The analyst firm doubled its previous target, citing unprecedented memory chip demand and long-term supply agreements that stretch through 2027
Melius Research just doubled down on Micron Technology in the most literal sense possible. The firm raised its price target on the chipmaker to $2,200 from $1,100, pointing to strong fundamentals and longer-term strategic agreements as the catalysts.
To put that number in perspective, Melius first initiated coverage of Micron on April 27, 2026, with a price target of $700. Roughly three weeks later, on May 18, the firm bumped that to $1,100. Now it’s at $2,200. That’s a tripling of conviction in a matter of weeks.
Wall Street is piling in
UBS raised its own Micron price target to $1,625 from $535 back on May 26. Susquehanna went to $1,750. Other financial institutions have clustered their targets in the $1,500 to $1,750 range.
Melius analyst Ben Reitzes has characterized the AI-driven demand for high-bandwidth memory, DRAM, and NAND chips as unprecedented in the semiconductor industry.
Why memory chips are the new oil
Micron’s memory capacity is sold out through 2027. In English: every chip Micron can make for the next year-plus already has a buyer’s name on it.
Long-term supply agreements are the structural tell here. Companies building AI infrastructure aren’t placing spot orders. They’re locking in multi-year contracts, which gives Micron the kind of revenue visibility that transforms it from a cyclical play into something that looks more like a growth stock.
What this means for investors
The export control wildcard is a real risk. Micron has been actively lobbying the US Congress on export control policy, seeking to maintain what it calls “competitive capacity visibility.” The outcome of those lobbying efforts is uncertain, and a restrictive policy shift could meaningfully alter the demand picture.
The broader pattern worth watching is whether AI semiconductor spending continues to accelerate or begins to plateau. Companies like Microsoft, Google, Amazon, and Meta have committed staggering sums to AI infrastructure. If any of those hyperscalers signals a slowdown in capital expenditure, the ripple effects through the memory supply chain would be immediate.
