上周《金融时报》报道称，OpenAI的收入接近500亿美元。这是一个巨大的数字，尽管比《Axios》上周报道的700亿美元低得多。科技股下跌。记者们对数据掌握不力，投资者们抱怨。科技股上涨。如果投资者能如此轻易被左右，他们对投资的掌控也必然不力，博通首席执行官霍克·谭（Hock Tan）如此抱怨。博通这家芯片制造商的首席执行官曾将市场比作赌场，正如《彭博社》所写。
Last week the Financial Times wrote that OpenAI’s revenue was nearing $50bn. A massive number, though massively lower than the $70bn reported by Axios the week before. Tech stocks fell. Journalists were not on top of their figures, cried investors. Tech stocks rose. If investors could be swayed so easily, they were not on top of their investments, cried Hock Tan. The boss of Broadcom, a chipmaker, had compared markets to a casino, wrote Bloomberg.
科技股下跌——并且一直持续下跌。市场或许曾经一飞冲天。但当涉及到真正重要的数字时，他们一直在盲目飞行。激进的变革一直难以计量。20世纪80年代的并购浪潮留下了充满商誉的公司资产负债表——这是公司资产账面价值与其实际收购价之间的问题所在。20世纪90年代和2000年代，衡量其他无形资产如品牌和专利更是让人头疼。
Tech stocks fell—and have continued to do so. Markets may have been flying. But when it comes to the numbers that matter, they have been flying blind. Radical change has always been hard to count. The merger wave of the 1980s left corporate balance-sheets full of goodwill, that problematic wedge between the book value of a company’s assets and what it was bought for. Measuring other intangible assets like brands and patents was an even bigger head-scratcher in the 1990s and 2000s.
2010年代股票基础报酬的兴起进一步模糊了公司健康状况的图景，尤其是对科技公司而言。每一次投资热潮都伴随着一批缩写词、调整后的数字和估值技术，有时旨在澄清，有时则旨在掩盖。如今的一个问题是，OpenAI和Anthropic所报告的收入实际上根本不是收入。相反，它们只是对硅谷一个众所周知的模糊指标——ARR的模糊演绎。
The rise of stock-based compensation in the 2010s further obscured the picture of corporate health, particularly for tech firms. Every investment boom brings with it a rogues’ gallery of acronyms, adjusted numbers and valuation techniques, sometimes designed to elucidate and other times to obscure. One problem today is that the reported revenues of OpenAI and Anthropic are not really revenues at all. Rather, they are vague riffs on a famously vague Silicon Valley metric: ARR.
“A”代表“年度”，在某种意义上与公历有关（过去一个月的销售额乘以12）。第一个“R”传统上代表“经常性”，这仅仅意味着公司希望这些收入能够持续。“收入”，即第二个“R”？
The “A” is for “annual”, in the sense of bearing some relationship to the Gregorian calendar (sales during the past month are multiplied by 12). The first “R” traditionally stands for “recurring”, which means only that the company hopes they will continue. And “revenue”, the second “R”?
Anthropic 将通过亚马逊等云服务提供商销售其模型的收入计入其中，而 OpenAI 则仅在扣除合作伙伴分成后才这样计算。在两家公司最终上市之前，投资者陷入了一段奇怪的过渡期：即便是关于仅具暗示意义的数字的最微小消息，也会震动整个市场。10 月 14 日，Anthropic 据称向投资者透露，其在第三季度的收入为 168 亿美元，并且根据其自身调整后的指标，该公司实现了盈利。
Anthropic includes sales of its models made through cloud providers like Amazon, whereas OpenAI does so only after subtracting its partners’ cut. Until the pair eventually list, investors are trapped in a strange interregnum where the faintest suggestion of news about numbers that are themselves merely suggestive shakes the entire market. On October 14th Anthropic reportedly told investors that it had revenues of $16.8bn during the third quarter and that, on its own adjusted measure, it was profitable.
然而，该公司自 7 月以来的销售额增长了多少仍不明确——当时其年经常性收入（ARR）为 650 亿美元。当投资者不知道应将新数据与什么进行比较时，这些新数字几乎毫无用处。古怪的财务状况理应伴随着古怪的估值。投资公司 Coatue 认为，这些实验室的价值应为其税前、利息、折旧及摊销前利润（EBITDA）的若干倍——也就是说，不计利息、税项、折旧和摊销，也不计训练模型的成本（这是一笔巨额支出）。
Still, it remained unclear how much the firm’s sales had grown since July, when its ARR was $65bn. New numbers are good for little when investors do not know what to compare them with. Wacky financials deserve wacky valuations. Coatue, an investment firm, reckons that the labs should be worth some multiple of their pre-training EBITDA (that is, their earnings before interest, tax, depreciation and amortisation—and before the cost of training their models, an enormous expense).
这可能会让投资者联想到石油行业（其中勘探前利润 EBITDAX 很常见）；或私募股权（通过虚高 EBITDA 来借贷更多资金的做法已被提升为一种艺术形式）；甚至联想到共享办公公司 WeWork——其创意财务报告（“社区调整后 EBITDA”）使其名列糟糕会计实践的殿堂之列。也许他们还能更进一步，玩得更危险。比如价格与单词价格比率？
This might remind investors of the oil industry (where EBITDAX, earnings before exploration, is common); or private equity (where inflating EBITDA in order to borrow more has been raised to something of an art form); or even WeWork, the office-leasing company whose creative financial reporting (“community-adjusted EBITDA”) earned it a deserved place in the pantheon of bad accounting. Perhaps they could live even more dangerously. How about price-to-token-price ratio?
或者存在性调整后的每股收益？公开市场确实提供了更多清晰度。但也只是多了一点。去年会计界的热门问题是：大型科技公司是否计算其计算资产折旧太慢，从而导致利润被虚高。这个问题并未真正得到解决，而是被两个更大的问题所取代。这些巨头到底有多高杠杆？亚马逊、谷歌、Meta 和微软合计的 2.4 万亿美元表外承诺额，超过了它们从债券市场借入的金额。
Or existentially-adjusted earnings per share? Public markets offer more clarity. But not much more. Last year the accounting question du jour was whether big tech firms were writing down the value of their computing assets too slowly, thus overstating their earnings. This issue was not so much resolved as replaced by two bigger ones. How leveraged are the giants? The $2.4trn of off-balance-sheet commitments made by Amazon, Google, Meta and Microsoft exceeds what they have borrowed from bond markets.
如何衡量他们在这笔资本上的回报？4月，微软表示其AI业务的“年化收入运行率”为370亿美元。7月，亚马逊将其ARRR设为250亿美元。尽管多了一个“r”，这些数字同样是黑箱。然而，或许没有什么比这笔收入实际来自何处对AI贸易更为重要。据高盛银行的研究人员称，如果云巨头希望在今年及明年对数据中心的投资获得10%至20%的回报，而AI开发者希望获得30%的利润率，则需要每年1万亿美元的收入。
And how to measure their return on this capital? In April Microsoft said its AI business had an “annual revenue run rate” of $37bn. In July Amazon put its ARRR at $25bn. Despite the additional “r” these figures, too, are black boxes. Yet perhaps nothing matters quite as much to the AI trade than where this revenue is actually coming from. According to researchers at Goldman Sachs, a bank, if the cloud giants are to earn a return between 10% and 20% on what they invest in data centres this year and next, and AI developers are to earn a 30% margin, some $1trn of annual revenue is required.
这意味着企业从该技术中提取的生产力收益价值应远高于此。这就又带来了一个测量问题。询问老板他是如何衡量自己在这项技术上的支出回报的，准备好迎接一脸茫然的表情吧。ARRRrrrrr-gh！甚至连衡量美国企业活力的最基本指标也变得难以理解。科技巨头在数据中心上的支出立即提升了半导体和工业公司的利润，但通过自身财务报表反映得远比这慢，因为折旧会在短期内拉高总体盈利。
That means the value of productivity gains companies extract from the technology should far exceed that. Therein lies yet another measurement problem. Ask a boss how he measures his returns from spending on the technology and prepare to be met with a blank stare.ARRrrrrr-gh! Even the most basic measures of corporate America’s vitality have been rendered unintelligible. The spending of tech giants on data centres immediately lifts the profits of semiconductor and industrial firms, but flows through their own financial statements far more slowly as depreciation, inflating aggregate earnings in the short term.
公开公司对私营实验室的持股一次性调整进一步放大了这一效应。第二季度，标普500指数的未调整盈利增长达到惊人的50%。剔除Alphabet和亚马逊对私营投资的上调后，这一数字仍为令人瞩目的32%。衡量AI对经济的影响同样充满挑战。内存芯片价格飙升导致某些软件产品的通胀被错误地高估。Epoch AI（一家研究公司）的一份广受讨论的报告指出，由于英伟达跨境销售的计量方式，美国过去一年的GDP增长被低估了整整0.3个百分点。
One-off adjustments for public firms’ stakes in private labs fatten them further. During the second quarter the unadjusted earnings growth of the S&P 500 index was an astonishing 50%. Exclude the write-ups of private investments by Alphabet and Amazon and it falls to a still-remarkable 32%.Measuring the impact of AI on the economy has become similarly fraught. Soaring prices for memory chips mean that inflation has been incorrectly overstated for some software products. One widely discussed report by Epoch AI, a research firm, suggests that GDP growth in America has been undercounted by a full 0.3 percentage points during the past year because of the way Nvidia’s cross-border sales are measured.
与此同时，美国的数据收集机构因资金不足而陷入困境，其能力也在下降。放松监管的另一个牺牲品是自2000年代安然丑闻以来一直在检查公司的会计监管机构，其预算已被大幅削减。该机构最近的一项审查对四大会计师事务所给出了最高评分——这可能是最后一组看起来太好了以至于可能不真实的数据。
At the same time, America’s data-collection bodies have been starved of funding and are declining in competence. Another casualty of deregulation is the accounting watchdog that has inspected firms since the Enron scandal in the 2000s, whose budget has been slashed. The big four audit firms have been given top marks in its recent review—one last set of numbers that might prove too good to be true.