconductor and hardware side, while vertical applications like Uber, WhatsApp, and Instagram emerged as companies valued in the tens of billions. Other technology waves that created major semiconductor companies include networking (Broadcom) and gaming (Nvidia). A new technology wave is currently sweeping the world—that of machine learning and artificial intelligence (A.I.). This new technology is likely to follow a similar course, in that the winners in the market will include both semiconductor companies and end-user vertical application companies. Right now, the dominant player in semiconductors for A.I. is Nvidia, whose graphic processing chips have been mostly adopted by the A.I. community. However, Nvidia chips are not optimized for A.I. applications, and a raft of new players, including Cerebras, Graphcore, and Groq, have risen to challenge the incumbent and to carve out a whole new market segment. Just as Intel never quite got its footing in mobile, it is possible Nvidia will also miss the new market trends due to its existing chip architecture being optimized for different purposes (video game and other graphics processing). If history is a guide, a startup will emerge with tens of billions of dollars of market capitalization in this segment. This area is underinvested in from a venture capital perspective, relative to its potential upside. On the software side, vertical applications should win again as well. While there are a number of “horizontal” companies trying to build general purpose A.I., they are likely to fail in the short-term market. The most probable winners in A.I. in the short run are likely to be companies that harness its power for specific end-user applications. There are likely to be three types of winners: First, expect large, data-rich Internet incumbents to dominate. Companies like Google, Facebook, Amazon, and Apple have already been deploying A.I. at scale for ad targeting, search, and voice recognition. These technology companies are far ahead of the curve and have proprietary datasets that they can use to find valuable applications for users. Second, new vertical application startups are emerging. Companies are using A.I. to build new types of applications in various markets, including autonomous vehicles (such as Cruise and Waymo), trucking and logistics (Samsara), health care (Color Genomics, Athelas), and fintech (Affirm, Stripe). A number of these companies will create products significantly better than those of existing vertical incumbents, since A.I. will be at the core of their offerings, versus tacked on. Third, look out for non-tech incumbents adopting A.I. tech to unlock their data. Large companies spanning industries are sitting on treasure troves of data. For example, Starwood Hotels & Resorts has an amazing footprint in real estate and hotels, and can use that dataset smartly for everything from pricing to credit checks on leases. Similarly, Visa, Mastercard, and American Express are sitting on massive datasets that can be applied to various consumer uses in e-commerce and credit. One can imagine these datasets generating new revenue streams if properly leveraged. This may lead to a new private equity model in which PE or large venture firms buy out incumbent companies with the idea of unlocking their data for new uses. As in prior technology waves, the real value should accumulate into a handful of companies: those building the underlying hardware systems and semiconductors and those far building vertical applications of A.I.. This consolidation will likely have ripple effects across the tech industry. Look for private investors to double down on companies producing semiconductors and A.I.-driven applications, and for PE companies to target large incumbents harboring considerable amounts of data. The A.I. wave may have few winners, but those who do catch it are likely to profit considerably. Elad Gil is a serial entrepreneur, technology executive, and angel investor. He is a co-founder of Color Genomics, and an investor in Athelas, Groq, Cerebras, and Stripe. |