AI executives says demand ‘almost unlimited’ amid stock volatility
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Chip stocks have surged in the past year as investors bet on the semiconductor industry’s central role in building the global AI infrastructure.
But the renewed surge in chip stocks has sparked a debate about whether it’s a sign of broader concern about AI demand.
In interviews with CNBC this week, many AI executives poured cold water on the idea that demand is slowing, even as they acknowledged that businesses are becoming more cautious about the cost of using AI.
“I think the demand for AI is almost unlimited,” Pat Gelsinger said. Intel The CEO and now general partner of Playground Global told CNBC on Wednesday, adding that energy availability is “the only real limiter.”
“Because how much economic value do you get for increased intelligence? It’s almost infinite in every conceivable industry,” Gelsinger added.
Data center chip player report supply constraints
A number of factors have increased volatility in markets around chip and AI data center-related stocks. An announcement from Meta that this will be done Sales were partially driven by selling excess AI computing capacity. As Meta’s shares soared on the news, questions were raised about whether this was a sign of a broader computing overcapacity. Elon Musk’s xAI also leased its excess capacity this year.
And this week, Samsung, one of the world’s largest memory chip companies, predicted a huge increase in profits, but its shares fell. After its shares rose more than 360% in the last 12 months, the market questioned how much further it could go.
None of these moves appear to reduce the demand for computing and the infrastructure behind it.
Chief Revenue Officer Marc Boroditsky said, “The situation we are experiencing in terms of demand is extraordinary. There is much more demand than we can meet, and that has been our experience for some time.” nebiushe told CNBC on Thursday. Nebius is building data centers NvidiaGPUs.
Andrew Feldman, CEO Cerebras Systemssaid the example Meta and xAI selling its excess capacity is a “unique” situation.
“Computing demand for the industry as a whole far exceeds available capacity, and we’re short on data centers. I think we’re short on a lot of the inputs for computing as an industry,” Feldman told CNBC on Wednesday.
Cerebras, which went public earlier this year, is one of a number of semiconductor startups trying to become big players in the data center market and challenge Nvidia.
Rebellions, another chip startup from South Korea backed by Samsung and SK Hynix, reported seeing similar demand.
“AI infrastructure acceleration [is] “It’s still huge,” Rebellions CEO Sungyun Park told CNBC on Wednesday.
“Personally, I believe this is not a signal that all hyperscalers are saying. [are overinvesting] in infrastructure,” Park added, citing Meta and xAI news.
LumentumThe company, which sells photonic and optical products for data center connectivity, said its products are out of stock for the next five years.
“At this point we’re trying to expand our capacity as much as possible to meet a demand that we’re going to see five years from now,” Lumentum CEO Michael Hurlston told CNBC on Wednesday.
Lumentum’s shares have risen nearly 600% in the past 12 months as investors flock to companies that address key bottlenecks in building AI data centers.
Corporate spending to ‘rationalise’
Another big debate in the AI business is how much businesses are willing to pay for the technology.
However, companies are now focusing more on the return on investment from AI; especially since these leading models remain expensive compared to open source offerings from companies like DeepSeek or others. Alibaba’s.
Nebius’ Boroditsky said tokenmaxxing is only valuable if an organization sees a return on investment as a result.
“The CFO reducing pressure and slowing down spending should actually be seeking to increase value or value maximization,” Boroditsky said, adding that artificial intelligence must be applied to create value that justifies spending.
“We are now seeing a shift towards more rationalization. We have seen this in every technology cycle, and this rationalization will definitely sustain demand,” said Nebius’ Boroditsky.
While edge AI models are considered the most advanced, there are many open source models that are close in performance and some less advanced. Different models have different capabilities that can be used for specific tasks.
In the future, certain models will be used in certain situations, Cerebras’ Feldman said. For example, while frontier models can be used for more advanced problems, some workloads will shift to others.
“I think it’s likely that you don’t need a giant bus to go to the grocery store,” Feldman said.
“As certain workloads migrate to some types of compute, easier workloads migrate to others, and I think the same thing will happen as we learn and become more sophisticated in our AI deployment.”
