每一波重大的科技浪潮袭来时，都会产生几家非常有价值的科技公司，有的市值甚至会达到几千亿美元。不过在这股浪潮袭来之初，通常很难判断谁才会成为“大赢家”。

然而只要你研究过科技史，你就会发现，当这些技术浪潮袭来时，有两类公司的价值和收入增长得最快：一类是生产底层系统和半导体硬件的公司，另一类是生产终端软件或应用的公司。

比如在PC时代，最有价值和盈利最多的“大赢家”是搞半导体的英特尔和做垂直应用的微软。（半导体就是计算机芯片，垂直应用就是为特定用途设计的软件。）到了移动时代，半导体和硬件端的大赢家是高通和ARM，重直应用端的大赢家则是Uber、WhatsApp和Instagram等等，它们都有了几百亿美元的市值。其它几次技术浪潮也各自催生出了自己的弄潮儿，比如网络领域的博通和游戏领域的英伟达等等。

现在，一股新的科技浪潮正在席卷世界，它就是机器学习和人工智能。它跟以往的技术浪潮大同小异，也会在半导体和终端用户垂直应用领域里催生出一批新的“大赢家”。

现在看来，在人工智能技术上，半导体领域的头号玩家是英伟达，它的图形处理芯片已经被人工智能界广泛采用。不过英伟达的芯片并未专门针对人工智能应用进行优化。除了英伟达之外，还有Cerebras、Graphcore和Groq等公司也在该领域发力，有望开辟出一个全新的细分市场。

然而正如英特尔在移动领域建树有限，英伟达也有可能错过这一新的市场趋势，毕竟它的现有芯片架构是针对其他用途（如视频游戏和其他图形处理功能）优化的。从历史经验来看，这一细分市场必然会出现一家市值几十亿美元的创业公司。从风险投资的角度看，相比于人工智能的发展前景，这一领域的投资依然是相对不足的。

而在软件方面，也必然会出现几个垂直应用领域的“大赢家”。虽然目前也有一些“横向”的公司试图构建通用的人工智能解决方案，但从短期市场来看，他们不大可能取得成功。短期最有可能成功的很可能是那些针对特定终端用户应用场景的公司。

在应用领域，很可能会有三类“大赢家”：

首先，一些拥有海量数据的互联网巨头很可能成为行业的主导者。比如谷歌、Facebook、亚马逊和苹果都等公司都大规模地部署了自己的人工智能程序，用于广告定位、搜索和语音识别等方面。这些科技巨头在人工智能领域里处于遥遥领先地位，此外他们还拥有大量专有数据，必然能给用户带来有价值的应用。

其次，目前一些新的垂直应用创业公司也正在兴起。很多公司都在利用人工智能技术在不同市场上开发新的应用，比如无人汽车领域的Cruise和Waymo、货运物流领域的Samsara、医疗领域的Color Genomics和Athelas、金融科技领域的Affirm和Stripe等等。很多公司开发的产品已显著好于目前的主流产品，因为人工智能正是这些产品的核心特色。

第三，有些非科技领域的传统企业可能会利用人工智能技术释放他们的数据潜能，这些公司也非常值得关注。各个企业的大公司都坐拥海量的数据。比如喜达屋酒店及度假村集团在房地产和酒店领域拥有庞大的业务，如果用人工智能技术挖掘这些数据的潜力，必然会在定价、信用检查、出租业务等多个方面对公司更有裨益。同理，Visa、万事达和美国运通等企业也都拥有海量的数据，可用于电商和信贷等多种用途。

可以想见，如果企业能够合理利用这些数据，就不难产生新的收入流。这很可能产生一种新的私有股权模式，即私募机构或大型风投公司买断一些传统公司，只是为了将这些公司的数据用于新的用途。

总之，与之前的几波科技浪潮一样，人工智能的真正价值应该也会集中在两类公司：一类是那些做底层硬件系统和半导体的公司，一类是那些做人工智能垂直应用的公司。这种整合很可能引起整个科技行业的连锁反应。估计在不久的未来，就会有私人投资者加倍下注那些做半导体和做AI垂直应用的公司，同时也会有一些私募公司瞄准那些坐拥大量数据的传统企业。

人工智能行业可能不会出现很多“大赢家”，而真正捕捉到这次商机的人则会实现显著的利润。（财富中文网）

本文作者埃拉德·吉尔是一位连续创业人、科技行业经理人和天使投资人。他也是Color Genomics的联合创始人之一，还是Athelas、Groq、Cerebras和Stripe等公司的投资者之一。

译者：朴成奎

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.

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