A wave of caution from leading AI developers has rattled global tech shares, but Wall Street heavyweight Goldman Sachs is pushing back with a bullish assessment of the memory chip sector. The investment bank has reiterated its "Buy" ratings on the world's two largest memory chipmakers, with price targets suggesting substantial upside potential of nearly 90%.

Global AI leaders like Anthropic and OpenAI have collectively called for a slowdown in frontier AI model development, prompting investors to reassess growth expectations for the AI computing supply chain. This sentiment, coupled with rising oil prices and renewed Federal Reserve rate hike risks, pressured AI-themed stocks across global markets. In South Korea, Samsung Electronics Co., Ltd. shares fell more than 3% in early trading, while rival SK Hynix dropped over 5%, dragging the KOSPI index down more than 3%.

However, many veteran Wall Street analysts argue these developments won't have a lasting impact on the industry or derail the long-term bull narrative for AI computing trades. In its latest Investor Feedback Report on the Korean tech sector, Goldman Sachs revealed that North American investors hold a notably more positive view on memory chip stocks compared to their Asian counterparts. The firm reaffirmed its "Buy" ratings on both Samsung Electronics Co., Ltd. and SK Hynix, with Samsung remaining on Goldman's "Conviction List".

Goldman's analysts set a target price of 490,000 Korean won for Samsung's common shares and 360,000 won for its preferred shares, alongside a 350,000 won target for SK Hynix. Based on September 11 closing prices, these targets imply potential gains of approximately 88.8%, 86.2%, and 93.2% respectively. Goldman continues to project roughly 100% year-on-year growth in blended average selling prices for HBM in 2027, with Samsung poised to benefit from an improved product and customer mix, while both companies offer potential catalysts for shareholder returns.

Some analysts suggest that calls to slow frontier model development don't necessarily translate into reduced AI capital expenditure or memory orders, as inference and application demand for existing models may continue to grow. "It could create some near-term pressure, but it's unlikely to break the long-term AI trade," said Gary Tan, Portfolio Manager at Allspring Global Investments in Singapore. "AI development is still in its relatively early stages, and I'm not sure other players in the AI ecosystem are willing to accept their current industry ranking and slow down while technology is still evolving so rapidly."

Billy Leung, Investment Strategist at Global X Management in Sydney, echoed this sentiment: "Three CEOs agreeing to moderate their pace doesn't really change the money flowing into chips, power, and infrastructure. In fact, it extends the development timeline. If commercialization and adoption continue to grow while new capabilities roll out slightly slower, this could actually help the industry shift from spending on construction to accelerating profits from what's already built, which is AI monetization."

Where the Opportunity Lies

For investors, the key indicators for memory chip demand and volume-price growth will be actual bit shipments, customer qualifications, contract pricing, profit margins, free cash flow data, and phased guidance outlooks. The optimistic price targets from Goldman Sachs and other Wall Street institutions ultimately depend on these metrics delivering results.

Goldman's discussions with investors in Toronto, Boston, New York, and San Francisco revealed that North American investors are generally more positive on memory than their Asian peers, though this optimism hasn't fully translated into increased positions due to a lack of major near-term catalysts and positioning concerns. The firm noted a divergence between short-term and long-term expectations: most respondents expect DRAM and NAND average selling prices to rise about 20% quarter-on-quarter in Q3 2026, but price increase and earnings expectations have been revised lower, with a stronger Korean won also potentially compressing won-denominated earnings for both companies.

Regarding 2027 HBM pricing, conservative investors anticipate roughly 50% year-on-year growth, while more optimistic ones believe increases need to exceed 100% to bring profit margins close to traditional DRAM levels. Some previous expectations had reached as high as 200%. Goldman itself still projects approximately 100% year-on-year growth in blended HBM average selling prices for 2027, believing Samsung's product and customer mix improvements could provide greater upside.

These latest signals suggest that North American investors focused on the memory sector are tempering expectations for extreme price hikes while maintaining their assessment of supply-demand tightness and earnings resilience. Whether Long-Term Agreements (LTAs) can stabilize memory profits represents a core point of divergence revealed in the report. Optimists favor rolling contracts, broader coverage, and arrangements like deposits and prepayments that improve order visibility; skeptics want to see whether these agreements can truly bind both buyers and sellers through price downcycles.

On customers reducing memory configurations and optimizing storage usage, most surveyed investors believe the primary cause is supply constraints rather than a sudden weakening in end demand. However, the report acknowledges that reducing memory capacity per consumer electronics device could partially offset growing shipments of data center HBM or high-performance enterprise SSDs in the near term.

Large-scale supply expansion from Chinese memory chip manufacturers has also been factored into many investors' models, including scenarios where Chinese DRAM suppliers exceed 10% market share by 2028. Consequently, new supply is no longer universally viewed as an unexpected shock. Respondents still believe technology gaps and the lack of EUV lithography equipment limit China's ability to advance data center server DRAM iterations and actual capacity catch-up speed.

Different Investment Appeals

Goldman emphasized that the two companies offer distinct investment attractions. Samsung's higher exposure to traditional memory businesses, HBM4 progress, and synergies between memory and foundry operations provide room for fundamental improvement. Investors also discussed when the foundry business might break even, new contributions from HBM base dies, and capital expenditure, though opinions remain divided on what valuation the foundry business deserves.

SK Hynix, meanwhile, garners more investor attention for shareholder returns, higher stock price elasticity, its continued dominant HBM market share leadership over Samsung and Micron, its largest HBM orders from Nvidia, and the opportunity to close the discount between its local Korean shares and its US ADRs. Respondents generally prefer share buybacks over cash dividends.

North American investors have shifted their valuation discussions for Samsung and SK Hynix more toward price-to-earnings (P/E) ratios, but fewer investors now expect double-digit P/E multiples compared to the first half of the year, indicating the market still applies a discount for cyclical persistence. Goldman applies a Sum-of-the-Parts (SOTP) valuation for Samsung, with a preferred share target price approximately 27% below common shares. For SK Hynix, the target price is based on just 9 times average earnings for 2026-2027, representing a significant valuation discount compared to Micron.

Memory Demand Sustained by AI Evolution

Market performance before the sharp US PPI-driven selloff last Thursday already reflected a comprehensive recovery in global capital sentiment toward the memory investment theme. The KOSPI index rebounded approximately 22% from its July 30 closing low by August 13, entering what's commonly called a technical bull market, before largely trading sideways until September 7 when it surged 4.61%, with Samsung Electronics Co., Ltd. and SK Hynix jumping 5.68% and 8.26% respectively. The KOSPI has gained 60% year-to-date.

Strong AI computing demand has been clearly reflected in the robust earnings and long-term capacity agreements of industry leaders. Nvidia reported Q2 FY2027 revenue of $96.2 billion, up 106% year-on-year, with data center revenue reaching $89 billion, up 117%. Recent media reports indicate Anthropic has secured a $45 billion compute rental agreement with Nscale and a $35 billion cloud computing deal with Lambda, involving approximately 460 MW and 350 MW of capacity respectively. These multi-year commitments substantially strengthen the visibility of AI computing resource demand across both AI chips and memory chips.

From an engineering perspective, increased inference demand simultaneously strengthens the importance of memory bandwidth, working capacity, and persistent capacity, though the benefit paths differ. High Bandwidth Memory (HBM, itself a type of DRAM) sits adjacent to accelerators, carrying model weights and active KV Cache, with many decoding scenarios dependent on timely data delivery to compute units. DDR5, LPDDR, and other server memory handle CPU workloads, data processing, and parts of cache hierarchy. NAND-based enterprise SSDs store model files, knowledge bases, and task results while accommodating historical KV Cache suitable for offloading and reuse.

Given fixed model architecture and cache precision, longer contexts and more concurrent sessions expand cache requirements, and continuously running agents increase state storage and data retrieval needs. Therefore, Samsung, SK Hynix, and Micron share the opportunity across the entire memory hierarchy expansion. While SSDs can alleviate capacity pressure, their latency and bandwidth limitations prevent them from universally replacing HBM. Micron's official technical articles explicitly explain this tiering trend through HBM, main memory, expanded memory, context SSDs, and network data lakes.

Meanwhile, high-performance AI inference led by Astra and the widespread adoption of agentic AI workflows focused on autonomous work are continuously and explosively driving up demand for HBM/high-performance DRAM capacity at the AI compute level, as well as data center NAND storage components.

Bernstein, another Wall Street financial giant, recently released a research report indicating that Astra and AI training operator research automation are providing new semiconductor demand sources for what it terms an unprecedented memory chip boom. The firm maintains "Outperform" ratings on the year's standout memory leaders, including Samsung Electronics Co., Ltd., SK Hynix, Micron, and SanDisk, with target prices of 440,000 Korean won, 3.3 million Korean won, $1,300, and $3,000 respectively, reflecting Wall Street's renewed positive outlook on the memory chip cycle.

Bernstein noted that the semiconductor industry is experiencing the expected seasonal downturn, but AI infrastructure-related semiconductor demand remains exceptionally strong, particularly for next-generation HBM systems and data center server-grade DRAM/NAND pricing and volume. July, traditionally a slow month for semiconductor sales, still saw 131.4% year-on-year growth, with global memory chip sales surging an extraordinary 451.7% year-on-year. Excluding memory, global semiconductor industry sales grew approximately 35% year-on-year.

OpenAI's recently launched GPT-6 Astra model and the industry's focus on RSI technical pathways are expected to become twin core drivers of exponential AI computing demand growth. More capable AI models, broader AI application adoption, and next-generation AI training paths requiring more compute are strengthening the evidence base for sustained AI infrastructure growth.

The investment significance of Astra lies in improving success rates and economic viability for complex tasks, encouraging enterprises to deploy more agents and handle more specialized work. Morgan Stanley's recent emphasis on "shifting from demand debates back to physical supply constraints in the AI theme" precisely captures the new round of AI computing resource demand expansion driven by the frontier Astra model. OpenAI's product chief noting that demand is so unprecedented the company might suspend new Pro subscriptions serves as a significant signal of current AI compute service capacity strain.