CNBC's Jim Cramer said the AI boom has fundamentally changed the memory chip industry, arguing it's not too late to buy stocks that have already soared hundreds of percent this year.

Sandisk has surged 653% in 2026, while Seagate jumped 261%, Micron gained 254%, and Western Digital climbed 211%. Those gains would normally signal caution in the historically cyclical sector.

But Cramer believes this cycle is different. Memory shortages have become so acute that Elon Musk has publicly complained about supply constraints limiting data center growth.

"Memory's in such short supply that Elon Musk has taken to X to talk about how it's become the key bottleneck to data center growth," Cramer said on Mad Money.

Supply discipline breaks the cycle

Unlike previous booms when manufacturers aggressively expanded capacity, companies are now securing long-term customer agreements that lock in attractive margins for years.

"They are basically building only to suit," Cramer explained. "They're taking that money and sending it to you, the shareholder, rather than investing in new capacity."

Share buyback programs underscore this disciplined approach. Sandisk has $15.5 billion remaining under its repurchase authorization, while Seagate is working through a $5 billion buyback announced last year. Western Digital authorized an additional $4 billion in repurchases earlier this year.

Cramer's Charitable Trust recently initiated a position in Micron, which he favors for growth potential. He believes the stock could double again before the AI boom ends, assuming no data center slowdown.

"I think Micron can double again before the boom comes to an end, assuming there's no data center slowdown," Cramer said.

The Mad Money host acknowledged that data center buildout may eventually slow and manufacturers could add excess capacity again. But he can't see an overbuild happening soon.

"Sometimes the opportunity is too great and you can't afford not to take it," Cramer concluded, arguing against assuming these companies will repeat historical cyclical patterns simply because they have before.