尊敬的用户您好，这是来自FT中文网的温馨提示：如您对更多FT中文网的内容感兴趣，请在苹果应用商店或谷歌应用市场搜索“FT中文网”，下载FT中文网的官方应用。
Another week, another record high in US equity markets. Last week’s jump was triggered by the Federal Reserve’s signal that investors can look forward to more interest rate cuts this year. But deeper market bullishness is built on two things: the cash reserves of the tech giants that now dominate the markets, and belief in their ability to monetise artificial intelligence.
又是一周，美股再创新高。上周的涨幅由美联储的信号触发，该信号表明投资者今年可以期待更多次的降息。但市场更深层的看涨情绪基于两大因素：一是目前主导市场的科技巨头们手中庞大的现金储备，二是对它们通过人工智能赚钱的能力抱有坚定的信心。
AI will “change the world”, we are told. It will radically increase productivity (albeit by disrupting millions of jobs). It will create a huge new wealth pie for the world to share. And, according to a breathless ARK Invest report that last week predicted a $40tn boost to global gross domestic product from AI by 2030, it will “transform every sector, impact every business, and catalyze every innovation platform”.
我们被告知，人工智能将“改变世界”。它将根本性地提升生产率（虽然这意味着数百万岗位的颠覆）。同时，它也将创造一个庞大的新财富“蛋糕”，供全球共享。根据方舟投资(ARK Invest)上周发布的一份令人兴奋的报告，预计到2030年，人工智能将为全球国内生产总值增加40万亿美元，它将“转型每一个行业，影响每一家企业，并成为每一个创新平台的催化剂”。
It’s the euphoria and sense of inevitability in this straightforward narrative that makes me nervous. Even if you believe AI will be today’s equivalent of electricity or the internet, we are at the very early stages of a highly complex multi-decade transformation that is by no means a done deal. Yet valuations are pricing in the entire sea change, and then some. A February report by Currency Research Associates pointed out that it would take 4,500 years for Nvidia’s future dividends to equal its current price. Talk about a long tail.
这种直接叙述中的狂喜和必然感让我感到紧张。即使你相信人工智能将成为当今的电力或互联网的等价物，我们仍处于一个高度复杂的、跨几十年的转型的早期阶段，这绝不是一个已经确定无疑的事情。然而，市场估值已经预期了整个巨变，甚至更多。货币研究协会(Currency Research Associates)在2月份的一份报告指出，英伟达(Nvidia)未来的股息需要4500年才能与其当前价格相等。这真是一个长尾(long tail)。
While Nvidia isn’t Pets.com — it has tangible revenues from selling real things — the overall AI narrative depends on many uncertain assumptions. For example, AI requires huge amounts of water and energy. There’s a push in both the US and EU to get companies to disclose their usage. Whether via carbon pricing, or a tax on resource usage, it’s quite likely that those input costs will rise significantly in the future.
虽然英伟达并非Pets.com——它通过销售实物获得了实实在在的收入——但整个人工智能的发展依赖于许多不确定的假设。例如，人工智能需要大量的水和能源。美国和欧盟都在推动企业公开其资源使用情况。无论是通过碳定价还是对资源使用征税，这些投入成本在未来都有可能大幅上升。
Likewise, AI developers don’t now have to own the copyright to content on which the models are trained. They don’t have to make profits on AI itself, of course; the assumption of future gains is enough to fuel the froth. Relentless techno-optimism and the illusion of inevitability is how Silicon Valley creates paper wealth. But remember, many of the proponents of “AI everywhere” were touting web3, crypto, the metaverse and the benefits of the gig economy not so long ago.
同样，人工智能开发者现在不必拥有其模型训练所依赖内容的版权。他们自然不需要直接从人工智能本身获利；仅仅对未来潜在收益的预期就足以推动这股热潮。不懈的技术乐观主义和对未来不可避免性的错觉是硅谷创造纸面财富的手段。但请记住，那些现在鼓吹“人工智能无所不在”的人，不久前还在大力推广web3、加密货币、元宇宙以及零工经济的种种好处。
One big difference, of course, is that AI has been validated by huge, cash-rich, market-leading companies such as Microsoft, Google and Amazon. But even within those companies developers have their doubts. One senior staffer at a leading AI company recently admitted to me, when pushed, that the profit assumptions around the technology were based “more on speculation than substance”, and that it has major kinks still to be worked out.
当然，一个显著的区别是，人工智能已经得到了像微软(Microsoft)、谷歌(Google)和亚马逊(Amazon)这样的大型、资金充足、市场领先的公司的认可。然而，即使在这些公司内部，开发者也存在疑虑。一位在一家领先的人工智能公司的高级员工最近在我追问下承认，关于这项技术的盈利预期“更多的是基于猜测，而非实质”，并且它还有一些重大的问题有待解决。
Anyone who’s experimented with large language models can vouch for this. I wouldn’t rely on a chatbot when doing research for my own work because I don’t want to worry about the accuracy of the data I’m being fed. I also don’t want to give up my ability to curate my own informational inputs. (I’d much rather do a Google search and see sources and citations laid out.)
任何体验过大型语言模型的人都会同意这一点。我在进行个人工作研究时，不会依赖聊天机器人，因为我不想担心我所接收数据的准确性。我也不想放弃自己筛选信息输入的权力。（我更倾向于进行谷歌搜索，查看资料来源和引用。）
I’m admittedly operating at the high end of the white-collar job spectrum. But even for more rote middle-market tasks, there are lots of questions about how to integrate AI into workflows, and whether it will really be more productive than the humans it may replace. And the humans are beginning to revolt. The Hollywood writers’ strikes were at their core about control of AI, and unions are taking on the issue of technology regulation more broadly.
无可否认，我所从事的属于白领工作的高端领域。但即便对于相对机械的中端市场任务，关于如何将人工智能整合进工作流程，以及它是否真能比它可能取代的人类更高效，都存在诸多疑问。而且，人类已经开始抗议了。好莱坞编剧的罢工在本质上是对人工智能控制权的争夺，而工会则在更广泛地对技术监管问题发起挑战。
Meanwhile, the copyright backlash against AI is gaining steam. Last week, French regulators fined Google €250mn for failing to notify news publishers that it was using their articles to train its AI algorithms, and for not licensing fair deals. This follows similar suits against OpenAI and Microsoft brought by the New York Times. As AI works its way into proprietary corporate data sets, opportunities for litigation over copyright will increase, and possibly even dovetail with worker complaints over corporate surveillance.
与此同时，针对人工智能的版权反弹正在加剧。上周，法国监管机构对谷歌罚款2.5亿欧元，原因是谷歌未向新闻出版商通知其正在使用他们的文章来训练其人工智能算法，也未就公平交易达成许可。此前，纽约时报(New York Times)对OpenAI和微软提起了类似的诉讼。随着人工智能逐渐渗透到企业专有数据集，版权诉讼的机会将会增加，甚至可能与员工对企业监控的投诉相交织。
Then there’s the monopoly problem. As Meredith Whittaker, president of the Signal Foundation and the co-founder of the AI Now Institute, wrote in 2021, modern AI advances are “primarily the product of significantly concentrated data and compute resources that reside in the hands of a few large tech corporations”. Our increasing reliance on such AI, Whittaker added, “cedes inordinate power over our lives and institutions to a handful of tech firms”.
然后是垄断问题。正如信号基金会(Signal Foundation)主席、AI Now Institute的联合创始人梅雷迪思•惠特克(Meredith Whittaker)在2021年所写，现代人工智能的进步“主要是集中在少数几家大型科技公司手中的数据和计算资源的产物”。惠特克补充说，我们对这种人工智能的日益依赖，“将我们生活和机构的过度权力拱手让给了少数几家科技公司”。
The so-called Magnificent Seven companies have driven AI enthusiasm and stock market gains over the past year. They have pushed the concentration of the S&P 500 to a historic extreme. But as a recent Morgan Stanley Wealth Management report notes, “index concentration has historically proved self-correcting, with some combination of regulatory, market and competitive forces, along with business cycle dynamics, undermining static leadership”. The report says “analysis suggests that equity returns have typically struggled following peaks in concentration”.
过去一年中，被称为“瑰丽七股”的公司推动了对人工智能的热情，并带动了股市的上涨，使得标普500指数的集中度达到了历史高点。但如摩根士丹利财富管理最近的报告所指出，“指数的集中度历史上往往能自行调整，通过监管、市场和竞争等力量的结合作用，以及商业周期的变化，来削弱固定的领先地位。”该报告还指出，“分析显示，在集中度达到顶峰之后，股权回报率通常会遇到挑战。”
That combination of correcting factors might include the growing number of Big Tech antitrust cases and the possibility that carbon pricing and copyright fines will challenge the “free” inputs necessary to make a profit.
这些可能的纠正因素包括大型科技公司反垄断案件的增加，以及碳定价和版权罚款对于实现利润所需“免费”输入材料构成的挑战。
Whether you see AI as the next tulip bubble or the next combustion engine, it’s worth questioning how the market is pricing this story.
无论你视人工智能为下一场郁金香泡沫，还是认为它将成为下一代内燃机，质疑市场如何给这个故事定价都是有价值的。