The hidden threat in Anthropic’s prospectus: diseconomies of scale

UPDATED 11:26 EDT / OCTOBER 30 2026 GUEST COLUMN by Jason Bloomberg As potentially the largest IPO in history, Anthropic PBC is breaking new ground. Its prospectus lists 88 risk fa…

UPDATED 11:26 EDT / OCTOBER 30 2026

GUEST COLUMN by Jason Bloomberg

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.

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.

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.

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.

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 unpreced