GPU as a Service and AI Business Models

It was virtually inevitable that the rise of artificial intelligence would create new roles for data centers and computing “as a service.” Consider firms that now offer access to graphics processing units “as a service,” including virtually all leading cloud computing as a service suppliers, as well as newer entrants in the “GPU as a service” or “cloud GPU” space:

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Amazon Web Services (AWS)

Microsoft Azure

Google Cloud Platform (GCP)

NVIDIA DGX Cloud

IBM Cloud

Oracle Cloud Infrastructure (OCI)

CoreWeave

Jarvis Labs

Lambda Labs

Paperspace CORE

Perhaps notable on that list are NVIDIA DGX Cloud and firms such as CoreWeave. The former is notable because it represents a foray by a major GPU infra supplier into the “services” space, while the latter represents a new type of data center, namely focused on GPU as a service. 

Those efforts are part of a broader creation of revenue and business models for AI in general. 

As you might guess, a key question for generative AI and AI overall is “what is the revenue model?” The answers depend on what part of the internet ecosystem a firm operates in, and whether the “customer” is a business or a consumer. 

Chipmakers and server suppliers will sell infrastructure. Connectivity suppliers will sell bandwidth. Data centers will sell compute cycles, storage, interconnection and security “as a service.”

Virtually every firm will embed AI into its existing core business processes, creating revenue models for software and platform suppliers. The point is that direct revenue models (selling chips, servers, software, bandwidth, compute cycles, storage, security, payment processing, analytics) are likely to be common in business-to-business settings. 

The cost of those tools, in turn, will be monetized indirectly in the form of higher profit margins, higher sales, lower operating costs, lower churn, higher add-on sales, greater awareness and longer customer life cycles, for example. 

Indirect models are likely to dominate in consumer markets. Subscriptions, transactions and advertising are the basic consumer revenue models. So, in most cases, AI is unlikely to be a “product” sold separately to consumers. Rather, it is going to be embedded in some other revenue-producing process. 

That is probably what ChapGPT founder Sam Altman means when he says the costs of intelligence are on a path to near-zero costs. Revenue from applying AI will be embedded in the consumer’s cost to buy things, watch things, listen to things, read things, communicate with others, use social media and find things. 

Some pay-to-play consumer models could develop, much as some firms rely on subscriptions for access to content, services and features. But consumers are price conscious, so advertising and transactions  is likely to remain a key alternative to pay-to-play and subscriptions. 

But GPU as a service is among the new direct revenue and business models developing around AI.