Chip stocks have had a blistering rally over the past year as investors bet on the semiconductor sector’s central role in the global AI infrastructure buildout.

But renewed volatility around chip stocks has sparked a debate if this is a sign of broader concern about AI demand.

In interviews with CNBC this week, several AI executives poured cold water over the idea that demand is slowing, even as they acknowledged that businesses are being more cautious on the cost of using AI.

“I somewhat think of AI demand as almost unlimited,” Pat Gelsinger, the former Intel CEO and now general partner at 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? Almost infinite across every industry imaginable,” Gelsinger added.

A number of factors have stoked volatility in markets around chip and AI data center-related stocks. An announcement from Meta that it will sell its excess AI computing capacity was in part a contributor to the sell-off. While Meta’s stock popped on the news, it raised questions over whether this was a sign that there was broader overcapacity of compute out there. Elon Musk‘s xAI also rented its excess capacity out this year.

And this week, Samsung, one of the world’s biggest memory chip companies, forecast a gigantic rise in profit, but its stock fell. After a more than 360% rally in its shares over the last 12 months, the market questioned how much further it could go.

None of these moves appears to have dampened demand for compute and the infrastructure behind it.

“What we’re experiencing in terms of demand is extraordinary. There’s much more demand than we’re able to fulfil, and that’s been our experience for some time now,” Marc Boroditsky, chief revenue officer at Nebius, told CNBC on Thursday. Nebius is building data centers using Nvidia‘s GPUs.

Andrew Feldman, CEO of Cerebras Systems, said the example of Meta and xAI selling its excess capacity is a “unique” case.

“For the industry as a whole, the demand for compute far outstrips available capacity, and we’re short on data centers. I think we’re short on, as an industry, many of the inputs to compute,” Feldman told CNBC on Wednesday.

Cerebras, which went public earlier this year, is one of a slew of semiconductor startups attempting to become major players in the data center market and challenge Nvidia.

Rebellions, another chip startup from South Korea, which is backed by Samsung and SK Hynix, reported seeing similar ample demand.

“AI infrastructure momentum [is] still huge,” Sungyun Park, CEO of Rebellions, told CNBC on Wednesday.

“I personally believe it’s not the signal saying that … all the hyperscalers [are overinvesting] in the infrastructure,” Park added in reference to the Meta and xAI news.

Lumentum, which sells photonics and optical products for connectivity in the data center, said its products are sold out for the next five years.

“We’re trying to build up our capacity as much as we possibly can to fulfil a demand that we see out five years at this point,” Michael Hurlston, CEO of Lumentum, told CNBC on Wednesday.

Lumentum’s stock is up around 600% over the last 12 months as investors pile into companies addressing key bottlenecks in the buildout of AI data centers.

There has been a period of so-called ‘tokenmaxxing’ at enterprises where companies would encourage employees to use as much AI as possible no matter the result. The tools often used were those from frontier labs like OpenAI and Anthropic.

But companies are now focusing more on the return on investment from AI, especially as those frontier models remain expensive relative to open source offerings from companies like DeepSeek or Alibaba.

Nebius’ Boroditsky said that tokenmaxxing is only worthwhile if an organization is seeing a return on investment as a result.

“The CFO bringing the hammer down and slowing spend should actually be looking for value or valuemaxxing,” Boroditsky said, adding that AI should be applied to create value that justifies the spending.

“We’re seeing a shift now to more rationalization. We’ve seen it with every tech cycle, and that rationalization will definitely continue the demand,” Nebius’ Boroditsky said.

While frontier AI models are seen as the most advanced, there are a plethora of open source models that are close in performance and some that are less advanced. Different models have different capabilities, which can be used for specific tasks.

Cerebras’ Feldman said that in the future, certain models will be used in specific situations. For example, frontier models can be used for more advanced problems, while some workloads will shift to others.

“I think it’s probably the case that you don’t need a giant bus to go to the grocery store,” Feldman said.

“Certain workloads migrate to some type of compute and easier workloads to others, and I think as we learn and become more sophisticated in our deployment of AI, the same thing will happen.”