Chicago, IL – March 30, 2026 – Today, Zacks Investment Ideas feature highlights Alphabet’s GOOGL, Sandisk Corp. SNDK and Micron Technology MU.
Memory stocks got hammered this week after Google dropped a research paper that has investors questioning the entire thesis for the AI-driven memory bull run. Alphabet’s Google Research group published details on Tuesday of a new compression algorithm called TurboQuant, and the fallout was swift.
Sandisk Corp.plunged as much as 11%, Micron Technology dropped 7% on Thursday as the selling accelerated, and Western Digital and Seagate each fell over 7%. The damage extended overseas, with Samsung and SK Hynix both sliding more than 5% in Seoul. As of this morning, the group is rebounding.
Both Micron and SanDisk currently carry a Zacks Rank #1 (Strong Buy). The supply constrained dynamic and exceptional demand for DRAM has pushed these stocks to incredible performances in the last six months, with SNDK up nearly 5x and MU more than doubling over that period.
So is Alphabet building a technology that destroys the bull case for this niche AI infrastructure boom? Or is this a case of headline-driven profit-taking in an overextended sector?
At its core, TurboQuant addresses one of the most expensive bottlenecks in running large language models: the key-value (KV) cache. This is the high-speed data store that retains context so a model doesn’t have to recompute everything with each new token it generates. As models process longer inputs, the KV cache balloons, consuming GPU memory that could otherwise serve more users or run larger models.
Google’s algorithm compresses the KV cache significantly, reducing its memory footprint by nearly 6x without sacrificing accuracy or requiring model retraining. Tested across five standard AI models, TurboQuant achieved perfect scores on retrieval tasks. Testing showed that it delivered up to an 8x acceleration in computing attention on Nvidia H100 GPUs.
Beyond LLMs, Alphabet noted that TurboQuant also improves vector search, the technology underpinning everything from Google Search to YouTube recommendations to ad targeting.
The sell-off in Micron, Sandisk, and their memory peers echoed the DeepSeek-driven panic from early 2025, when a Chinese AI lab demonstrated that competitive models could be trained with far less compute than assumed. That episode triggered a one-day massacre in semiconductor names before the market ultimately concluded that efficiency gains accelerate adoption, and adoption drives more hardware demand, not less.
The same logic may apply here. TurboQuant compresses data during inference, not training. That’s an important distinction. AI model training, which is where the most memory consumption occurs, is essentially unaffected by this algorithm. High Bandwidth Memory (HBM), the product category driving the strongest revenue growth at Micron and its peers, remains essential for training workloads.
There’s also the Jevons paradox to consider: by making AI inference cheaper and more memory-efficient, TurboQuant could accelerate the deployment of LLMs into edge devices, smartphones, and IoT applications, all categories that would create entirely new memory demand that doesn’t exist today.
Micron’s earnings last week were quite exceptional. The company delivered February-quarter earnings that beat guidance by 45%, and its May-quarter earnings guidance effectively doubled consensus estimates. Revenue nearly tripled year-over-year, and net income surged almost 10x. MU currently trades at roughly 6x forward earnings, the cheapest forward P/E in the entire S&P 500 after its recent 23% post-earnings slide from record highs.
SanDisk’s fundamentals tell a similar story. SNDK reported Q2 fiscal 2026 EPS of $6.20, beating forecasts by nearly 78%, with revenue up 61% year-over-year. Gross margins expanded dramatically to 51.1% from 29.9% in the prior quarter, and management guided Q3 revenue to $4.4–$4.8 billion with gross margins of 65–67%. The company is actively signing long-term supply agreements and projecting 75–100 exabytes of AI-related storage demand by 2027.
While investor focus remains fixated on Alphabet’s research breakthroughs, the more relevant risk to these stocks is not technological obsolescence driven by research, but the inherently cyclical nature of the memory and storage industry itself. Historically, these businesses have been defined by periods of undersupply followed by aggressive capacity expansion, which ultimately leads to pricing pressure and margin compression. In that context, the eventual end of this cycle is far more likely to be driven by supply-demand dynamics normalizing than by any single leap forward in AI capabilities.
In the chart below, we can see that Micron stock has traded down to a clear level of support following the earnings, and Alphabet news driven selloff. For now, it appears to be holding at support. Investors can keep an eye on this level to determine where the next move may be.
Alphabet’s TurboQuant is a legitimate technical achievement. It will likely reduce the cost of AI inference over time, and GOOGL stands to benefit directly through lower operating costs across its cloud and search infrastructure. But the leap from “6x compression of the KV cache” to “the AI memory boom is over” may be a narrative stretched too far.
The memory cycle is driven by supply-demand dynamics that operate on multi-year capex timelines, not by individual research papers. Samsung, SK Hynix, and Micron have all constrained supply in recent quarters precisely because the industry learned its lesson from the brutal 2022–2023 downturn. Demand from hyperscale data center buildouts, where Meta alone recently committed $27 billion in a single deal with Nebius, shows no signs of abating.
For investors in Micron and SanDisk, this week’s pullback looks more like a gift than a warning. Both stocks are Zacks Rank #1 names with accelerating earnings, strengthening margins, and valuations that have moderated in the last couple of weeks.
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