Who is Jukan @COMPUTEX?

Jukan @COMPUTEX (@jukan05) is a FinTwit voice tracked on Surfaced in the Semis category with 175.1k followers on X. Surfaced archives their cashtag mentions and plots each one on a price chart so the calls age publicly.

What stocks does @jukan05 mention most?

Over the past 12 months, @jukan05's most-mentioned tickers are $INTC, $MU, $NVDA, $SNDK, $AAPL — $INTC leads with 80 mentions.

When did @jukan05 first mention $INTC?

@jukan05's first tracked mention of $INTC was on Dec 16, 2025.

How active is @jukan05?

In the past 12 months, @jukan05 posted 346 times carrying cashtag mentions, totalling 425 mentions across 48 distinct tickers.

Jukan @COMPUTEX (@jukan05) on Surfaced

Recent calls

19 calls

@jukan05Apr 30, 2026 · 1:43 PM

KIS, which I consider one of the top three Korean sell-side firms, published a memory report, and there’s a comment from it that I wanted to share with you all: Memory is the critical variable that determines GPU utilization. In particular, when HBM and DRAM capacity is insufficient, memory bottlenecks prevent GPUs from being fully utilized, leading to a decline in overall system efficiency. Conversely, expanding memory capacity raises GPU utilization, allowing the same GPU resources to process a greater number of tokens. This translates directly into a lower cost per token. As cost per token falls, more users are drawn in, ultimately driving a larger expansion in inference demand. This is precisely why hyperscalers, despite rising memory ASPs, are willing to go as far as proposing long-term supply agreements in order to secure greater memory allocation. The cost of purchasing memory rises, but at the same time, that purchase improves GPU utilization—lifting overall system efficiency and lowering cost per unit of performance. With DRAM prices having spiked roughly 3x year-over-year in a short span, the market is now bracing for a subsequent decline in demand or prices. But this view stems from looking at memory purely as a standalone cost line item. Even if DRAM is purchased at 3x the price, if that spending allows more GPUs to run and more tokens to be processed, system-wide profitability can actually improve. The fate of the chips that once tried to challenge NVIDIA GPUs by leaning on MLPerf benchmarks—touting per-chip performance-per-watt and price-performance comparisons—illustrates this well. As Jensen Huang has repeatedly emphasized, comparing single-chip specs in isolation is meaningless. Huang has stated that NVIDIA GPUs deliver the lowest cost per token in the world, adding that the lowest cost per token and the highest performance per watt are the decisive metrics of AI economics. In other words, his consistent argument is that competition should not be waged on GPU hardware specs alone, but rather on cost per token at the full-system level. The same logic extends to memory. If "buying memory at a premium still pays off at the system level," then memory purchases by hyperscalers locked in the AI arms race will continue. The same logic applies to NAND. Active efforts are underway to integrate NAND into AI infrastructure systems. Some argue that wider NAND adoption will eat into DRAM demand, but this too misreads the memory market as a zero-sum game with a fixed pie. As AI workloads have grown, the upper tiers of the memory hierarchy—HBM and DRAM—alone can no longer accommodate the demand, and NAND has emerged as a key element in extending that hierarchy. NAND, in particular, offers an overwhelmingly lower cost per unit of capacity than DRAM. As of Q1 2026, NAND prices stand at roughly $0.10–$0.12 per GB, whereas DRAM—even mobile DRAM, the lowest-priced application segment—has already surpassed $6 per GB on a contract basis. Even if NAND prices were to double or triple, they would still remain far below DRAM. If even a portion of AI workloads can be offloaded to NAND, it can directly contribute to lowering system-wide cost per token. $DRAM $MU $SNDK

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