The strain on the AI ​​supply chain means Dell wants buyers to plan sooner

Demand for artificial intelligence infrastructure is outstripping available supply, and the imbalance is changing the way companies buy technology. The old tactic of waiting for a quarter-end discount now risks leaving a project stuck in the hardware queue.

At the beginning of the year, many buyers didn’t see the shortage coming. “I think it’s a time of turmoil,” Schmoock said. “I think people are waking up and saying we need to plan for longer-term horizons.”

Schmoock spoke with theCUBE Research’s Dave Vellante at the Dell Technologies New York Symposium, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how supply shortages, workflow redesign, and token economics are changing the way Dell and its customers plan for AI. (*Disclosure below.)

Plan earlier in a constrained AI supply chain

Demand began accelerating last November, and the surge in graphics processing unit servers for AI pulled standard x86 servers along with it, Schmoock explained. Dell Chief Operating Officer Jeff Clarke told investors in September that shortages had spread beyond memory, storage and central processing units to rack-level cooling, networking and power components. In this environment, Dell is asking customers to communicate deployment plans much earlier than in previous hardware cycles.

“But the key is how can we plan with our customers earlier? If you come to us late in our cycle, we will respond to that [a request for proposal] “Just like everyone else,” he said. “But you wouldn’t have that joint planning that could delay your rollout.”

The conversation with buyers has also changed. Price once dominated negotiations, but the market is entering a period of cost inflation, and customers for whom technology is the product, such as high-frequency traders, have been among the first to secure delivery schedules, according to Schmoock.

Beyond securing hardware, companies also need to rethink how they use AI to get the most value from their investments. Dell is sharing what it has learned from its own AI rollout, and the biggest lesson is that order matters: Redesign workflows first, then fix the data layer.

“If you just apply AI to existing processes, you will see a slight increase in productivity,” Schmoock said. “You won’t get that [tenfold gains] that we all want to drive.”

This approach has helped Dell decouple revenue growth from operating costs, which have traditionally increased in tandem, he noted. The next challenge is managing a cost base that connects people to AI tokens. He compared the task to a National Football League salary cap: A team can afford only one star quarterback, so it must fill the rest of the roster with lower-cost players who still contribute.

“Thinking about tokens is no different. You can only use boundary models for a lot of your work,” he said. “You have to use older versions, you have to use open source models, you have to use machine learning.”

Just as companies must balance the cost of different AI models, they also need to plan for the broader infrastructure that supports their workloads. AI isn’t just about buying GPU servers, he said; This also includes traditional x86 workloads that do not require tokens, as well as desktops and workstations that can run inference at the edge. Customers who arrive late in the process force Dell to fight for supplies.

Here’s the full video interview, part of SiliconANGLE and theCUBE’s coverage of the Dell Technologies New York Symposium:

Photo: SiliconANGLE