U.S. stocks fell for a third straight session Tuesday as higher long-term bond yields put fresh pressure on richly valued technology names. The Nasdaq Composite dropped 1.33%, underperforming the Dow Jones Industrial Average and the S&P 500, while the 30-year U.S. Treasury yield briefly touched 5.338%, its highest level since 2007. The move was part of a wider global bond sell-off that also pushed long-dated yields higher in France, Germany, Japan and the U.K. Markets are increasingly focused on the growing debt burden tied to artificial intelligence expansion. According to figures cited in the report, AI-related bond issuance has reached $489 billion so far this year, well above an earlier full-year 2025 estimate of roughly $322 billion, while The Wall Street Journal reported that nine major technology companies have about $3 trillion in off-balance-sheet AI commitments. That backdrop hit semiconductors, memory, optical communications and AI cloud-service providers especially hard. Investors are also weighing fiscal deficits, oil-driven inflation risks tied to the Iran situation, and a heavy event calendar that includes U.S. tariffs on some Canadian products, a 20-year Treasury auction, Federal Reserve minutes and China’s one-year LPR decision.

Japanese equities fell for a second straight session, with AI-linked names and chip-related stocks under pressure as rising bond yields unsettled investors. Early in the session, the Nikkei 225 dropped more than 3%, while shares including memory chip maker Kioxia Holdings and fiber-optic cable producer Furukawa Electric were among the weaker performers. The broader Topix also fell as much as 2.8%, dragged down by electronics and banking stocks. The move came as investors weighed the effect of higher borrowing costs on companies tied to the AI spending cycle. A chief strategist at Daiwa Asset Management said rising yields could increase funding costs for hyperscale cloud providers, raising questions about capital expenditure plans and potentially hitting infrastructure companies that have benefited from the AI investment boom. In Japan, the 10-year government bond yield climbed to its highest level since 1996 on Tuesday, while the market speculated that the Bank of Japan could raise rates next month. The 10-year U.S. Treasury yield was also hovering near its highest level since early 2025.

Odaily Star Daily, citing Gate data, said the Nikkei 225 fell 3% intraday to 65,422.31. Kioxia shares in Japan also traded lower, sliding nearly 8% during the session.

The automotive semiconductor cycle is turning in the second half of 2026. Major overseas chipmakers have reported a rebound, inventories across the supply chain have normalized, Tier 1 orders are recovering, and several product lines are already in a second round of price increases. Deutsche Bank and Bernstein say the industry has entered a structural uptrend, but this recovery is set to be uneven rather than broad-based. Infineon, NXP and other leaders have seen inventory days fall from above 200 to 120-140 days in the first quarter of 2026, while supplier inventory-to-sales ratios have returned to safer levels. Lead times for mainstream automotive power devices have stretched beyond 30 weeks, and SiC modules are taking more than 40 weeks in some cases. That tightening has already pushed prices up 10%-25% across multiple categories. The split is clearest in SiC, high-voltage IGBT, high-end MCU, and in-vehicle storage. 800V platforms are driving demand for higher-value semiconductors, while AI-related demand is absorbing mature 8-inch capacity. For automakers, the key issue is no longer shortage across the board, but which chips are tight, which are normalized, and which are still under pressure.

AI enthusiasm got another boost this week after Anthropic and OpenAI reported strong financial updates and Nvidia pledged support for data center construction. The rebound lifted U.S. storage stocks and pushed the Philadelphia Semiconductor Index back into a technical bull market. Micron, SanDisk, SK Hynix, Western Digital and Seagate all moved higher, while Kioxia ADR jumped on gains in Japan. Analysts said the latest revenue disclosures from Anthropic and OpenAI, along with Nvidia’s financing role in AI infrastructure, are the main near-term catalysts for chips and memory names.

AI spending in semiconductors has largely been framed around GPUs, high-bandwidth memory, networking and power. This report argues that inference is widening that trade. As AI systems move from training to serving billions of queries, enterprise agents, multimodal workloads and real-time applications, the need to keep large pools of data accessible at reasonable cost is becoming more central. That is giving NAND a larger role as a capacity layer alongside HBM and DRAM, rather than leaving it as a conventional commodity storage product. Sandisk has become one of the clearest public advocates of that view. At its 2026 investor day, the company said enterprise data center flash demand could reach 1.2 ZB by 2030 and projected mid-to-high double-digit annual revenue growth from FY2028 through FY2030, with Reuters cited in the source for related expectations. The company is also developing High Bandwidth Flash, or HBF, for AI inference, while continuing to push denser QLC NAND products. At the same time, Sandisk and other storage makers are leaning more heavily on multi-year customer agreements. Reuters, as cited in the source, reported that Sandisk had signed eight long-term agreements with six customers worth about $93.9 billion in total. The article’s main conclusion is narrower than a permanent re-rating story. AI is unlikely to erase NAND cyclicality. What may change is the amplitude: stronger bit demand, better demand visibility and tighter supply discipline could make future NAND cycles less violent than in the past.

Elon Musk backed a claim on X that memory, rather than raw compute, is the real rate-limiting factor in the age of AI agents, replying that 「Few realize this.」 The point is one he has made repeatedly this year. According to the source material, Musk raised the issue at Tesla’s first-quarter earnings call on April 22, echoed complaints about memory price inflation in late June, and then gave a sharper figure at SpaceX’s Q2 earnings call in early August: memory capacity is growing about 20% a year, while demand is rising 200% or more. The report ties that view to a broader shift in AI infrastructure. As agentic systems hold much larger context windows and persistent task histories, KV cache becomes a bigger memory burden. With HBM still expensive and tight in supply, companies are moving more context data down the memory stack into NAND and storage. NVIDIA’s Inference Context Memory Storage Platform, introduced at CES, is cited as one example, using Dynamo and NIXL to place KV cache on NVMe SSDs. The same report also tracks how markets are reading the memory cycle. Korean brokerages cut target prices for Samsung Electronics and SK hynix on concerns that the conventional memory upcycle may have peaked, while Sandisk later issued long-term revenue growth guidance that helped memory shares rebound across Asia.

Sandisk used its Aug. 13 investor day to lay out a long-range financial model that sits well above market expectations and puts its in-house High Bandwidth Flash, or HBF, at the center of the story. The company said its long-term sustainable model, based on average performance across FY2028 to FY2030, calls for revenue growth in the mid-to-high teens, a Non-GAAP gross margin of about 80%, a Non-GAAP operating margin of roughly 75%, adjusted free cash flow margin near 50%, and capital intensity in the mid-single digits as a percentage of revenue. Mizuho Securities kept its Outperform rating on Sandisk and set a $1,900 price target, arguing that the company’s framework, buyback capacity, and exposure to AI infrastructure support upside. Sandisk also detailed the positioning of HBF, saying the product can deliver comparable read bandwidth to HBM at about one-eighth the cost, while offering 8x to 16x more capacity in a similar package footprint. The company tied that pitch to memory-heavy AI inference workloads and said HBF is meant to work alongside HBM rather than replace it.