Anthropic前景中的隐性威胁：规模不经济
Anthropic PBC作为历史上可能最大的IPO，正在开拓新的领域。 它列出了仅涉及93页的88个风险因素。 从对人类构成生存威胁的人工智能到AI可能敲诈人，公司列举了一连串风险，这些风险应当使任何潜在的Anthropic投资者警惕。
然而，在计划书中没有明确出现一个潜在的连锁公司风险因素：规模不经济。
当公司生产每一产出单位的平均成本随着公司的增长而增加时，就会出现规模上的不经济。
你们会认为，鉴于Anthropic为确保其危险因素清单而投入的时间和精力是完整的，它们本会包括这一个因素。 然而，尽管规模不经济是房间里的大象，他们还是错过了这个机会是有原因的。
我们以前一直用云计算来计算。 超规模云的基本经济驱动力是规模经济：其规模越大，每个产出单位的成本就越低，导致随着时间的推移的大规模增长和盈利。
由于AI的繁荣需要像云计算一样大规模地建立数据中心，因此可以合理地假设AI会经历与云计算一样的大规模增长所带来的经济效益。
然而，这种假设可能是错误的。 为了了解原因，我们必须看看软件和云商业的规模，以及AI为何如此不同。
缩放软件的经济学
在整个企业计算历史中，软件得益于其可忽略的边际成本。
有了实物，从汽车到鸡肉，每个单位都有原料和生产成本。 你做的部件越多成本就越多
软件根本不同。 一旦一个供应商花钱建造一个应用程序，它就可以将该应用程序出售给它能找到的尽可能多的客户，在那里，与开发该应用程序的原始成本相比，发送该应用程序额外副本的成本是可忽略不计的。
这种可忽略不计的边际成本使软件供应商能够因其业务规模而获得巨大利润。
缩放云的经济学
相比之下，计算机硬件只不过是部件，你部署得越多，材料和生产成本就越高。
云计算在其提供品中兼有软件和硬件。 该软件可能有可忽略的边际成本，但每个云提供商必须为每个物理服务器抛出钱。
为应对这一成本挑战，云提供商利用了规模经济。
超标者通过部署大量商品服务器来建造云。 随着每个公司业务的扩大，每个产出单位的成本，例如提供服务器实例或云存储单位的成本都减少了。
因此，服务器的成本随着规模的扩大而下降，而云计算公司也随着增长而变得日益有利可图，尽管其业务模式依赖于硬件和软件。
AI的经济学与软件和云有什么不同
回到Anthropic和其他前沿大语言模型提供商：Ostensibly,AI只是一类软件，而Anthropic正在其软件运行于基本上是云层的超尺度数据中心。 这一基本假设隐含在公司的前景书中。
然而，这种假设是有缺陷的。 前沿模式供应商需要图形处理装置和其他价格昂贵的硬件，而他们的专门处理器要求随着软件的快速创新而继续加快。
这一先进的专门处理器要求并没有影响云层的上升。 每个云提供商最终会随着增长而增加各种专门的硬件实例类型
Anthropic并没有奢侈地部署商品硬件来实现规模经济。 相反，它必须建造昂贵的服务器，这些服务器只有在LLMs的处理器要求不断得到推进时才会变得更昂贵。
让人类加入经济争论
与"出云"的成长形成对比的AI繁荣的人类侧面也有缩小的不经济。
超规模者当然需要一支经验丰富的劳动力队伍，具有很深的专门知识来部署他们的云并保持它们的运行。 但是，随着他们的成长，他们能够更有效地利用这些人员。 管理上千个实体服务器的管理员在基础云平台成熟后，可以轻松管理上万个或上百万个。
然而，与云供应商不同，Anthropic和其他前沿模式供应商必须不断扩大其团队的专门知识。 新的LLMs不仅仅是老版本的缩放。 每一个都要求人类专家提供新的专门知识。
Anthropic正与竞争对手进行斗争，以快速创新，并在前所未有的时间范围内推出日益强大的模型。 因此，前沿的LLM供应商团队遭遇了超规模商从来不需要面对的规模不经济。
软件，硬件和人，哦，我的！
Anthropic和其余边疆的LLM小贩遭遇了全新的经济现实，因为他们必须不断提高硬件能力，同时提高员工技能，以便有竞争力地创新他们的软件。
他们不能简单地在商品硬件上运行越来越强大的模型 — — 甚至是他们运行的前一代模型上的同样的硬件。 它们必须以跟上软件革新的速度增加对硬件的投资。
最终的结果是技术工业从未见过的规模上的不经济。 我们习惯于软件的微不足道的边际成本和云层的巨大经济规模，以至于我们对LLM这样的边境小贩必须谈判的规模不经济，完全没有准备。
英特丽克丝拿
尽管前沿模型市场仍相当新颖，我们已经看到其固有的规模经济不景气的影响
LLMs越来越强大，所需硬件也成为日益昂贵的瓶颈
Anthropic和其他LLM模式的销售商可以尝试通过提高它们更强大的LLMs的效率来应对，或者推出的功率更低，从而降低出价的模型 — 假设它们的客户买入这些动作。
我们还可能最终达到一个高地，即模型足够强大，硬件也足够能发挥作用，从而使更熟悉的规模经济能够接管。 然而，还没有看到这种高地。
因此，潜在的Anthropic投资者必须问，公司在LLM市场的领导地位是强是弱？在其供品太贵之前，它能创新和成长多久？然后它会做什么？
杰森·布隆伯格是Intellyx的创始人并担任总经理，该公司就企业领导人和技术供应商的数字转型策略提供建议。 他为SiliconANGLE写了这篇文章。 Intellyx的所有内容都是100%的人类产生，包括这篇文章。 Anthropic不是Intellyx客户。
图片：克赖永
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The hidden threat in Anthropic’s prospectus: diseconomies of scale
As potentially the largest IPO in history, Anthropic PBC is breaking new ground. Its prospectus lists 88 risk factors covering 93 pages alone. From artificial intelligence posing an existential threat to humanity to the possibility AI will blackmail people, the company lists a litany of risks that should make any potential Anthropic investor wary.
One potentially company-sinking risk factor, however, does not explicitly appear in the prospectus: diseconomies of scale.
Diseconomies of scale occur when a company’s average cost of producing each unit of output increases as the company grows.
You would think that given the time and effort that Anthropic devoted to ensuring its list of risk factors was complete that they would have included this one. There is a reason, however, that they missed it, despite the fact that diseconomies of scale is the elephant in the room.
We’ve been down this road before with cloud computing. The fundamental economic driver of the hyperscaler clouds was economies of scale: the larger they became, the lower their cost of each unit of output, leading to massive growth and profitability over time.
Since the AI boom requires massive data center buildouts just as cloud computing does, it might be reasonable to assume that AI will experience the same economic benefits of massive growth that cloud computing enjoyed.
This assumption, however, is likely to be false. To understand why, we must take a look at how the software and cloud businesses scale – and why AI is so different.
The economics of scaling software
Throughout the history of enterprise computing, software has benefited from its negligible marginal cost.
With physical goods, from automobiles to chicken nuggets, every unit has a raw materials and production cost. The more widgets you make, the more those costs add up.
Software is fundamentally different. Once a vendor has spent the money to build an application, it can sell that application to as many customers as it can find, where the cost of delivering additional copies of that application is negligible compared to the original cost of developing it.
This negligible marginal cost enables software vendors to become immensely profitable as their business scales.
The economics of scaling the cloud
Computer hardware, in contrast, is nothing more than more widgets – the more you deploy, the greater your materials and production costs.
Cloud computing combines both software and hardware in its offerings. The software may have negligible marginal cost, but each cloud provider must shell out money for each physical server.
To address this cost challenge, cloud providers take advantage of economies of scale.
The hyperscalers built their clouds by deploying massive numbers of commodity servers. As each firm expanded its business, the cost of each unit of output – say, the cost of providing a server instance or a unit of cloud storage – decreased.
As a result, the cost of servers went down as they scaled, and the cloud computing firms became increasingly profitable as they grew, despite the fact that their business models depended on hardware as well as software.
How the economics of AI differs from both software and the cloud
Back to Anthropic and the rest of the frontier large language model providers: Ostensibly, AI is just a type of software, and Anthropic is running its software in hyperscaled data centers that are essentially clouds. This underlying assumption is implicit in the company’s prospectus.
This assumption, however, is flawed. Frontier model vendors require graphics processing units and other premium, expensive hardware from day one – and their specialized processor requirements continue to accelerate as they rapidly innovate their software.
This advancing specialized processor requirement didn’t impact the rise of the cloud. True, each cloud provider eventually added various specialized hardware instance types as they grew, but those instances didn’t change the fundamental economics of the cloud business.
Anthropic, in contrast, doesn’t have the luxury of deploying commodity hardware to achieve economies of scale. Instead, it must build out expensive servers that only get more expensive as LLMs’ processor requirements continue to advance.
Adding humans to the economic argument
There are also diseconomies of scaling the human side of the AI boom that contrast with the growth of the cloud.
The hyperscalers certainly require an experienced workforce with deep expertise to deploy their clouds and keep them running. But as they grew, they were able to leverage such personnel more efficiently. An admin who managed a thousand physical servers could easily manage 10,000 or a million as the underlying cloud platforms matured.
Unlike the cloud providers, however, Anthropic and the other frontier model vendors must continually scale up the expertise of their teams. New LLMs aren’t simply scaled-out versions of older ones. Each one requires new expertise from expert humans.
Anthropic is battling its competitors to innovate quickly and roll out increasingly powerful models on unprecedented timeframes. As a result, frontier LLM vendor teams experience diseconomies of scale that the hyperscalers never had to face.
Software and hardware and people, oh my!
Anthropic and the rest of the frontier LLM vendors suffer from a fundamentally novel economic reality, because they must continue to increase their hardware capabilities while upskilling their workforce so they can competitively innovate their software.
They can’t simply run increasingly powerful models on commodity hardware – or even the same hardware they ran the previous generation model on. They must increase their investments in hardware at a pace that keeps up with the pace of software innovation.
The end result is a diseconomy of scale the technology industry has never seen before. We are so used to the negligible marginal cost of software and the massive economies of scale of the cloud, that we are simply unprepared for the diseconomies of scale that frontier LLM vendors like Anthropic must negotiate.
The Intellyx take
Even though the frontier model marketplace is still quite new, we’re already seeing the impact of its inherent diseconomies of scale as token costs become an increasingly significant expense for consumers of AI.
As LLMs become more powerful and the hardware they require becomes an increasingly expensive bottleneck, it’s just a matter of time until token costs become prohibitively high for a range of AI-based applications.
Anthropic and the other LLM model vendors can try to respond by increasing the efficiency of their more powerful LLMs or rolling out less powerful, and thus less expensive models – assuming their customers buy into these moves.
We may also eventually reach a plateau where the models are sufficiently powerful and the hardware is sufficiently performant, thus enabling more familiar economies of scale to take over. However, no such plateau is in sight.
The question potential Anthropic investors must ask, therefore, is whether the company’s leadership position in the LLM market is a strength or a weakness? Just how far can it innovate and grow before its offerings are too expensive? And what does it do then?
Jason Bloomberg is founder and managing director of Intellyx, which advises business leaders and technology vendors on their digital transformation strategies. He wrote this article for SiliconANGLE. All of Intellyx’s content is 100% human generated, including this article. Anthropic is not an Intellyx customer.
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